9 Commits

119 changed files with 13929 additions and 23733 deletions

View File

@@ -1,4 +0,0 @@
build
jobs
relog-web/node_modules
relog-web/build

View File

@@ -14,10 +14,10 @@ jobs:
shell: julia --color=yes {0} shell: julia --color=yes {0}
run: | run: |
using Pkg using Pkg
Pkg.add(PackageSpec(name="JuliaFormatter", version="1")) Pkg.add(PackageSpec(name="JuliaFormatter", version="0.14.4"))
using JuliaFormatter using JuliaFormatter
format("src", verbose=true) format("src", verbose=true)
format("test/src", verbose=true) format("test", verbose=true)
out = String(read(Cmd(`git diff`))) out = String(read(Cmd(`git diff`)))
if isempty(out) if isempty(out)
exit(0) exit(0)

View File

@@ -21,15 +21,5 @@ jobs:
with: with:
version: ${{ matrix.version }} version: ${{ matrix.version }}
arch: ${{ matrix.arch }} arch: ${{ matrix.arch }}
- name: Run tests - uses: julia-actions/julia-buildpkg@v1
shell: julia --color=yes --project=test {0} - uses: julia-actions/julia-runtest@v1
run: |
using Pkg
Pkg.develop(path=".")
Pkg.update()
using RELOGT
try
runtests()
catch
exit(1)
end

6
.gitignore vendored
View File

@@ -12,8 +12,4 @@ Manifest.toml
data data
build build
benchmark benchmark
run.jl **/*.log
relog-web-legacy
.vscode
jobs
tmp

View File

@@ -1,28 +0,0 @@
{
"creators": [
{
"orcid": "0000-0002-5022-9802",
"affiliation": "Argonne National Laboratory",
"name": "Santos Xavier, Alinson"
},
{
"orcid": "0000-0002-3426-9425",
"affiliation": "Argonne National Laboratory",
"name": "Iloeje, Chukwunwike"
},
{
"affiliation": "Argonne National Laboratory",
"name": "Atkins, John"
},
{
"affiliation": "Argonne National Laboratory",
"name": "Sun, Kyle"
},
{
"affiliation": "Argonne National Laboratory",
"name": "Gallier, Audrey"
}
],
"title": "RELOG: Reverse Logistics Optimization",
"description": "<b>RELOG</b> is a supply chain optimization package focusing on reverse logistics and reverse manufacturing. For example, the package can be used to determine where to build recycling plants, what sizes should they have and which customers should be served by which plants. The package supports customized reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods."
}

58
CHANGELOG.md Normal file
View File

@@ -0,0 +1,58 @@
# Changelog
All notable changes to this project will be documented in this file.
- The format is based on [Keep a Changelog][changelog].
- This project adheres to [Semantic Versioning][semver].
- For versions before 1.0, we follow the [Pkg.jl convention][pkjjl]
that `0.a.b` is compatible with `0.a.c`.
[changelog]: https://keepachangelog.com/en/1.0.0/
[semver]: https://semver.org/spec/v2.0.0.html
[pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
## [Unreleased]
- Allow product disposal at collection centers
- Implement stochastic optimization
## [0.5.2] -- 2022-08-26
### Changed
- Update to JuMP 1.x
## [0.5.1] -- 2021-07-23
### Added
- Allow user to specify locations as unique identifiers, instead of latitude and longitude (e.g. `us-state:IL` or `2018-us-county:17043`)
- Add what-if scenarios.
- Add products report.
## [0.5.0] -- 2021-01-06
### Added
- Allow plants to store input material for processing in later years
## [0.4.0] -- 2020-09-18
### Added
- Generate simplified solution reports (CSV)
## [0.3.3] -- 2020-10-13
### Added
- Add option to write solution to JSON file in RELOG.solve
- Improve error message when instance is infeasible
- Make output file more readable
## [0.3.2] -- 2020-10-07
### Added
- Add "building period" parameter
## [0.3.1] -- 2020-07-17
### Fixed
- Fix expansion cost breakdown
## [0.3.0] -- 2020-06-25
### Added
- Track emissions and energy (transportation and plants)
### Changed
- Minor changes to input file format:
- Make all dictionary keys lowercase
- Rename "outputs (tonne)" to "outputs (tonne/tonne)"

25
COPYING.md Normal file
View File

@@ -0,0 +1,25 @@
Copyright © 2020, UChicago Argonne, LLC
All Rights Reserved
Software Name: RELOG
By: Argonne National Laboratory
OPEN SOURCE LICENSE
-------------------
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
********************************************************************************
DISCLAIMER
----------
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
********************************************************************************

19
Makefile Normal file
View File

@@ -0,0 +1,19 @@
VERSION := 0.5
clean:
rm -rfv build Manifest.toml test/Manifest.toml deps/formatter/build deps/formatter/Manifest.toml
docs:
cd docs; julia --project=. make.jl; cd ..
rsync -avP --delete-after docs/build/ ../docs/$(VERSION)/
format:
cd deps/formatter; ../../juliaw format.jl
test: test/Manifest.toml
./juliaw test/runtests.jl
test/Manifest.toml: test/Project.toml
julia --project=test -e "using Pkg; Pkg.instantiate()"
.PHONY: docs test format

View File

@@ -1,18 +1,45 @@
name = "RELOG" name = "RELOG"
uuid = "7cafaa7a-b311-45f0-b313-80bf15b5e5e5" uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
authors = ["Alinson S. Xavier <git@axavier.org>"] authors = ["Alinson S Xavier <axavier@anl.gov>"]
version = "0.8.0" version = "0.5.2"
[deps] [deps]
CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205" CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d" Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JSONSchema = "7d188eb4-7ad8-530c-ae41-71a32a6d4692"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572" JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" StochasticPrograms = "8b8459f2-c380-502b-8633-9aed2d6c2b35"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
[compat]
CRC = "4"
CSV = "0.10"
DataFrames = "1"
DataStructures = "0.18"
GZip = "0.5"
Geodesy = "1"
JSON = "0.21"
JSONSchema = "1"
JuMP = "1"
MathOptInterface = "1"
OrderedCollections = "1"
ProgressBars = "1"
Shapefile = "0.8"
ZipFile = "0.10"
julia = "1"

View File

@@ -1,5 +1,8 @@
<h1 align="center">RELOG: Supply Chain Analysis and Optimization</h1> <h1 align="center">RELOG: Reverse Logistics Optimization</h1>
<p align="center"> <p align="center">
<a href="https://github.com/ANL-CEEESA/RELOG/actions">
<img src="https://github.com/ANL-CEEESA/RELOG/workflows/CI/badge.svg">
</a>
<a href="https://doi.org/10.5281/zenodo.4302341"> <a href="https://doi.org/10.5281/zenodo.4302341">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4302341.svg"> <img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4302341.svg">
</a> </a>
@@ -8,44 +11,31 @@
</a> </a>
</p> </p>
**RELOG** is an open-source package designed to optimize supply chains for **RELOG** is a supply chain optimization package focusing on reverse logistics and reverse manufacturing. For example, the package can be used to determine where to build recycling plants, what sizes should they have and which customers should be served by which plants. The package supports customized reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods.
forward, reverse and circular manufacturing. Using mixed-integer linear
optimization, RELOG helps users determine strategic decisions such as:
- Where and when to build manufacturing and recycling plants
- The size of these plants, when to expand them, and by how much
- The sources for each plant's input materials and the destinations for their
processed outputs
- Whether to process input materials immediately or store them for later use
RELOG has been successfully applied in research at various laboratories and
universities, focusing on areas like critical material recovery from spent NiMH
and Li-Ion batteries, biomass processing for hydrogen production, and the
recycling of electronics, plastics and solar PV materials, among others. See
references for more details.
## Screenshots <img src="https://anl-ceeesa.github.io/RELOG/0.5/assets/ex_transportation.png" width="1000px"/>
<img src="https://raw.githubusercontent.com/ANL-CEEESA/RELOG/refs/heads/circular/docs/src/assets/relog.png" width="1000px"/> ### Documentation
## Documentation * [Usage](https://anl-ceeesa.github.io/RELOG/0.5/usage)
* [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.5/format)
* [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.5/reports)
* [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.5/model)
See official documentation at: https://anl-ceeesa.github.io/RELOG/ ### Authors
## Authors * **Alinson S. Xavier** <<axavier@anl.gov>>
* **Nwike Iloeje** <<ciloeje@anl.gov>>
* **John Atkins**
* **Kyle Sun**
- **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov> ### License
- **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>
- **Kavitha G. Menon,** Argonne National Laboratory
- **John Atkins,** Argonne National Laboratory
- **Kyle Sun,** Argonne National Laboratory
- **Audrey Gallier,** Argonne National Laboratory
## License
```text ```text
RELOG: Reverse Logistics Optimization RELOG: Reverse Logistics Optimization
Copyright © 2020-2025, UChicago Argonne, LLC. All Rights Reserved. Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met: provided that the following conditions are met:

5
deps/formatter/Project.toml vendored Normal file
View File

@@ -0,0 +1,5 @@
[deps]
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
[compat]
JuliaFormatter = "0.14.4"

8
deps/formatter/format.jl vendored Normal file
View File

@@ -0,0 +1,8 @@
using JuliaFormatter
format(
[
"../../src",
"../../test",
],
verbose=true,
)

View File

@@ -1,5 +1,4 @@
[deps] [deps]
BetterFileWatching = "c9fd44ac-77b5-486c-9482-9798bd063cc6"
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
RELOG = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008" RELOG = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"

View File

@@ -1,16 +1,14 @@
using Documenter using Documenter, RELOG
using RELOG
using BetterFileWatching
function make() function make()
makedocs( makedocs(
sitename="RELOG", sitename="RELOG",
pages=[ pages=[
"Home" => "index.md", "Home" => "index.md",
"User guide" => [ "usage.md",
"problem.md",
"format.md", "format.md",
] "reports.md",
"model.md",
], ],
format = Documenter.HTML( format = Documenter.HTML(
assets=["assets/custom.css"], assets=["assets/custom.css"],
@@ -18,9 +16,4 @@ function make()
) )
end end
function watch()
make() make()
watch_folder("src") do event
make()
end
end

Binary file not shown.

Before

Width:  |  Height:  |  Size: 2.0 MiB

View File

@@ -1,37 +1,51 @@
# Input data format # Input and Output Data Formats
RELOG accepts as input a JSON file with five sections: `parameters`, `products`, In this page, we describe the input and output JSON formats used by RELOG. In addition to these, RELOG can also produce [simplified reports](reports.md) in tabular data format.
`centers`, `plants` and `emissions`. Below, we describe each section in more
detail.
## Parameters ## Input Data Format (JSON)
| Key | Description | RELOG accepts as input a JSON file with three sections: `parameters`, `products` and `plants`. Below, we describe each section in more detail.
| :------------------------ | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `time horizon (years)` | Number of years in the simulation. | ### Parameters
The **parameters** section describes details about the simulation itself.
| Key | Description
|:--------------------------|:---------------|
|`time horizon (years)` | Number of years in the simulation.
|`building period (years)` | List of years in which we are allowed to open new plants. For example, if this parameter is set to `[1,2,3]`, we can only open plants during the first three years. By default, this equals `[1]`; that is, plants can only be opened during the first year. | |`building period (years)` | List of years in which we are allowed to open new plants. For example, if this parameter is set to `[1,2,3]`, we can only open plants during the first three years. By default, this equals `[1]`; that is, plants can only be opened during the first year. |
| `distance metric` | Metric used to compute distances between pairs of locations. Valid options are: `"Euclidean"`, for the straight-line distance between points; or `"driving"` for an approximated driving distance. If not specified, defaults to `"Euclidean"`. |
#### Example #### Example
```json ```json
{ {
"parameters": { "parameters": {
"time horizon (years)": 4, "time horizon (years)": 2,
"building period (years)": [1], "building period (years)": [1]
"distance metric": "driving"
} }
} }
``` ```
## Products ### Products
| Key | Description | The **products** section describes all products and subproducts in the simulation. The field `instance["Products"]` is a dictionary mapping the name of the product to a dictionary which describes its characteristics. Each product description contains the following keys:
| :------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `transportation cost ($/km/tonne)` | The cost to transport this product. Must be a time series. | | Key | Description
| `transportation energy (J/km/tonne)` | The energy required to transport this product. Must be a time series. Optional. | |:--------------------------------------|:---------------|
| `transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional. | |`transportation cost ($/km/tonne)` | The cost to transport this product. Must be a time series.
| `disposal limit (tonne)` | Global disposal limit for this product, per year, across all plants and centers. Entry may be `null` if unlimited. Note that individual plants and centers may also have their individual disposal limits for this product. | |`transportation energy (J/km/tonne)` | The energy required to transport this product. Must be a time series. Optional.
|`transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional.
|`initial amounts` | A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a time series.
| `disposal limit (tonne)` | Total amount of product that can be disposed of across all collection centers. If omitted, all product must be processed. This parameter has no effect on product disposal at plants.
| `disposal cost ($/tonne)` | Cost of disposing one tonne of this product at a collection center. If omitted, defaults to zero. This parameter has no effect on product disposal costs at plants.
Each product may have some amount available at the beginning of each time period. In this case, the key `initial amounts` maps to a dictionary with the following keys:
| Key | Description
|:------------------------|:---------------|
| `latitude (deg)` | The latitude of the location.
| `longitude (deg)` | The longitude of the location.
| `amount (tonne)` | The amount of the product initially available at the location. Must be a time series.
#### Example #### Example
@@ -39,211 +53,178 @@ detail.
{ {
"products": { "products": {
"P1": { "P1": {
"transportation cost ($/km/tonne)": 0.015, "initial amounts": {
"transportation energy (J/km/tonne)": 0.12,
"transportation emissions (tonne/km/tonne)": {
"CO2": 0.052,
"CH4": 0.003
},
"disposal limit (tonne)": 100.0
}
}
}
```
## Centers
| Key | Description |
| :------------------------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `latitude (deg)` | The latitude of the center. |
| `longitude (deg)` | The longitude of the center. |
| `input` | The name of the product this center takes as input from the plants. May be `null` if the center accept no input product. |
| `outputs` | List of output products collected by the center. May be `[]` if none. |
| `fixed output (tonne)` | Dictionary mapping the name of each output product to the amount generated by this center each year, regardless of how much input the center receives. For example, if this field equals to `{"P1": [1.0, 2.0, 3.0, 4.0]}`, then this center generates 1.0, 2.0, 3.0 and 4.0 tonnes of P2 in years 1, 2, 3 and 4, respectively. |
| `variable output (tonne/tonne)` | Dictionary mapping the name of each output product to the amount of output generated, for each tonne of input material, and for each year after the input is received. For example, in a 4-year simulation, if this field equals to `{"P1": [0.1, 0.3, 0.6, 0.0]}` and the center receives 1.0, 2.0, 3.0 and 4.0 tonnes of input material in years 1, 2, 3 and 4, then the center will produce $1.0 * 0.1 = 0.1$ of P1 in the first year, $1.0 * 0.3 + 2.0 * 0.1 = 0.5$ the second year, $1.0 * 0.6 + 2.0 * 0.3 + 3.0 * 0.1 = 1.5$ in the third year, and $2.0 * 0.6 + 3.0 * 0.3 + 4.0 * 0.1 = 2.5$ in the final year. |
| `revenue ($/tonne)` | Revenue generated by each tonne of input material sent to the center. If the center accepts no input, this should be `null` |
| `collection cost ($/tonne)` | Dictionary mapping the name of each output product to the cost of collecting one tonne of the product. |
| `operating cost ($)` | Fixed cost to operate the center for one year, regardless of amount of product received or generated. |
| `disposal limit (tonne)` | Dictionary mapping the name of each output product to the maximum disposal amount allowed per year of the product at the center. Entry may be `null` if unlimited. |
| `disposal cost ($/tonne)` | Dictionary mapping the name of each output product to the cost to dispose one tonne of the product at the center. |
```json
{
"centers": {
"C1": { "C1": {
"latitude (deg)": 41.881, "latitude (deg)": 7.0,
"longitude (deg)": -87.623, "longitude (deg)": 7.0,
"input": "P1", "amount (tonne)": [934.56, 934.56]
"outputs": ["P2", "P3"],
"fixed output (tonne)": {
"P2": [100, 50, 0, 0],
"P3": [20, 10, 0, 0]
},
"variable output (tonne/tonne)": {
"P2": [0.12, 0.25, 0.12, 0.0],
"P3": [0.25, 0.25, 0.25, 0.0]
},
"revenue ($/tonne)": [12.0, 12.0, 12.0, 12.0],
"collection cost ($/tonne)": {
"P2": [0.25, 0.25, 0.25, 0.25],
"P3": [0.37, 0.37, 0.37, 0.37]
},
"operating cost ($)": [150.0, 150.0, 150.0, 150.0],
"disposal limit (tonne)": {
"P2": [0, 0, 0, 0],
"P3": [null, null, null, null]
},
"disposal cost ($/tonne)": {
"P2": [0.23, 0.23, 0.23, 0.23],
"P3": [1.0, 1.0, 1.0, 1.0]
}
}, },
"C2": { "C2": {
"latitude (deg)": 41.881, "latitude (deg)": 7.0,
"longitude (deg)": -87.623, "longitude (deg)": 19.0,
"input": null, "amount (tonne)": [198.95, 198.95]
"outputs": ["P4"],
"variable output (tonne/tonne)": {
"P4": [0, 0, 0, 0]
},
"fixed output (tonne)": {
"P4": [50, 60, 70, 80]
},
"revenue ($/tonne)": null,
"collection cost ($/tonne)": {
"P4": [0.25, 0.25, 0.25, 0.25]
},
"operating cost ($)": [150.0, 150.0, 150.0, 150.0],
"disposal limit (tonne)": {
"P4": [null, null, null, null]
},
"disposal cost ($/tonne)": {
"P4": [0, 0, 0, 0]
}
}, },
"C3": { "C3": {
"latitude (deg)": 41.881, "latitude (deg)": 84.0,
"longitude (deg)": -87.623, "longitude (deg)": 76.0,
"input": "P1", "amount (tonne)": [212.97, 212.97]
"outputs": [], }
"variable output (tonne/tonne)": {}, },
"constant output (tonne)": {}, "transportation cost ($/km/tonne)": [0.015, 0.015],
"revenue ($/tonne)": [12.0, 12.0, 12.0, 12.0], "transportation energy (J/km/tonne)": [0.12, 0.11],
"collection cost ($/tonne)": {}, "transportation emissions (tonne/km/tonne)": {
"operating cost ($)": [150.0, 150.0, 150.0, 150.0], "CO2": [0.052, 0.050],
"disposal limit (tonne)": {}, "CH4": [0.003, 0.002]
"disposal cost ($/tonne)": {} },
"disposal cost ($/tonne)": [-10.0, -12.0],
"disposal limit (tonne)": [1.0, 1.0],
},
"P2": {
"transportation cost ($/km/tonne)": [0.022, 0.020]
},
"P3": {
"transportation cost ($/km/tonne)": [0.0125, 0.0125]
},
"P4": {
"transportation cost ($/km/tonne)": [0.0175, 0.0175]
} }
} }
} }
``` ```
## Plants ### Processing plants
| Key | Description | The **plants** section describes the available types of reverse manufacturing plants, their potential locations and associated costs, as well as their inputs and outputs. The field `instance["Plants"]` is a dictionary mapping the name of the plant to a dictionary with the following keys:
| :----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| `latitude (deg)` | The latitude of the plant, in degrees. |
| `longitude (deg)` | The longitude of the plant, in degrees. |
| `input mix (%)` | Dictionary mapping the name of each input product to the amount required (as a percentage). Must sum to 100%. |
| `output (tonne)` | Dictionary mapping the name of each output product to the amount produced (in tonne) for one tonne of input mix. |
| `processing emissions (tonne)` | A dictionary mapping the name of each greenhouse gas, produced to process each tonne of input, to the amount of gas produced (in tonne). |
| `storage cost ($/tonne)` | Dictionary mapping the name of each input product to the cost of storing the product for one year at the plant for later processing. |
| `storage limit (tonne)` | Dictionary mapping the name of each input product to the maximum amount allowed in storage at any time. May be `null` if unlimited. |
| `disposal cost ($/tonne)` | Dictionary mapping the name of each output product to the cost of disposing it at the plant. |
| `disposal limit (tonne)` | Dictionary mapping the name of each output product to the maximum amount allowed to be disposed of at the plant. May be `null` if unlimited. |
| `capacities` | List describing what plant sizes are allowed, and their characteristics. |
| `initial capacity (tonne)` | Capacity already available. If the plant has not been built yet, this should be `0`. |
The entries in the `capacities` list should be dictionaries with the following | Key | Description
keys: |:------------------------|:---------------|
| `input` | The name of the product that this plant takes as input. Only one input is accepted per plant.
| `outputs (tonne/tonne)` | A dictionary specifying how many tonnes of each product is produced for each tonnes of input. For example, if the plant outputs 0.5 tonnes of P2 and 0.25 tonnes of P3 for each tonnes of P1 provided, then this entry should be `{"P2": 0.5, "P3": 0.25}`. If the plant does not output anything, this key may be omitted.
|`energy (GJ/tonne)` | The energy required to process 1 tonne of the input. Must be a time series. Optional.
|`emissions (tonne/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to process each tonne of input, to the amount of gas produced (in tonne). Must be a time series. Optional.
| `locations` | A dictionary mapping the name of the location to a dictionary which describes the site characteristics (see below).
| Key | Description | Each type of plant is associated with a set of potential locations where it can be built. Each location is represented by a dictionary with the following keys:
| :---------------------------------- | :-------------------------------------------------------------------------------------------------- |
| `size (tonne)` | The size of the plant. |
| `opening cost ($)` | The cost to open a plant of this size. |
| `fixed operating cost ($)` | The cost to keep the plant open, even if the plant doesn't process anything. |
| `variable operating cost ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be the same for all capacities. |
```json | Key | Description
{ |:------------------------------|---------------|
"plants": { | `latitude (deg)` | The latitude of the location, in degrees.
"L1": { | `longitude (deg)` | The longitude of the location, in degrees.
"latitude (deg)": 41.881, | `disposal` | A dictionary describing what products can be disposed locally at the plant.
"longitude (deg)": -87.623, | `storage` | A dictionary describing the plant's storage.
"input mix (%)": { | `capacities (tonne)` | A dictionary describing what plant sizes are allowed, and their characteristics.
"P1": 95.3,
"P2": 4.7
},
"output (tonne)": {
"P3": 0.25,
"P4": 0.12,
"P5": 0.1
},
"processing emissions (tonne)": {
"CO2": 0.1
},
"storage cost ($/tonne)": {
"P1": 0.1,
"P2": 0.1
},
"storage limit (tonne)": {
"P1": 100,
"P2": null
},
"disposal cost ($/tonne)": {
"P3": 0,
"P4": 0.86,
"P5": 0.25,
},
"disposal limit (tonne)": {
"P3": null,
"P4": 1000.0,
"P5": 1000.0
},
"capacities": [
{
"size": 100,
"opening cost ($)": 500,
"fixed operating cost ($)": 300,
"variable operating cost ($/tonne)": 5.0
},
{
"size": 500,
"opening cost ($)": 1000.0,
"fixed operating cost ($)": 400.0,
"variable operating cost ($/tonne)": 5.0.
}
],
"initial capacity (tonne)": 0,
}
}
}
```
## Emissions The `storage` dictionary should contain the following keys:
| Key | Description | | Key | Description
| :------------------ | :------------------------------------------------------------------------------------------------------------------------------------- | |:------------------------|:---------------|
| `limit (tonne)` | Maximum amount of this greenhouse gas allowed to be emitted per year across the entire supply chain. Entry may be `null` if unlimited. | | `cost ($/tonne)` | The cost to store a tonne of input product for one time period. Must be a time series.
| `penalty ($/tonne)` | Penalty cost per tonne of this greenhouse gas emitted. | | `limit (tonne)` | The maximum amount of input product this plant can have in storage at any given time.
The keys in the `disposal` dictionary should be the names of the products. The values are dictionaries with the following keys:
| Key | Description
|:------------------------|:---------------|
| `cost ($/tonne)` | The cost to dispose of the product. Must be a time series.
| `limit (tonne)` | The maximum amount that can be disposed of. If an unlimited amount can be disposed, this key may be omitted. Must be a time series.
The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes). The values are dictionaries with the following keys:
| Key | Description
|:--------------------------------------|:---------------|
| `opening cost ($)` | The cost to open a plant of this size.
| `fixed operating cost ($)` | The cost to keep the plant open, even if the plant doesn't process anything. Must be a time series.
| `variable operating cost ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series.
#### Example #### Example
```json ```json
{ {
"emissions": { "plants": {
"CO2": { "F1": {
"limit (tonne)": 1000.0, "input": "P1",
"penalty ($/tonne)": 50.0 "outputs (tonne/tonne)": {
"P2": 0.2,
"P3": 0.5
}, },
"CH4": { "energy (GJ/tonne)": [0.12, 0.11],
"limit (tonne)": null, "emissions (tonne/tonne)": {
"penalty ($/tonne)": 1200.0 "CO2": [0.052, 0.050],
"CH4": [0.003, 0.002]
}, },
"N2O": { "locations": {
"limit (tonne)": 10.0, "L1": {
"penalty ($/tonne)": 15000.0 "latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"disposal": {
"P2": {
"cost ($/tonne)": [-10.0, -12.0],
"limit (tonne)": [1.0, 1.0]
}
},
"storage": {
"cost ($/tonne)": [5.0, 5.3],
"limit (tonne)": 100.0,
},
"capacities (tonne)": {
"100": {
"opening cost ($)": [500, 530],
"fixed operating cost ($)": [300.0, 310.0],
"variable operating cost ($/tonne)": [5.0, 5.2],
},
"500": {
"opening cost ($)": [750, 760],
"fixed operating cost ($)": [400.0, 450.0],
"variable operating cost ($/tonne)": [5.0, 5.2]
}
}
}
}
} }
} }
} }
``` ```
### Geographic database
Instead of specifying locations using latitudes and longitudes, it is also possible to specify them using unique identifiers, such as the name of a US state, or the county FIPS code. This works anywhere `latitude (deg)` and `longitude (deg)` are expected. For example, instead of:
```json
{
"initial amounts": {
"C1": {
"latitude (deg)": 37.27182,
"longitude (deg)": -119.2704,
"amount (tonne)": [934.56, 934.56]
},
}
}
```
is is possible to write:
```json
{
"initial amounts": {
"C1": {
"location": "us-state:CA",
"amount (tonne)": [934.56, 934.56]
},
}
}
```
Location names follow the format `db:id`, where `db` is the name of the database and `id` is the identifier for a specific location. RELOG currently includes the following databases:
Database | Description | Examples
:--------|:------------|:---------
`us-state`| List of states of the United States. | `us-state:IL` (State of Illinois)
`2018-us-county` | List of United States counties, as of 2018. IDs are 5-digit FIPS codes. | `2018-us-county:17043` (DuPage county in Illinois)
### Current limitations
* Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation.
* Plants can be expanded at any time, even long after they are open.
* All material available at the beginning of a time period must be entirely processed by the end of that time period. It is not possible to store unprocessed materials from one time period to the next.
* Up to two plant sizes are currently supported. Variable operating costs must be the same for all plant sizes.
## Output Data Format (JSON)
To be documented.

View File

@@ -1,42 +1,35 @@
# RELOG -- Supply Chain Analysis and Optimization # RELOG: Reverse Logistics Optimization
**RELOG** is an open-source package designed to optimize supply chains for **RELOG** is an open-source supply chain optimization package focusing on reverse logistics and reverse manufacturing. The package uses Mixed-Integer Linear Programming to determine where to build recycling plants, what size should these plants have and which customers should be served by which plants. The package supports custom reverse logistics pipelines, with multiple types of plants, multiple types of product and multiple time periods.
forward, reverse and circular manufacturing. Using mixed-integer linear
optimization, RELOG helps users determine strategic decisions such as:
- Where and when to build manufacturing and recycling plants
- The size of these plants, when to expand them, and by how much
- The sources for each plant's input materials and the destinations for their
processed outputs
- Whether to process input materials immediately or store them for later use
RELOG has been successfully applied in research at various laboratories and
universities, focusing on areas like critical material recovery from spent NiMH
and Li-Ion batteries, biomass processing for hydrogen production, and the
recycling of electronics, plastics and solar PV materials, among others. See
references for more details.
## Screenshots
```@raw html ```@raw html
<center> <center>
<img src="assets/relog.png" width="1000px"/> <img src="assets/ex_transportation.png" width="1000px"/>
</center> </center>
``` ```
## Authors
- **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov> ### Table of Contents
- **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>
- **Kavitha G. Menon,** Argonne National Laboratory
- **John Atkins,** Argonne National Laboratory
- **Kyle Sun,** Argonne National Laboratory
- **Audrey Gallier,** Argonne National Laboratory
## License ```@contents
Pages = ["usage.md", "format.md", "reports.md", "model.md"]
Depth = 3
```
### Source Code
* [https://github.com/ANL-CEEESA/RELOG](https://github.com/ANL-CEEESA/RELOG)
### Authors
* **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
* **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>
### License
```text ```text
RELOG: Reverse Logistics Optimization RELOG: Reverse Logistics Optimization
Copyright © 2020-2025, UChicago Argonne, LLC. All Rights Reserved. Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met: provided that the following conditions are met:

196
docs/src/model.md Normal file
View File

@@ -0,0 +1,196 @@
# Optimization Model
In this page, we describe the precise mathematical optimization model used by RELOG to find the optimal logistics plan. This model is a variation of the classical Facility Location Problem, which has been widely studied in the operations research literature. To simplify the exposition, we present the simplified case where there is only one type of plant.
## Mathematical Description
### Sets
Symbol | Description
:-------|:------------
$L$ | Set of locations holding the original material to be recycled
$M$ | Set of materials recovered during the reverse manufacturing process
$P$ | Set of potential plants to open
$T = \{ 1, \ldots, t^{max} \}$ | Set of time periods
### Constants
#### Plants
Symbol | Description | Unit
:-------|:------------|:---
$c^\text{disp}_{pmt}$ | Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ | \$/tonne/km
$c^\text{exp}_{pt}$ | Cost of adding one tonne of capacity to plant $p$ at time $t$ | \$/tonne
$c^\text{open}_{pt}$ | Cost of opening plant $p$ at time $t$, at minimum capacity | $
$c^\text{f-base}_{pt}$ | Fixed cost of keeping plant $p$ open during time period $t$ | $
$c^\text{f-exp}_{pt}$ | Increase in fixed cost for each additional tonne of capacity | \$/tonne
$c^\text{var}_{pt}$ | Variable cost of processing one tonne of input at plant $p$ at time $t$ | \$/tonne
$c^\text{store}_{pt}$ | Cost of storing one tonne of original material at plant $p$ at time $t$ | \$/tonne
$m^\text{min}_p$ | Minimum capacity of plant $p$ | tonne
$m^\text{max}_p$ | Maximum capacity of plant $p$ | tonne
$m^\text{disp}_{pmt}$ | Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ | tonne
$m^\text{store}_p$ | Maximum amount of original material that plant $p$ can store for later processing. | tonne
#### Products
Symbol | Description | Unit
:-------|:------------|:---
$\alpha_{pm}$ | Amount of material $m$ recovered by plant $t$ for each tonne of original material | tonne/tonne
$m^\text{initial}_{lt}$ | Amount of original material to be recycled at location $l$ during time $t$ | tonne
#### Transportation
Symbol | Description | Unit
:-------|:------------|:---
$c^\text{tr}_{t}$ | Transportation cost during time $t$ | \$/tonne/km
$d_{lp}$ | Distance between plant $p$ and location $l$ | km
### Decision variables
Symbol | Description | Unit
:-------|:------------|:---
$q_{mpt}$ | Amount of material $m$ recovered by plant $p$ during time $t$ | tonne
$u_{pt}$ | Binary variable that equals 1 if plant $p$ starts operating at time $t$ | Boolean
$w_{pt}$ | Extra capacity (amount above the minimum) added to plant $p$ during time $t$ | tonne
$x_{pt}$ | Binary variable that equals 1 if plant $p$ is operational at time $t$ | Boolean
$y_{lpt}$ | Amount of product sent from location $l$ to plant $p$ during time $t$ | tonne
$z^{\text{disp}}_{mpt}$ | Amount of material $m$ disposed of by plant $p$ during time $t$ | tonne
$z^{\text{store}}_{pt}$ | Amount of original material in storage at plant $p$ by the end of time period $t$ | tonne
$z^{\text{proc}}_{mpt}$ | Amount of original material processed by plant $p$ during time period $t$ | tonne
### Objective function
RELOG minimizes the overall capital, production and transportation costs:
```math
\begin{align*}
\text{minimize} \;\; &
\sum_{t \in T} \sum_{p \in P} \left[
c^\text{open}_{pt} u_{pt} +
c^\text{f-base}_{pt} x_{pt} +
\sum_{i=1}^t c^\text{f-exp}_{pt} w_{pi} +
c^{\text{exp}}_{pt} w_{pt}
\right] + \\
&
\sum_{t \in T} \sum_{p \in P} \left[
c^{\text{store}}_{pt} z^{\text{store}}_{pt} +
c^{\text{proc}}_{pt} z^{\text{proc}}_{pt}
\right] + \\
&
\sum_{t \in T} \sum_{l \in L} \sum_{p \in P}
c^{\text{tr}}_t d_{lp} y_{lpt}
\\
&
\sum_{t \in T} \sum_{p \in P} \sum_{m \in M} c^{\text{disp}}_{pmt} z_{pmt}
\end{align*}
```
In the first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period.
In the second line, we have storage and variable processing costs.
In the third line, we have transportation costs.
In the fourth line, we have the disposal costs.
### Constraints
* All original materials must be sent to a plant:
```math
\begin{align*}
& \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt}
& \forall l \in L, t \in T
\end{align*}
```
* Amount received equals amount processed plus stored. Furthermore, all original material should be processed by the end of the simulation.
```math
\begin{align*}
& \sum_{l \in L} y_{lpt} + z^{\text{store}}_{p,t-1}
= z^{\text{proc}}_{pt} + z^{\text{store}}_{p,t}
& \forall p \in P, t \in T \\
& z^{\text{store}}_{p,0} = 0
& \forall p \in P \\
& z^{\text{store}}_{p,t^{\max}} = 0
& \forall p \in P
\end{align*}
```
* Plants have a limited processing capacity. Furthermore, if a plant is closed, it has zero processing capacity:
```math
\begin{align*}
& z^{\text{proc}}_{pt} \leq m^\text{min}_p x_p + \sum_{i=1}^t w_p
& \forall p \in P, t \in T
\end{align*}
```
* Plants have limited storage capacity. Furthermore, if a plant is closed, is has zero storage capacity:
```math
\begin{align*}
& z^{\text{store}}_{pt} \leq m^\text{store}_p x_p
& \forall p \in P, t \in T
\end{align*}
```
* Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded:
```math
\begin{align*}
& \sum_{i=1}^t w_p \leq m^\text{max}_p x_p
& \forall p \in P, t \in T
\end{align*}
```
* Amount of recovered material is proportional to amount processed:
```math
\begin{align*}
& q_{mpt} = \alpha_{pm} z^{\text{proc}}_{pt}
& \forall m \in M, p \in P, t \in T
\end{align*}
```
* Because we only consider a single type of plant, all recovered material must be immediately disposed of. In RELOG's full model, recovered materials may be sent to another plant for further processing.
```math
\begin{align*}
& q_{mpt} = z_{mpt}
& \forall m \in M, p \in P, t \in T
\end{align*}
```
* A plant is operational at time $t$ if it was operational at time $t-1$ or it was built at time $t$. This constraint also prevents a plant from being built multiple times.
```math
\begin{align*}
& x_{pt} = x_{p,t-1} + u_{pt}
& \forall p \in P, t \in T \setminus \{1\} \\
& x_{p,1} = u_{p,1}
& \forall p \in P
\end{align*}
```
* Variable bounds:
```math
\begin{align*}
& q_{mpt} \geq 0
& \forall m \in M, p \in P, t \in T \\
& u_{pt} \in \{0,1\}
& \forall p \in P, t \in T \\
& w_{pt} \geq 0
& \forall p \in P, t \in T \\
& x_{pt} \in \{0,1\}
& \forall p \in P, t \in T \\
& y_{lpt} \geq 0
& \forall l \in L, p \in P, t \in T \\
& z^{\text{store}}_{pt} \geq 0
& p \in P, t \in T \\
& z^{\text{disp}}_{mpt}, z^{\text{proc}}_{mpt} \geq 0
& \forall m \in M, p \in P, t \in T
\end{align*}
```

View File

@@ -1,406 +0,0 @@
# Mathematical problem definition
## Overview and assumptions
The mathematical model employed by RELOG is based on three main components:
1. **Products and Materials:** Inputs and outputs for both manufacturing and
recycling plants. This includes raw materials, whether virgin or recovered,
and final products, whether new or at their end-of-life. Each product has
associated transportation parameters, such as costs, energy and emissions.
2. **Manufacturing and Recycling Plants:** Facilities that take in specific
materials and produce certain products. The outputs can be sent to another
plant for further processing, to a collection & distribution center for
customer sale, or simply disposed of at landfill. Plants have associated
costs (capital, fixed and operating), as well as various limits (processing
capacity, storage and disposal limits).
3. **Collection and Distribution Centers:** Facilities that receive final
products from the plants, sell them to customers, and then collect them back
once they reach their end-of-life. Collected products can either be sent to a
plant for recycling or disposed of at a local landfill. Centers have
associated revenue and various costs, such as operating cost, collection cost
and disposal cost. The amount of material collected by a center can either be
a fixed rate per year, or depend on the amount of product sold at the center
in previous years.
!!! note
- We assume that transportation costs, energy and emissions scale linearly with transportation distance and amount being transported. Distances between locations are calculated using either approximated driving distances (continental U.S. only) or straight-line distances.
- Once a plant is opened, we assume that it remains open until the end of the planning horizon. Similarly, once a plant is expanded, its size cannot be reduced at a later time.
- In addition to serving as a source of end-of-life products, centers can also serve as a source for virgin materials. In this case, the center does not receive any inputs from manufacturing or recycling plants, and it generates the desired material at a fixed rate. Collection cost, in this case, refers to the cost to produce the virgin material.
- We assume that centers accept either no input product, or a single input product.
## Sets
| Symbol | Description |
| :------- | :-------------------------------------------------------------------------------------------------------------------------------------------------- |
| $C$ | Set of collection and distribution centers |
| $P$ | Set of manufacturing and recycling plants |
| $M$ | Set of products and materials |
| $G$ | Set of greenhouse gases |
| $M^+_u$ | Set of output products of plant/center $u$. |
| $M^-_u$ | Set of input products of plant/center $u$. |
| $T$ | Set of time periods in the planning horizon. We assume $T=\{1,\ldots,t^{max}\}.$ |
| $E$ | Set of transportation edges. Specifically, $(u,v,m) \in E$ if $m$ is an output of $u$ and an input of $v$, where $m \in M$ and $u, v \in P \cup C$. |
| $E^-(v)$ | Set of incoming edges for plant/center v. Specifically, edges $(u,m)$ such that $(u,v,m) \in E$. |
| $E^+(u)$ | Set of outgoing edges for plant/center u. Specifically, edges $(v,m)$ such that $(u,v,m) \in E$. |
## Constants
| Symbol | Description | Unit |
| :---------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------- |
| $K^{\text{dist}}_{uv}$ | Distance between plants/centers $u$ and $v$ | km |
| $K^\text{cap-init}_p$ | Initial capacity of plant $p$ | tonne |
| $K^\text{cap-max}_p$ | Maximum capacity of plant $p$ | tonne |
| $K^\text{cap-min}_p$ | Minimum capacity of plant $p$ | tonne |
| $K^\text{disp-limit}_{mt}$ | Maximum amount of material $m$ that can be disposed of (globally) at time $t$ | tonne |
| $K^\text{disp-limit}_{mut}$ | Maximum amount of material $m$ that can be disposed of at plant/center $u$ at time $t$ | tonne |
| $K^\text{em-limit}_{gt}$ | Maximum amount of greenhouse gas $g$ allowed to be emitted (globally) at time $t$ | tonne |
| $K^\text{em-plant}_{gpt}$ | Amount of greenhouse gas $g$ released by plant $p$ at time $t$ for each tonne of input material processed | tonne/tonne |
| $K^\text{em-tr}_{gmt}$ | Amount of greenhouse gas $g$ released by transporting 1 tonne of material $m$ over one km at time $t$ | tonne/km-tonne |
| $K^\text{mix}_{pmt}$ | If plant $p$ receives one tonne of input material at time $t$, then $K^\text{mix}_{pmt}$ is the amount of product $m$ in this mix. Must be between zero and one, and the sum of these amounts must equal to one. | tonne |
| $K^\text{out-fix}_{cmt}$ | Fixed amount of material $m$ collected at center $c$ at time $t$ | tonne |
| $K^\text{out-var-len}_{cm}$ | Length of the $K^\text{out-var}_{c,m,*}$ vector. | -- |
| $K^\text{out-var}_{cmi}$ | Factor used to calculate variable amount of material $m$ collected at center $c$. See `eq_z_collected` for more details. | -- |
| $K^\text{output}_{pmt}$ | Amount of material $m$ produced by plant $p$ at time $t$ for each tonne of input material processed | tonne |
| $K^\text{storage-limit}_{pm}$ | Maximum amount of material $m$ that can be stored at plant $p$ at any time | tonne |
| $R^\text{collect}_{cmt}$ | Cost of collecting material $m$ at center $c$ at time $t$ | \$/tonne |
| $R^\text{disp}_{umt}$ | Cost to dispose of material at plant/center $u$ at time $t$ | \$/tonne |
| $R^\text{em}_{gt}$ | Penalty cost per tonne of greenhouse gas $g$ emitted at time $t$ | \$/tonne |
| $R^\text{expand}_{pt}$ | Cost to increase capacity of plant $p$ at time $t$ | \$/tonne |
| $R^\text{fix-exp}_{pt}$ | Increase in fixed operational cost for plant $p$ at time $t$ for every additional tonne of capacity | \$/tonne |
| $R^\text{fix-min}_{pt}$ | Fixed operating cost for plant $p$ at time $t$ at minimum capacity | \$ |
| $R^\text{fix}_{ct}$ | Fixed operating cost for center $c$ at time $t$ | \$ |
| $R^\text{open}_{pt}$ | Cost to open plant $p$ at time $t$, at minimum capacity | \$ |
| $R^\text{rev}_{ct}$ | Revenue for selling the input product of center $c$ at this center at time $t$ | \$/tonne |
| $R^\text{storage}_{pmt}$ | Cost to store one tonne of material $m$ at plant $p$ at time $t$ for one year | \$/tonne |
| $R^\text{tr}_{mt}$ | Cost to send material $m$ at time $t$ | \$/km-tonne |
| $R^\text{var}_{pt}$ | Cost to process one tonne of input material at plant $p$ at time $t$ | \$/tonne |
## Decision variables
| Symbol | JuMP name | Description | Unit |
| :--------------------------- | :------------------------------------------- | :------------------------------------------------------------------------------------------------------ | :----- |
| $x_{pt}$ | `x[p.name, t]` | One if plant $p$ is operational at time $t$ | binary |
| $y_{uvmt}$ | `y[u.name, v.name, m.name, t]` | Amount of product $m$ sent from plant/center $u$ to plant/center $v$ at time $t$ | tonne |
| $z^{\text{exp}}_{pt}$ | `z_exp[p.name, t]` | Extra capacity installed at plant $p$ at time $t$ above the minimum capacity | tonne |
| $z^{\text{collected}}_{cmt}$ | `z_collected[c.name, m.name, t]` | Amount of material $m$ collected by center $c$ at time $t$ | tonne |
| $z^{\text{disp}}_{umt}$ | `z_disp[u.name, m.name, t]` | Amount of product $m$ disposed of at plant/center $u$ at time $t$ | tonne |
| $z^{\text{em-plant}}_{gpt}$ | `z_em_plant[g.name, p.name, t]` | Amount of greenhouse gas $g$ released by plant $p$ at time $t$ | tonne |
| $z^{\text{em-tr}}_{guvmt}$ | `z_em_tr[g.name, u.name, v.name, m.name, t]` | Amount of greenhouse gas $g$ released at time $t$ due to transportation of material $m$ from $u$ to $v$ | tonne |
| $z^{\text{input}}_{ut}$ | `z_input[u.name, t]` | Total amount received by plant/center $u$ at time $t$ | tonne |
| $z^{\text{prod}}_{umt}$ | `z_prod[u.name, m.name, t]` | Amount of product $m$ produced by plant/center $u$ at time $t$ | tonne |
| $z^{\text{storage}}_{pmt}$ | `z_storage[p.name, m.name, t]` | Amount of input material $m$ stored at plant $p$ at the end of time $t$ | tonne |
| $z^{\text{process}}_{pt}$ | `z_process[p.name, t]` | Total amount of input material processed by plant $p$ at time $t$ | tonne |
## Objective function
The goal is to minimize a linear objective function with the following terms:
- Transportation costs, which depend on transportation distance
$K^{\text{dist}}_{uv}$ and product-specific factor $R^\text{tr}_{mt}$:
```math
\sum_{(u, v, m) \in E} \sum_{t \in T} K^{\text{dist}}_{uv} R^\text{tr}_{mt} y_{uvmt}
```
- Center revenue, obtained by selling products received from manufacturing and
recycling plants:
```math
- \sum_{c \in C} \sum_{(p,m) \in E^-(c)} \sum_{t \in T} R^\text{rev}_{ct} y_{pcmt}
```
- Center collection cost, incurred for each tonne of output material sent to a
plant:
```math
\sum_{c \in C} \sum_{(p,m) \in E^+(c)} \sum_{t \in T} R^\text{collect}_{cmt} y_{cpmt}
```
- Center disposal cost, incurred when disposing of output material, instead of
sending it to a plant:
```math
\sum_{c \in C} \sum_{m \in M^+_c} \sum_{t \in T} R^\text{disp}_{cmt} z^\text{disp}_{cmt}
```
- Center fixed operating cost, incurred for every time period, regardless of
input or output amounts:
```math
\sum_{c \in C} \sum_{t \in T} R^\text{fix}_{ct}
```
- Plant disposal cost, incurred for each tonne of product discarded at the
plant:
```math
\sum_{p \in P} \sum_{m \in M^+_p} \sum_{t \in T} R^\text{disp}_{pmt} z^\text{disp}_{pmt}
```
- Plant opening cost, incurred when the plant goes from non-operational at time
$t-1$ to operational at time $t$. Never incurred if the plant is initially
open:
```math
\sum_{p \in P} \sum_{t \in T} R^\text{open}_{pt} \left(
x_{pt} - x_{p,t-1}
\right)
```
- Plant fixed operating cost, incurred for every time period, regardless of
input or output amounts, as long as the plant is operational. Depends on the
size of the plant:
```math
\sum_{p \in P} \sum_{t \in T} \left(
R^\text{fix-min}_{pt} x_{pt} +
R^\text{fix-exp}_{pt} z^\text{exp}_{pt}
\right)
```
- Plant expansion cost, incurred whenever plant capacity increases:
```math
\sum_{p \in P} \sum_{t \in T} R^\text{expand}_{pt} \left(z^\text{exp}_{pt} - z^\text{exp}_{p,t-1} \right)
```
- Plant variable operating cost, incurred for each tonne of input material
received by the plant:
```math
\sum_{p \in P} \sum_{(u,m) \in E^-(p)} \sum_{t \in T} R^\text{var}_{pt} y_{upmt}
```
- Plant storage cost, incurred for each tonne of material stored at the plant:
```math
\sum_{p \in P} \sum_{m \in M^-_p} \sum_{t \in T} R^\text{storage}_{pmt} z^{\text{storage}}_{pmt}
```
- Emissions penalty cost, incurred for each tonne of greenhouse gas emitted:
```math
\sum_{g \in G} \sum_{t \in T} R^\text{em}_{gt} \left(
\sum_{p \in P} z^{\text{em-plant}}_{gpt} + \sum_{(u,v,m) \in E} z^{\text{em-tr}}_{guvmt}
\right)
```
## Constraints
- Definition of plant input (`eq_z_input[p.name, t]`):
```math
\begin{align*}
& z^{\text{input}}_{pt} = \sum_{(u,m) \in E^-(p)} y_{upmt}
& \forall p \in P, t \in T
\end{align*}
```
- Definition of plant processing (`eq_z_process[p.name, t]`):
```math
\begin{align*}
& z^{\text{process}}_{pt} = z^{\text{input}}_{pt} + \sum_{m \in M^-_p} \left(z^{\text{storage}}_{p,m,t-1} - z^{\text{storage}}_{pmt}\right)
& \forall p \in P, t \in T
\end{align*}
```
- Plant processing mix must have correct proportion
(`eq_process_mix[p.name, m.name, t]`):
```math
\begin{align*}
& \sum_{u : (u,m) \in E^-(p)} y_{upmt} + z^{\text{storage}}_{p,m,t-1} - z^{\text{storage}}_{pmt}
= K^\text{mix}_{pmt} z^{\text{process}}_{pt}
& \forall p \in P, m \in M^-_p, t \in T
\end{align*}
```
- Definition of amount produced by a plant (`eq_z_prod[p.name, m.name, t]`):
```math
\begin{align*}
& z^\text{prod}_{pmt} = K^\text{output}_{pmt} z^{\text{process}}_{pt}
& \forall p \in P, m \in M^+_p, t \in T
\end{align*}
```
- Material produced by a plant must be sent somewhere or disposed of
(`eq_balance[p.name, m.name, t]`):
```math
\begin{align*}
& z^\text{prod}_{pmt} = \sum_{v : (v,m) \in E^+(p)} y_{pvmt} + z^\text{disp}_{pmt}
& \forall p \in P, m \in M^+_p, t \in T
\end{align*}
```
- Plant can only be expanded if the plant is open, and up to a certain amount
(`eq_exp_ub[p.name, t]`):
```math
\begin{align*}
& z^\text{exp}_{pt} \leq \left(K^\text{cap-max}_p - K^\text{cap-min}_p) x_{pt}
& \forall p \in P, t \in T
\end{align*}
```
- Plant is initially open if initial capacity is positive:
```math
\begin{align*}
& x_{p,0} = \begin{cases}
0 & \text{ if } K^\text{cap-init}_p = 0 \\
1 & \text{otherwise}
\end{cases}
& \forall p \in P
\end{align*}
```
- Calculation of initial plant expansion:
```math
\begin{align*}
& z^\text{exp}_{p,0} = K^\text{cap-init}_p - K^\text{cap-min}_p
& \forall p \in P
\end{align*}
```
- Plants cannot process more than their current capacity
(`eq_process_limit[p.name,t]`)
```math
\begin{align*}
& z^\text{process}_{pt} \leq K^\text{cap-min}_p x_{pt} + z^\text{exp}_{pt}
& \forall p \in P, t \in T
\end{align*}
```
- Storage limit at the plants (`eq_storage_limit[p.name, m.name, t]`):
```math
\begin{align*}
& z^{\text{storage}}_{pmt} \leq K^\text{storage-limit}_{pm}
& \forall p \in P, m \in M^-_p, t \in T
\end{align*}
```
- Disposal limit at the plants (`eq_disposal_limit[p.name, m.name, t]`):
```math
\begin{align*}
& z^\text{disp}_{pmt} \leq K^\text{disp-limit}_{pmt}
& \forall p \in P, m \in M^+_p, t \in T
\end{align*}
```
- Once a plant is built, it must remain open until the end of the planning
horizon (`eq_keep_open[p.name, t]`):
```math
\begin{align*}
& x_{pt} \geq x_{p,t-1}
& \forall p \in P, t \in T
\end{align*}
```
- Definition of center input (`eq_z_input[c.name, t]`):
```math
\begin{align*}
& z^\text{input}_{ct} = \sum_{u : (u,m) \in E^-(c)} y_{ucmt}
& \forall c \in C, t \in T
\end{align*}
```
- Calculation of amount collected by the center
(`eq_z_collected[c.name, m.name, t]`). In the equation below,
$K^\text{out-var-len}$ is the length of the $K^\text{out-var}_{c,m,*}$ vector.
```math
\begin{align*}
& z^\text{collected}_{cmt}
= \sum_{i=0}^{\min\{K^\text{out-var-len}_{cm}-1,t-1\}} K^\text{out-var}_{c,m,i+1} z^\text{input}_{c,t-i}
+ K^\text{out-fix}_{cmt}
& \forall c \in C, m \in M^+_c, t \in T
\end{align*}
```
- Products collected at centers must be sent somewhere or disposed of
(`eq_balance[c.name, m.name, t]`):
```math
\begin{align*}
& z^\text{collected}_{cmt} = \sum_{v : (v,m) \in E^+(c)} y_{cvmt} + z^\text{disp}_{cmt}
& \forall c \in C, m \in M^+_c, t \in T
\end{align*}
```
- Disposal limit at the centers (`eq_disposal_limit[c.name, m.name, t]`):
```math
\begin{align*}
& z^\text{disp}_{cmt} \leq K^\text{disp-limit}_{cmt}
& \forall c \in C, m \in M^+_c, t \in T
\end{align*}
```
- Global disposal limit (`eq_disposal_limit[m.name, t]`)
```math
\begin{align*}
& \sum_{p \in P} z^\text{disp}_{pmt} + \sum_{c \in C} z^\text{disp}_{cmt} \leq K^\text{disp-limit}_{mt}
& \forall m \in M, t \in T
\end{align*}
```
- Computation of transportation emissions
(`eq_emission_tr[g.name, u.name, v.name, m.name, t]`):
```math
\begin{align*}
& z^{\text{em-tr}}_{guvmt} = K^{\text{dist}}_{uv} K^\text{em-tr}_{gmt} y_{uvmt}
& \forall g \in G, (u, v, m) \in E, t \in T
\end{align*}
```
- Computation of plant emissions (`eq_emission_plant[g.name, p.name, t]`):
```math
\begin{align*}
& z^{\text{em-plant}}_{gpt} = K^\text{em-plant}_{gpt} z^{\text{process}}_{pt}
& \forall g \in G, p \in P, t \in T
\end{align*}
```
- Global emissions limit (`eq_emission_limit[g.name, t]`):
```math
\begin{align*}
& \sum_{p \in P} z^{\text{em-plant}}_{gpt} + \sum_{(u,v,m) \in E} z^{\text{em-tr}}_{guvmt} \leq K^\text{em-limit}_{gt}
& \forall g \in G, t \in T
\end{align*}
```
- All stored materials must be processed by the end of the time horizon
(`eq_storage_final[p.name, m.name]`):
```math
\begin{align*}
& z^{\text{storage}}_{p,m,t^{max}} = 0
& \forall p \in P, m \in M^-_p
\end{align*}
```
- Initial storage is zero (`eq_storage_initial[p.name, m.name]`):
```math
\begin{align*}
& z^{\text{storage}}_{p,m,0} = 0
& \forall p \in P, m \in M^-_p
\end{align*}
```

View File

@@ -1,4 +1,4 @@
# Solution reports # Simplified Solution Reports
In addition to the full output format described in [data formats](format.md), RELOG can also generate a number of simplified reports in tabular data format (CSV), which can be more easily processed by spreadsheet software (such as Microsoft Excel), by data analysis libraries (such as Pandas) or by relational databases (such as SQLite). In addition to the full output format described in [data formats](format.md), RELOG can also generate a number of simplified reports in tabular data format (CSV), which can be more easily processed by spreadsheet software (such as Microsoft Excel), by data analysis libraries (such as Pandas) or by relational databases (such as SQLite).

View File

@@ -1,4 +1,5 @@
# Usage Usage
=====
## 1. Installation ## 1. Installation
@@ -6,7 +7,13 @@ To use RELOG, the first step is to install the [Julia programming language](http
```julia ```julia
using Pkg using Pkg
Pkg.add(name="RELOG", version="0.7") Pkg.add(name="RELOG", version="0.5")
```
After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed:
```julia
Pkg.test("RELOG")
``` ```
## 2. Modeling the problem ## 2. Modeling the problem
@@ -15,21 +22,21 @@ The two main model components in RELOG are **products** and **plants**.
A **product** is any material that needs to be recycled, any intermediary product produced during the recycling process, or any product recovered at the end of the process. For example, in a NiMH battery recycling study case, products could include (i) the original batteries to be recycled; (ii) the cathode and anode parts of the battery; (iii) rare-earth elements and (iv) scrap metals. A **product** is any material that needs to be recycled, any intermediary product produced during the recycling process, or any product recovered at the end of the process. For example, in a NiMH battery recycling study case, products could include (i) the original batteries to be recycled; (ii) the cathode and anode parts of the battery; (iii) rare-earth elements and (iv) scrap metals.
- The model assumes that some products are initially available at user-specified locations (described by their latitude, longitude and the amount available), while other products only become available during the recycling process. * The model assumes that some products are initially available at user-specified locations (described by their latitude, longitude and the amount available), while other products only become available during the recycling process.
- Products that are initially available must be sent to a plant for processing during the same time period they became available. * Products that are initially available must be sent to a plant for processing during the same time period they became available.
- Transporting products from one location to another incurs a transportation cost (`$/km/tonne`), spends some amount of energy (`J/km/tonne`) and may generate multiple types of emissions (`tonne/tonne`). All these parameters are user-specified and may be product- and time-specific. * Transporting products from one location to another incurs a transportation cost (`$/km/tonne`), spends some amount of energy (`J/km/tonne`) and may generate multiple types of emissions (`tonne/tonne`). All these parameters are user-specified and may be product- and time-specific.
A **plant** is a facility that converts one type of product to another. RELOG assumes that each plant receives a single type of product as input and converts this input into multiple types of products. Multiple types of plants, with different inputs, outputs and performance characteristics, may be specified. In the NiMH battery recycling study case, for example, one type of plant could be a _disassembly plant_, which converts _batteries_ into _cathode_ and _anode_. Another type of plant could be _anode recycling plant_, which converts _anode_ into _rare-earth elements_ and _scrap metals_. A **plant** is a facility that converts one type of product to another. RELOG assumes that each plant receives a single type of product as input and converts this input into multiple types of products. Multiple types of plants, with different inputs, outputs and performance characteristics, may be specified. In the NiMH battery recycling study case, for example, one type of plant could be a *disassembly plant*, which converts *batteries* into *cathode* and *anode*. Another type of plant could be *anode recycling plant*, which converts *anode* into *rare-earth elements* and *scrap metals*.
- To process each tonne of input material, plants incur a variable operating cost (`$/tonne`), spend some amount of energy (`GJ/tonne`), and produce multiple types of emissions (`tonne/tonne`). Plants also incur a fixed operating cost (`$`) regardless of the amount of material they process. All these parameters are user-specified and may be region- and time-specific. * To process each tonne of input material, plants incur a variable operating cost (`$/tonne`), spend some amount of energy (`GJ/tonne`), and produce multiple types of emissions (`tonne/tonne`). Plants also incur a fixed operating cost (`$`) regardless of the amount of material they process. All these parameters are user-specified and may be region- and time-specific.
- Plants can be built at user-specified potential locations. Opening a plant incurs a one-time opening cost (`$`) which may be region- and time-specific. Plants also have a limited capacity (in `tonne`), which indicates the maximum amount of input material they are able to process per year. When specifying potential locations for each type of plant, it is also possible to specify the minimum and maximum capacity of the plants that can be built at that particular location. Different plants sizes may have different opening costs and fixed operating costs. After a plant is built, it can be further expanded in the following years, up to its maximum capacity. * Plants can be built at user-specified potential locations. Opening a plant incurs a one-time opening cost (`$`) which may be region- and time-specific. Plants also have a limited capacity (in `tonne`), which indicates the maximum amount of input material they are able to process per year. When specifying potential locations for each type of plant, it is also possible to specify the minimum and maximum capacity of the plants that can be built at that particular location. Different plants sizes may have different opening costs and fixed operating costs. After a plant is built, it can be further expanded in the following years, up to its maximum capacity.
- Products received by a plant can be either processed immediately or stored for later processing. Plants have a maximum storage capacity (`tonne`). Storage costs (`$/tonne`) can also be specified. * Products received by a plant can be either processed immediately or stored for later processing. Plants have a maximum storage capacity (`tonne`). Storage costs (`$/tonne`) can also be specified.
- All products generated by a plant can either be sent to another plant for further processing, or disposed of locally for either a profit or a loss (`$/tonne`). To model environmental regulations, it is also possible to specify the maximum amount of each product that can be disposed of at each location. * All products generated by a plant can either be sent to another plant for further processing, or disposed of locally for either a profit or a loss (`$/tonne`). To model environmental regulations, it is also possible to specify the maximum amount of each product that can be disposed of at each location.
All user parameters specified above must be provided to RELOG as a JSON file, which is fully described in the [data format page](format.md). All user parameters specified above must be provided to RELOG as a JSON file, which is fully described in the [data format page](format.md).
@@ -89,11 +96,12 @@ To use the `resolve` method, the new input file should be very similar to the or
- **Plant's storage:** Cost. - **Plant's storage:** Cost.
- **Plant's capacity:** Opening cost, fixed operating cost and variable operating cost. - **Plant's capacity:** Opening cost, fixed operating cost and variable operating cost.
## 5. Advanced options ## 5. Advanced options
### 5.1 Changing the solver ### 5.1 Changing the solver
By default, RELOG internally uses [HiGHS](https://github.com/ERGO-Code/HiGHS), an open-source and freely-available Mixed-Integer Linear Programming solver. For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch to Gurobi, for example: By default, RELOG internally uses [Cbc](https://github.com/coin-or/Cbc), an open-source and freely-available Mixed-Integer Linear Programming solver developed by the [COIN-OR Project](https://www.coin-or.org/). For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch from Cbc to Gurobi, for example:
```julia ```julia
using RELOG, Gurobi, JuMP using RELOG, Gurobi, JuMP

75
juliaw Executable file
View File

@@ -0,0 +1,75 @@
#!/bin/bash
# UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
if [ ! -e Project.toml ]; then
echo "juliaw: Project.toml not found"
exit 1
fi
if [ ! -e Manifest.toml ]; then
julia --project=. -e 'using Pkg; Pkg.instantiate()' || exit 1
fi
if [ ! -e build/sysimage.so -o Project.toml -nt build/sysimage.so ]; then
echo "juliaw: rebuilding system image..."
# Generate temporary project folder
rm -rf $HOME/.juliaw
mkdir -p $HOME/.juliaw/src
cp Project.toml Manifest.toml $HOME/.juliaw
NAME=$(julia -e 'using TOML; toml = TOML.parsefile("Project.toml"); "name" in keys(toml) && print(toml["name"])')
if [ ! -z $NAME ]; then
cat > $HOME/.juliaw/src/$NAME.jl << EOF
module $NAME
end
EOF
fi
# Add PackageCompiler dependencies to temporary project
julia --project=$HOME/.juliaw -e 'using Pkg; Pkg.add(["PackageCompiler", "TOML", "Logging"])'
# Generate system image scripts
cat > $HOME/.juliaw/sysimage.jl << EOF
using PackageCompiler
using TOML
using Logging
Logging.disable_logging(Logging.Info)
mkpath("$PWD/build")
println("juliaw: generating precompilation statements...")
run(\`julia --project="$PWD" --trace-compile="$PWD"/build/precompile.jl \$(ARGS)\`)
println("juliaw: finding dependencies...")
project = TOML.parsefile("Project.toml")
manifest = TOML.parsefile("Manifest.toml")
deps = Symbol[]
for dep in keys(project["deps"])
if dep in keys(manifest)
# Up to Julia 1.6
dep_entry = manifest[dep][1]
else
# Julia 1.7+
dep_entry = manifest["deps"][dep][1]
end
if "path" in keys(dep_entry)
println(" - \$(dep) [skip]")
else
println(" - \$(dep)")
push!(deps, Symbol(dep))
end
end
println("juliaw: building system image...")
create_sysimage(
deps,
precompile_statements_file = "$PWD/build/precompile.jl",
sysimage_path = "$PWD/build/sysimage.so",
)
EOF
julia --project=$HOME/.juliaw $HOME/.juliaw/sysimage.jl $*
else
julia --project=. --sysimage build/sysimage.so $*
fi

23
mkdocs.yml Normal file
View File

@@ -0,0 +1,23 @@
site_name: RELOG
theme: cinder
copyright: "Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved."
repo_url: https://github.com/ANL-CEEESA/RELOG
edit_uri: edit/master/src/docs/
nav:
- Home: index.md
- Usage: usage.md
- Data Format: format.md
- Reports: reports.md
- Optimization Model: model.md
plugins:
- search
markdown_extensions:
- admonition
- mdx_math
extra_javascript:
- https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-AMS-MML_HTMLorMML
- js/mathjax.js
docs_dir: src/docs
site_dir: docs
extra_css:
- "css/custom.css"

View File

@@ -1,20 +1,27 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
module RELOG module RELOG
function _round(x::Number)
if abs(x) < 1e-5
return 0
else
return round(x, digits = 5)
end
end
include("instance/structs.jl") include("instance/structs.jl")
include("instance/parse.jl")
include("model/jumpext.jl")
include("model/dist.jl")
include("model/build.jl")
include("reports/plants.jl")
include("reports/transportation.jl")
include("reports/centers.jl")
end # module RELOG include("graph/structs.jl")
include("graph/build.jl")
include("graph/csv.jl")
include("instance/compress.jl")
include("instance/geodb.jl")
include("instance/parse.jl")
include("instance/validate.jl")
include("model/build.jl")
include("model/getsol.jl")
include("model/solve.jl")
include("reports/plant_emissions.jl")
include("reports/plant_outputs.jl")
include("reports/plants.jl")
include("reports/products.jl")
include("reports/tr_emissions.jl")
include("reports/tr.jl")
include("reports/write.jl")
end

103
src/graph/build.jl Normal file
View File

@@ -0,0 +1,103 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Geodesy
function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64
x = LLA(source_lat, source_lon, 0.0)
y = LLA(dest_lat, dest_lon, 0.0)
return round(euclidean_distance(x, y) / 1000.0, digits = 2)
end
function build_graph(instance::Instance)::Graph
arcs = []
next_index = 0
process_nodes = ProcessNode[]
plant_shipping_nodes = ShippingNode[]
collection_shipping_nodes = ShippingNode[]
name_to_process_node_map = Dict{Tuple{AbstractString,AbstractString},ProcessNode}()
collection_center_to_node = Dict()
process_nodes_by_input_product =
Dict(product => ProcessNode[] for product in instance.products)
shipping_nodes_by_plant = Dict(plant => [] for plant in instance.plants)
# Build collection center shipping nodes
for center in instance.collection_centers
node = ShippingNode(next_index, center, center.product, [], [])
next_index += 1
collection_center_to_node[center] = node
push!(collection_shipping_nodes, node)
end
# Build process and shipping nodes for plants
for plant in instance.plants
pn = ProcessNode(next_index, plant, [], [])
next_index += 1
push!(process_nodes, pn)
push!(process_nodes_by_input_product[plant.input], pn)
name_to_process_node_map[(plant.plant_name, plant.location_name)] = pn
for product in keys(plant.output)
sn = ShippingNode(next_index, plant, product, [], [])
next_index += 1
push!(plant_shipping_nodes, sn)
push!(shipping_nodes_by_plant[plant], sn)
end
end
# Build arcs from collection centers to plants, and from one plant to another
for source in [collection_shipping_nodes; plant_shipping_nodes]
for dest in process_nodes_by_input_product[source.product]
distance = calculate_distance(
source.location.latitude,
source.location.longitude,
dest.location.latitude,
dest.location.longitude,
)
values = Dict("distance" => distance)
arc = Arc(length(arcs) + 1, source, dest, values)
push!(source.outgoing_arcs, arc)
push!(dest.incoming_arcs, arc)
push!(arcs, arc)
end
end
# Build arcs from process nodes to shipping nodes within a plant
for source in process_nodes
plant = source.location
for dest in shipping_nodes_by_plant[plant]
weight = plant.output[dest.product]
values = Dict("weight" => weight)
arc = Arc(length(arcs) + 1, source, dest, values)
push!(source.outgoing_arcs, arc)
push!(dest.incoming_arcs, arc)
push!(arcs, arc)
end
end
return Graph(
process_nodes,
plant_shipping_nodes,
collection_shipping_nodes,
arcs,
name_to_process_node_map,
collection_center_to_node,
)
end
function print_graph_stats(instance::Instance, graph::Graph)::Nothing
@info @sprintf(" %12d time periods", instance.time)
@info @sprintf(" %12d process nodes", length(graph.process_nodes))
@info @sprintf(" %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
@info @sprintf(
" %12d shipping nodes (collection)",
length(graph.collection_shipping_nodes)
)
@info @sprintf(" %12d arcs", length(graph.arcs))
return
end

11
src/graph/csv.jl Normal file
View File

@@ -0,0 +1,11 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
function to_csv(graph::Graph)
result = ""
for a in graph.arcs
result *= "$(a.source.index),$(a.dest.index)\n"
end
return result
end

46
src/graph/structs.jl Normal file
View File

@@ -0,0 +1,46 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Geodesy
abstract type Node end
mutable struct Arc
index::Int
source::Node
dest::Node
values::Dict{String,Float64}
end
mutable struct ProcessNode <: Node
index::Int
location::Plant
incoming_arcs::Vector{Arc}
outgoing_arcs::Vector{Arc}
end
mutable struct ShippingNode <: Node
index::Int
location::Union{Plant,CollectionCenter}
product::Product
incoming_arcs::Vector{Arc}
outgoing_arcs::Vector{Arc}
end
mutable struct Graph
process_nodes::Vector{ProcessNode}
plant_shipping_nodes::Vector{ShippingNode}
collection_shipping_nodes::Vector{ShippingNode}
arcs::Vector{Arc}
name_to_process_node_map::Dict{Tuple{AbstractString,AbstractString},ProcessNode}
collection_center_to_node::Dict{CollectionCenter,ShippingNode}
end
function Base.show(io::IO, instance::Graph)
print(io, "RELOG graph with ")
print(io, "$(length(instance.process_nodes)) process nodes, ")
print(io, "$(length(instance.plant_shipping_nodes)) plant shipping nodes, ")
print(io, "$(length(instance.collection_shipping_nodes)) collection shipping nodes, ")
print(io, "$(length(instance.arcs)) arcs")
end

101
src/instance/compress.jl Normal file
View File

@@ -0,0 +1,101 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataStructures
using JSON
using JSONSchema
using Printf
using Statistics
"""
_compress(instance::Instance)
Create a single-period instance from a multi-period one. Specifically,
replaces every time-dependent attribute, such as initial_amounts,
by a list with a single element, which is either a sum, an average,
or something else that makes sense to that specific attribute.
"""
function _compress(instance::Instance)::Instance
T = instance.time
compressed = deepcopy(instance)
compressed.time = 1
compressed.building_period = [1]
# Compress products
for p in compressed.products
p.transportation_cost = [mean(p.transportation_cost)]
p.transportation_energy = [mean(p.transportation_energy)]
for (emission_name, emission_value) in p.transportation_emissions
p.transportation_emissions[emission_name] = [mean(emission_value)]
end
p.disposal_limit = [maximum(p.disposal_limit) * T]
p.disposal_cost = [mean(p.disposal_cost)]
end
# Compress collection centers
for c in compressed.collection_centers
c.amount = [maximum(c.amount) * T]
end
# Compress plants
for plant in compressed.plants
plant.energy = [mean(plant.energy)]
for (emission_name, emission_value) in plant.emissions
plant.emissions[emission_name] = [mean(emission_value)]
end
for s in plant.sizes
s.capacity *= T
s.variable_operating_cost = [mean(s.variable_operating_cost)]
s.opening_cost = [s.opening_cost[1]]
s.fixed_operating_cost = [sum(s.fixed_operating_cost)]
end
for (prod_name, disp_limit) in plant.disposal_limit
plant.disposal_limit[prod_name] = [sum(disp_limit)]
end
for (prod_name, disp_cost) in plant.disposal_cost
plant.disposal_cost[prod_name] = [mean(disp_cost)]
end
end
return compressed
end
function _slice(instance::Instance, T::UnitRange)::Instance
sliced = deepcopy(instance)
sliced.time = length(T)
for p in sliced.products
p.transportation_cost = p.transportation_cost[T]
p.transportation_energy = p.transportation_energy[T]
for (emission_name, emission_value) in p.transportation_emissions
p.transportation_emissions[emission_name] = emission_value[T]
end
p.disposal_limit = p.disposal_limit[T]
p.disposal_cost = p.disposal_cost[T]
end
for c in sliced.collection_centers
c.amount = c.amount[T]
end
for plant in sliced.plants
plant.energy = plant.energy[T]
for (emission_name, emission_value) in plant.emissions
plant.emissions[emission_name] = emission_value[T]
end
for s in plant.sizes
s.variable_operating_cost = s.variable_operating_cost[T]
s.opening_cost = s.opening_cost[T]
s.fixed_operating_cost = s.fixed_operating_cost[T]
end
for (prod_name, disp_limit) in plant.disposal_limit
plant.disposal_limit[prod_name] = disp_limit[T]
end
for (prod_name, disp_cost) in plant.disposal_cost
plant.disposal_cost[prod_name] = disp_cost[T]
end
end
return sliced
end

212
src/instance/geodb.jl Normal file
View File

@@ -0,0 +1,212 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using CRC
using CSV
using DataFrames
using Shapefile
using Statistics
using ZipFile
using ProgressBars
using OrderedCollections
import Downloads: download
import Base: parse
crc32 = crc(CRC_32)
struct GeoPoint
lat::Float64
lon::Float64
end
struct GeoRegion
centroid::GeoPoint
population::Int
GeoRegion(; centroid, population) = new(centroid, population)
end
DB_CACHE = Dict{String,Dict{String,GeoRegion}}()
function centroid(geom::Shapefile.Polygon)::GeoPoint
x_max, x_min, y_max, y_min = -Inf, Inf, -Inf, Inf
for p in geom.points
x_max = max(x_max, p.x)
x_min = min(x_min, p.x)
y_max = max(y_max, p.y)
y_min = min(y_min, p.y)
end
x_center = (x_max + x_min) / 2.0
y_center = (y_max + y_min) / 2.0
return GeoPoint(round(y_center, digits = 5), round(x_center, digits = 5))
end
function _download_file(url, output, expected_crc32)::Nothing
if isfile(output)
return
end
mkpath(dirname(output))
@info "Downloading: $url"
fname = download(url)
actual_crc32 = open(crc32, fname)
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
cp(fname, output)
return
end
function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
if isfile(expected_output_file)
return
end
mkpath(outputdir)
@info "Downloading: $url"
zip_filename = download(url)
actual_crc32 = open(crc32, zip_filename)
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
open(zip_filename) do zip_file
zr = ZipFile.Reader(zip_file)
for file in zr.files
open(joinpath(outputdir, file.name), "w") do output_file
write(output_file, read(file))
end
end
end
return
end
function _geodb_load_gov_census(;
db_name,
extract_cols,
shp_crc32,
shp_filename,
shp_url,
population_url,
population_crc32,
population_col,
population_preprocess,
population_join,
)::Dict{String,GeoRegion}
basedir = joinpath(dirname(@__FILE__), "..", "..", "data", db_name)
csv_filename = "$basedir/locations.csv"
if !isfile(csv_filename)
# Download required files
_download_zip(shp_url, basedir, joinpath(basedir, shp_filename), shp_crc32)
_download_file(population_url, "$basedir/population.csv", population_crc32)
# Read shapefile
@info "Processing: $shp_filename"
table = Shapefile.Table(joinpath(basedir, shp_filename))
geoms = Shapefile.shapes(table)
# Build empty dataframe
df = DataFrame()
cols = extract_cols(table, 1)
for k in keys(cols)
df[!, k] = []
end
df[!, "latitude"] = Float64[]
df[!, "longitude"] = Float64[]
# Add regions to dataframe
for (i, geom) in tqdm(enumerate(geoms))
c = centroid(geom)
cols = extract_cols(table, i)
push!(df, [values(cols)..., c.lat, c.lon])
end
sort!(df)
# Join with population data
population = DataFrame(CSV.File("$basedir/population.csv"))
population_preprocess(population)
population = population[:, [population_join, population_col]]
rename!(population, population_col => "population")
df = leftjoin(df, population, on = population_join)
# Write output
CSV.write(csv_filename, df)
end
if db_name keys(DB_CACHE)
csv = CSV.File(csv_filename)
DB_CACHE[db_name] = Dict(
string(row.id) => GeoRegion(
centroid = GeoPoint(row.latitude, row.longitude),
population = (row.population === missing ? 0 : row.population),
) for row in csv
)
end
return DB_CACHE[db_name]
end
# 2018 US counties
# -----------------------------------------------------------------------------
function _extract_cols_2018_us_county(
table::Shapefile.Table,
i::Int,
)::OrderedDict{String,Any}
return OrderedDict(
"id" => table.STATEFP[i] * table.COUNTYFP[i],
"statefp" => table.STATEFP[i],
"countyfp" => table.COUNTYFP[i],
"name" => table.NAME[i],
)
end
function _population_preprocess_2018_us_county(df)
df[!, "id"] = [@sprintf("%02d%03d", row.STATE, row.COUNTY) for row in eachrow(df)]
end
function _geodb_load_2018_us_county()::Dict{String,GeoRegion}
return _geodb_load_gov_census(
db_name = "2018-us-county",
extract_cols = _extract_cols_2018_us_county,
shp_crc32 = 0x83eaec6d,
shp_filename = "cb_2018_us_county_500k.shp",
shp_url = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip",
population_url = "https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.csv",
population_crc32 = 0xf85b0405,
population_col = "POPESTIMATE2019",
population_join = "id",
population_preprocess = _population_preprocess_2018_us_county,
)
end
# US States
# -----------------------------------------------------------------------------
function _extract_cols_us_state(table::Shapefile.Table, i::Int)::OrderedDict{String,Any}
return OrderedDict(
"id" => table.STUSPS[i],
"statefp" => parse(Int, table.STATEFP[i]),
"name" => table.NAME[i],
)
end
function _population_preprocess_us_state(df)
rename!(df, "STATE" => "statefp")
end
function _geodb_load_us_state()::Dict{String,GeoRegion}
return _geodb_load_gov_census(
db_name = "us-state",
extract_cols = _extract_cols_us_state,
shp_crc32 = 0x9469e5ca,
shp_filename = "cb_2018_us_state_500k.shp",
shp_url = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip",
population_url = "http://www2.census.gov/programs-surveys/popest/datasets/2010-2019/national/totals/nst-est2019-alldata.csv",
population_crc32 = 0x191cc64c,
population_col = "POPESTIMATE2019",
population_join = "statefp",
population_preprocess = _population_preprocess_us_state,
)
end
function geodb_load(db_name::AbstractString)::Dict{String,GeoRegion}
db_name == "2018-us-county" && return _geodb_load_2018_us_county()
db_name == "us-state" && return _geodb_load_us_state()
error("Unknown database: $db_name")
end
function geodb_query(name)::GeoRegion
db_name, id = split(name, ":")
return geodb_load(db_name)[id]
end

View File

@@ -1,183 +1,201 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataStructures
using JSON using JSON
using OrderedCollections using JSONSchema
using Printf
using Statistics
function parsefile(path::String)::Instance function parsefile(path::String)::Instance
return RELOG.parse(JSON.parsefile(path, dicttype = () -> OrderedDict())) return RELOG.parse(JSON.parsefile(path))
end end
function parse(json)::Instance function parse(json)::Instance
# Read parameters basedir = dirname(@__FILE__)
time_horizon = json["parameters"]["time horizon (years)"] json_schema = JSON.parsefile("$basedir/../schemas/input.json")
building_period = json["parameters"]["building period (years)"] validate(json, Schema(json_schema))
# Read distance metric T = json["parameters"]["time horizon (years)"]
distance_metric_str = lowercase(json["parameters"]["distance metric"]) json_schema["definitions"]["TimeSeries"]["minItems"] = T
if distance_metric_str == "driving" json_schema["definitions"]["TimeSeries"]["maxItems"] = T
distance_metric = KnnDrivingDistance() validate(json, Schema(json_schema))
elseif distance_metric_str == "euclidean"
distance_metric = EuclideanDistance()
else
error("Invalid distance metric: $distance_metric_str")
end
timeseries(::Nothing; null_val = nothing) = repeat([null_val], time_horizon) building_period = [1]
timeseries(x::Number; null_val = nothing) = repeat([x], time_horizon) if "building period (years)" in keys(json)
timeseries(x::Array; null_val = nothing) = [xi === nothing ? null_val : xi for xi in x] building_period = json["building period (years)"]
timeseries(d::OrderedDict; null_val = nothing) =
OrderedDict(k => timeseries(v; null_val) for (k, v) in d)
# Read products
products = Product[]
products_by_name = OrderedDict{String,Product}()
for (name, pdict) in json["products"]
tr_cost = timeseries(pdict["transportation cost (\$/km/tonne)"])
tr_energy = timeseries(pdict["transportation energy (J/km/tonne)"])
tr_emissions = timeseries(pdict["transportation emissions (tonne/km/tonne)"])
disposal_limit = timeseries(pdict["disposal limit (tonne)"], null_val = Inf)
prod = Product(; name, tr_cost, tr_energy, tr_emissions, disposal_limit)
push!(products, prod)
products_by_name[name] = prod
end
# Read centers
centers = Center[]
centers_by_name = OrderedDict{String,Center}()
for (name, cdict) in json["centers"]
latitude = cdict["latitude (deg)"]
longitude = cdict["longitude (deg)"]
input = nothing
revenue = [0.0 for t = 1:time_horizon]
if cdict["input"] !== nothing
input = products_by_name[cdict["input"]]
revenue = timeseries(cdict["revenue (\$/tonne)"])
end
outputs = [products_by_name[p] for p in cdict["outputs"]]
operating_cost = timeseries(cdict["operating cost (\$)"])
prod_dict(key, null_val) =
OrderedDict(p => timeseries(cdict[key][p.name]; null_val) for p in outputs)
fixed_output = prod_dict("fixed output (tonne)", 0.0)
var_output = prod_dict("variable output (tonne/tonne)", 0.0)
collection_cost = prod_dict("collection cost (\$/tonne)", 0.0)
disposal_limit = prod_dict("disposal limit (tonne)", Inf)
disposal_cost = prod_dict("disposal cost (\$/tonne)", 0.0)
center = Center(;
name,
latitude,
longitude,
input,
outputs,
revenue,
operating_cost,
fixed_output,
var_output,
collection_cost,
disposal_cost,
disposal_limit,
)
push!(centers, center)
centers_by_name[name] = center
end end
plants = Plant[] plants = Plant[]
plants_by_name = OrderedDict{String,Plant}() products = Product[]
for (name, pdict) in json["plants"] collection_centers = CollectionCenter[]
prod_dict(key; scale = 1.0, null_val = Inf) = OrderedDict{Product,Vector{Float64}}( prod_name_to_product = Dict{String,Product}()
products_by_name[p] => [
v === nothing ? null_val : v * scale for v in timeseries(pdict[key][p])
] for p in keys(pdict[key])
)
latitude = pdict["latitude (deg)"] # Create products
longitude = pdict["longitude (deg)"] for (product_name, product_dict) in json["products"]
input_mix = prod_dict("input mix (%)", scale = 0.01) cost = product_dict["transportation cost (\$/km/tonne)"]
output = prod_dict("output (tonne)") energy = zeros(T)
emissions = timeseries(pdict["processing emissions (tonne)"]) emissions = Dict()
storage_cost = prod_dict("storage cost (\$/tonne)") disposal_limit = zeros(T)
storage_limit = prod_dict("storage limit (tonne)") disposal_cost = zeros(T)
disposal_cost = prod_dict("disposal cost (\$/tonne)")
disposal_limit = prod_dict("disposal limit (tonne)") if "transportation energy (J/km/tonne)" in keys(product_dict)
initial_capacity = pdict["initial capacity (tonne)"] energy = product_dict["transportation energy (J/km/tonne)"]
capacities = PlantCapacity[]
for cdict in pdict["capacities"]
size = cdict["size (tonne)"]
opening_cost = timeseries(cdict["opening cost (\$)"])
fix_operating_cost = timeseries(cdict["fixed operating cost (\$)"])
var_operating_cost = timeseries(cdict["variable operating cost (\$/tonne)"])
push!(
capacities,
PlantCapacity(; size, opening_cost, fix_operating_cost, var_operating_cost),
)
end end
# Validate capacity count and duplicate if needed if "transportation emissions (tonne/km/tonne)" in keys(product_dict)
if length(capacities) == 0 emissions = product_dict["transportation emissions (tonne/km/tonne)"]
error("Plant '$name' must have at least one capacity defined")
elseif length(capacities) == 1
# Duplicate the single capacity
push!(capacities, capacities[1])
elseif length(capacities) > 2
error(
"Plant '$name' cannot have more than 2 capacities, got $(length(capacities))",
)
end end
# Validate capacity sizes are non-decreasing if "disposal limit (tonne)" in keys(product_dict)
if capacities[1].size > capacities[2].size disposal_limit = product_dict["disposal limit (tonne)"]
error(
"Plant '$name' capacity sizes must be non-decreasing: $(capacities[1].size) > $(capacities[2].size)",
)
end end
# Validate variable operating costs are the same if "disposal cost (\$/tonne)" in keys(product_dict)
if capacities[1].var_operating_cost != capacities[2].var_operating_cost disposal_cost = product_dict["disposal cost (\$/tonne)"]
error(
"Plant '$name' variable operating costs must be the same across all capacities",
)
end end
plant = Plant(; prod_centers = []
name,
latitude, product = Product(
longitude, product_name,
input_mix, cost,
output, energy,
emissions, emissions,
storage_cost,
storage_limit,
disposal_cost,
disposal_limit, disposal_limit,
capacities, disposal_cost,
initial_capacity, prod_centers,
) )
push!(plants, plant) push!(products, product)
plants_by_name[name] = plant prod_name_to_product[product_name] = product
end
# Read emissions # Create collection centers
emissions = Emissions[] if "initial amounts" in keys(product_dict)
emissions_by_name = OrderedDict{String,Emissions}() for (center_name, center_dict) in product_dict["initial amounts"]
if haskey(json, "emissions") if "location" in keys(center_dict)
for (name, edict) in json["emissions"] region = geodb_query(center_dict["location"])
limit = timeseries(edict["limit (tonne)"], null_val = Inf) center_dict["latitude (deg)"] = region.centroid.lat
penalty = timeseries(edict["penalty (\$/tonne)"]) center_dict["longitude (deg)"] = region.centroid.lon
emission = Emissions(; name, limit, penalty) end
push!(emissions, emission) center = CollectionCenter(
emissions_by_name[name] = emission length(collection_centers) + 1,
center_name,
center_dict["latitude (deg)"],
center_dict["longitude (deg)"],
product,
center_dict["amount (tonne)"],
)
push!(prod_centers, center)
push!(collection_centers, center)
end
end end
end end
return Instance(; # Create plants
time_horizon, for (plant_name, plant_dict) in json["plants"]
building_period, input = prod_name_to_product[plant_dict["input"]]
distance_metric, output = Dict()
products,
products_by_name, # Plant outputs
centers, if "outputs (tonne/tonne)" in keys(plant_dict)
centers_by_name, output = Dict(
plants, prod_name_to_product[key] => value for
plants_by_name, (key, value) in plant_dict["outputs (tonne/tonne)"] if value > 0
)
end
energy = zeros(T)
emissions = Dict()
if "energy (GJ/tonne)" in keys(plant_dict)
energy = plant_dict["energy (GJ/tonne)"]
end
if "emissions (tonne/tonne)" in keys(plant_dict)
emissions = plant_dict["emissions (tonne/tonne)"]
end
for (location_name, location_dict) in plant_dict["locations"]
sizes = PlantSize[]
disposal_limit = Dict(p => [0.0 for t = 1:T] for p in keys(output))
disposal_cost = Dict(p => [0.0 for t = 1:T] for p in keys(output))
# GeoDB
if "location" in keys(location_dict)
region = geodb_query(location_dict["location"])
location_dict["latitude (deg)"] = region.centroid.lat
location_dict["longitude (deg)"] = region.centroid.lon
end
# Disposal
if "disposal" in keys(location_dict)
for (product_name, disposal_dict) in location_dict["disposal"]
limit = [1e8 for t = 1:T]
if "limit (tonne)" in keys(disposal_dict)
limit = disposal_dict["limit (tonne)"]
end
disposal_limit[prod_name_to_product[product_name]] = limit
disposal_cost[prod_name_to_product[product_name]] =
disposal_dict["cost (\$/tonne)"]
end
end
# Capacities
for (capacity_name, capacity_dict) in location_dict["capacities (tonne)"]
push!(
sizes,
PlantSize(
Base.parse(Float64, capacity_name),
capacity_dict["variable operating cost (\$/tonne)"],
capacity_dict["fixed operating cost (\$)"],
capacity_dict["opening cost (\$)"],
),
)
end
length(sizes) > 1 || push!(sizes, sizes[1])
sort!(sizes, by = x -> x.capacity)
# Storage
storage_limit = 0
storage_cost = zeros(T)
if "storage" in keys(location_dict)
storage_dict = location_dict["storage"]
storage_limit = storage_dict["limit (tonne)"]
storage_cost = storage_dict["cost (\$/tonne)"]
end
# Validation: Capacities
if length(sizes) != 2
throw("At most two capacities are supported")
end
if sizes[1].variable_operating_cost != sizes[2].variable_operating_cost
throw("Variable operating costs must be the same for all capacities")
end
plant = Plant(
length(plants) + 1,
plant_name,
location_name,
input,
output,
location_dict["latitude (deg)"],
location_dict["longitude (deg)"],
disposal_limit,
disposal_cost,
sizes,
energy,
emissions, emissions,
emissions_by_name, storage_limit,
storage_cost,
) )
push!(plants, plant)
end
end
@info @sprintf("%12d collection centers", length(collection_centers))
@info @sprintf("%12d candidate plant locations", length(plants))
return Instance(T, products, collection_centers, plants, building_period)
end end

View File

@@ -1,75 +1,60 @@
using OrderedCollections # RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
abstract type DistanceMetric end using DataStructures
using JSON
using JSONSchema
using Printf
using Statistics
Base.@kwdef mutable struct KnnDrivingDistance <: DistanceMetric mutable struct Product
tree = nothing
ratios = nothing
end
mutable struct EuclideanDistance <: DistanceMetric end
Base.@kwdef struct Product
name::String name::String
tr_cost::Vector{Float64} transportation_cost::Vector{Float64}
tr_energy::Vector{Float64} transportation_energy::Vector{Float64}
tr_emissions::OrderedDict{String,Vector{Float64}} transportation_emissions::Dict{String,Vector{Float64}}
disposal_limit::Vector{Float64} disposal_limit::Vector{Float64}
disposal_cost::Vector{Float64}
collection_centers::Vector
end end
Base.@kwdef struct Center mutable struct CollectionCenter
index::Int64
name::String name::String
latitude::Float64 latitude::Float64
longitude::Float64 longitude::Float64
input::Union{Product,Nothing} product::Product
outputs::Vector{Product} amount::Vector{Float64}
fixed_output::OrderedDict{Product,Vector{Float64}}
var_output::OrderedDict{Product,Vector{Float64}}
revenue::Vector{Float64}
collection_cost::OrderedDict{Product,Vector{Float64}}
operating_cost::Vector{Float64}
disposal_limit::OrderedDict{Product,Vector{Float64}}
disposal_cost::OrderedDict{Product,Vector{Float64}}
end end
Base.@kwdef struct PlantCapacity mutable struct PlantSize
size::Float64 capacity::Float64
variable_operating_cost::Vector{Float64}
fixed_operating_cost::Vector{Float64}
opening_cost::Vector{Float64} opening_cost::Vector{Float64}
fix_operating_cost::Vector{Float64}
var_operating_cost::Vector{Float64}
end end
Base.@kwdef struct Plant mutable struct Plant
name::String index::Int64
plant_name::String
location_name::String
input::Product
output::Dict{Product,Float64}
latitude::Float64 latitude::Float64
longitude::Float64 longitude::Float64
input_mix::OrderedDict{Product,Vector{Float64}} disposal_limit::Dict{Product,Vector{Float64}}
output::OrderedDict{Product,Vector{Float64}} disposal_cost::Dict{Product,Vector{Float64}}
emissions::OrderedDict{String,Vector{Float64}} sizes::Vector{PlantSize}
storage_cost::OrderedDict{Product,Vector{Float64}} energy::Vector{Float64}
storage_limit::OrderedDict{Product,Vector{Float64}} emissions::Dict{String,Vector{Float64}}
disposal_cost::OrderedDict{Product,Vector{Float64}} storage_limit::Float64
disposal_limit::OrderedDict{Product,Vector{Float64}} storage_cost::Vector{Float64}
capacities::Vector{PlantCapacity}
initial_capacity::Float64
end end
Base.@kwdef struct Emissions mutable struct Instance
name::String time::Int64
limit::Vector{Float64}
penalty::Vector{Float64}
end
Base.@kwdef struct Instance
building_period::Vector{Int}
centers_by_name::OrderedDict{String,Center}
centers::Vector{Center}
distance_metric::DistanceMetric
products_by_name::OrderedDict{String,Product}
products::Vector{Product} products::Vector{Product}
time_horizon::Int collection_centers::Vector{CollectionCenter}
plants::Vector{Plant} plants::Vector{Plant}
plants_by_name::OrderedDict{String,Plant} building_period::Vector{Int64}
emissions_by_name::OrderedDict{String,Emissions}
emissions::Vector{Emissions}
end end

21
src/instance/validate.jl Normal file
View File

@@ -0,0 +1,21 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataStructures
using JSON
using JSONSchema
using Printf
using Statistics
function validate(json, schema)
result = JSONSchema.validate(json, schema)
if result !== nothing
if result isa JSONSchema.SingleIssue
msg = "$(result.reason) in $(result.path)"
else
msg = convert(String, result)
end
throw("Error parsing input file: $(msg)")
end
end

View File

@@ -2,482 +2,363 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
using JuMP using JuMP, LinearAlgebra, Geodesy, ProgressBars, Printf, DataStructures, StochasticPrograms
R_expand(p::Plant, t::Int) = function build_model(
(p.capacities[2].opening_cost[t] - p.capacities[1].opening_cost[t]) / instance::Instance,
(p.capacities[2].size - p.capacities[1].size) graph::Graph,
optimizer,
R_fix_exp(p::Plant, t::Int) = )
(p.capacities[2].fix_operating_cost[t] - p.capacities[1].fix_operating_cost[t]) / return build_model(
(p.capacities[2].size - p.capacities[1].size) instance,
[graph],
function build_model(instance::Instance; optimizer, variable_names::Bool = false) [1.0],
model = JuMP.Model(optimizer) optimizer=optimizer,
centers = instance.centers method=:ef,
products = instance.products
plants = instance.plants
T = 1:instance.time_horizon
model.ext[:instance] = instance
# Constants
# -------------------------------------------------------------------------
K_cap_min = Dict(p => p.capacities[1].size for p in plants)
K_cap_max = Dict(p => p.capacities[2].size for p in plants)
R_open = Dict((p, t) => p.capacities[1].opening_cost[t] for p in plants for t in T)
R_fix_min =
Dict((p, t) => p.capacities[1].fix_operating_cost[t] for p in plants for t in T)
# Transportation edges
# -------------------------------------------------------------------------
# Connectivity
model.ext[:E] = E = []
model.ext[:E_in] = E_in = Dict(src => [] for src in plants centers)
model.ext[:E_out] = E_out = Dict(src => [] for src in plants centers)
function push_edge!(src, dst, m)
push!(E, (src, dst, m))
push!(E_out[src], (dst, m))
push!(E_in[dst], (src, m))
end
for m in products
for p1 in plants
m keys(p1.output) || continue
# Plant to plant
for p2 in plants
p1 != p2 || continue
m keys(p2.input_mix) || continue
push_edge!(p1, p2, m)
end
# Plant to center
for c in centers
m == c.input || continue
push_edge!(p1, c, m)
end
end
for c1 in centers
m c1.outputs || continue
# Center to plant
for p in plants
m keys(p.input_mix) || continue
push_edge!(c1, p, m)
end
# Center to center
for c2 in centers
m == c2.input || continue
push_edge!(c1, c2, m)
end
end
end
# Distances
model.ext[:distances] = distances = Dict()
for (p1, p2, m) in E
d = _calculate_distance(
p1.latitude,
p1.longitude,
p2.latitude,
p2.longitude,
instance.distance_metric,
) )
distances[p1, p2, m] = d
end end
# Decision variables function build_model(
# ------------------------------------------------------------------------- instance::Instance,
graphs::Vector{Graph},
probs::Vector{Float64};
optimizer,
method=:ef,
tol=0.1,
)
T = instance.time
# Plant p is operational at time t @stochastic_model model begin
x = _init(model, :x) # Stage 1: Build plants
for p in plants # =====================================================================
x[p.name, 0] = p.initial_capacity > 0 ? 1 : 0 @stage 1 begin
end pn = graphs[1].process_nodes
for p in plants, t in T PN = length(pn)
x[p.name, t] = @variable(model, binary = true)
end
# Amount of product m sent from center/plant u to center/plant v at time T # Var: open_plant
y = _init(model, :y) @decision(
for (p1, p2, m) in E, t in T model,
y[p1.name, p2.name, m.name, t] = @variable(model, lower_bound = 0) open_plant[n in 1:PN, t in 1:T],
end binary = true,
)
# Amount of product m produced by plant/center at time T
z_prod = _init(model, :z_prod)
for p in plants, m in keys(p.output), t in T
z_prod[p.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Amount of product m disposed at plant/center p at time T
z_disp = _init(model, :z_disp)
for p in plants, m in keys(p.output), t in T
z_disp[p.name, m.name, t] = @variable(model, lower_bound = 0)
end
for c in centers, m in c.outputs, t in T
z_disp[c.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Total plant/center input
z_input = _init(model, :z_input)
for p in plants, t in T
z_input[p.name, t] = @variable(model, lower_bound = 0)
end
for c in centers, t in T
z_input[c.name, t] = @variable(model, lower_bound = 0)
end
# Plant expansion
z_exp = _init(model, :z_exp)
for p in plants
z_exp[p.name, 0] = max(0, p.initial_capacity - K_cap_min[p])
end
for p in plants, t in T
z_exp[p.name, t] = @variable(model, lower_bound = 0)
end
# Total amount collected by the center
z_collected = _init(model, :z_collected)
for c in centers, m in c.outputs, t in T
z_collected[c.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Amount of input material stored at plant at end of time period
z_storage = _init(model, :z_storage)
for p in plants
for m in keys(p.input_mix)
z_storage[p.name, m.name, 0] = 0 # Initial storage is zero
end
end
for p in plants, m in keys(p.input_mix), t in T
z_storage[p.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Total amount of input material processed by plant
z_process = _init(model, :z_process)
for p in plants, t in T
z_process[p.name, t] = @variable(model, lower_bound = 0)
end
# Transportation emissions by greenhouse gas
z_em_tr = _init(model, :z_em_tr)
for (p1, p2, m) in E, t in T, g in keys(m.tr_emissions)
z_em_tr[g, p1.name, p2.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Plant emissions by greenhouse gas
z_em_plant = _init(model, :z_em_plant)
for p in plants, t in T, g in keys(p.emissions)
z_em_plant[g, p.name, t] = @variable(model, lower_bound = 0)
end
# Var: is_open
@decision(
model,
is_open[n in 1:PN, t in 1:T],
binary = true,
)
# Objective function # Objective function
# ------------------------------------------------------------------------- @objective(
obj = AffExpr()
# Transportation cost
for (p1, p2, m) in E, t in T
add_to_expression!(
obj,
distances[p1, p2, m] * m.tr_cost[t],
y[p1.name, p2.name, m.name, t],
)
end
# Center: Revenue
for c in centers, (p, m) in E_in[c], t in T
add_to_expression!(obj, -c.revenue[t], y[p.name, c.name, m.name, t])
end
# Center: Collection cost
for c in centers, (p, m) in E_out[c], t in T
add_to_expression!(obj, c.collection_cost[m][t], y[c.name, p.name, m.name, t])
end
# Center: Disposal cost
for c in centers, m in c.outputs, t in T
add_to_expression!(obj, c.disposal_cost[m][t], z_disp[c.name, m.name, t])
end
# Center: Operating cost
for c in centers, t in T
add_to_expression!(obj, c.operating_cost[t])
end
# Plants: Disposal cost
for p in plants, m in keys(p.output), t in T
add_to_expression!(obj, p.disposal_cost[m][t], z_disp[p.name, m.name, t])
end
# Plants: Opening cost
for p in plants, t in T
add_to_expression!(obj, R_open[p, t], (x[p.name, t] - x[p.name, t-1]))
end
# Plants: Fixed operating cost
for p in plants, t in T
add_to_expression!(obj, R_fix_min[p, t], x[p.name, t])
add_to_expression!(obj, R_fix_exp(p, t), z_exp[p.name, t])
end
# Plants: Expansion cost
for p in plants, t in T
add_to_expression!(obj, R_expand(p, t), z_exp[p.name, t] - z_exp[p.name, t-1])
end
# Plants: Variable operating cost
for p in plants, (src, m) in E_in[p], t in T
add_to_expression!(
obj,
p.capacities[1].var_operating_cost[t],
y[src.name, p.name, m.name, t],
)
end
# Plants: Storage cost
for p in plants, m in keys(p.storage_cost), t in T
add_to_expression!(obj, p.storage_cost[m][t], z_storage[p.name, m.name, t])
end
# Emissions penalty cost
for emission in instance.emissions, t in T
# Plant emissions penalty
for p in plants
if emission.name in keys(p.emissions)
add_to_expression!(
obj,
emission.penalty[t],
z_em_plant[emission.name, p.name, t],
)
end
end
# Transportation emissions penalty
for (p1, p2, m) in E
if emission.name in keys(m.tr_emissions)
add_to_expression!(
obj,
emission.penalty[t],
z_em_tr[emission.name, p1.name, p2.name, m.name, t],
)
end
end
end
@objective(model, Min, obj)
# Constraints
# -------------------------------------------------------------------------
# Plants: Definition of total plant input
eq_z_input = _init(model, :eq_z_input)
for p in plants, t in T
eq_z_input[p.name, t] = @constraint(
model, model,
z_input[p.name, t] == Min,
sum(y[src.name, p.name, m.name, t] for (src, m) in E_in[p])
)
end
# Plants: Definition of total processing amount # Opening, fixed operating costs
eq_z_process = _init(model, :eq_z_process) sum(
for p in plants, t in T pn[n].location.sizes[1].opening_cost[t] * open_plant[n, t] +
eq_z_process[p.name, t] = @constraint( pn[n].location.sizes[1].fixed_operating_cost[t] * is_open[n, t]
for n in 1:PN
for t in 1:T
),
)
for t = 1:T, n in 1:PN
# Plant is currently open if it was already open in the previous time period or
# if it was built just now
if t > 1
@constraint(
model, model,
z_process[p.name, t] == is_open[n, t] == is_open[n, t-1] + open_plant[n, t]
z_input[p.name, t] + sum(
z_storage[p.name, m.name, t-1] - z_storage[p.name, m.name, t] for
m in keys(p.input_mix)
)
) )
else
@constraint(model, is_open[n, t] == open_plant[n, t])
end end
# Plants: Processing mix must have correct proportion # Plant can only be opened during building period
eq_process_mix = _init(model, :eq_process_mix)
for p in plants, m in keys(p.input_mix), t in T
eq_process_mix[p.name, m.name, t] = @constraint(
model,
sum(y[src.name, p.name, m.name, t] for (src, m2) in E_in[p] if m == m2) +
z_storage[p.name, m.name, t-1] - z_storage[p.name, m.name, t] ==
z_process[p.name, t] * p.input_mix[m][t]
)
end
# Plants: Calculate amount produced
eq_z_prod = _init(model, :eq_z_prod)
for p in plants, m in keys(p.output), t in T
eq_z_prod[p.name, m.name, t] = @constraint(
model,
z_prod[p.name, m.name, t] == z_process[p.name, t] * p.output[m][t]
)
end
# Plants: Produced material must be sent or disposed
eq_balance = _init(model, :eq_balance)
for p in plants, m in keys(p.output), t in T
eq_balance[p.name, m.name, t] = @constraint(
model,
z_prod[p.name, m.name, t] ==
sum(y[p.name, dst.name, m.name, t] for (dst, m2) in E_out[p] if m == m2) +
z_disp[p.name, m.name, t]
)
end
# Plants: Expansion upper bound
eq_exp_ub = _init(model, :eq_exp_ub)
for p in plants, t in T
eq_exp_ub[p.name, t] = @constraint(
model,
z_exp[p.name, t] <= (K_cap_max[p] - K_cap_min[p]) * x[p.name, t]
)
end
# Plants: Processing limit
eq_process_limit = _init(model, :eq_process_limit)
for p in plants, t in T
eq_process_limit[p.name, t] = @constraint(
model,
z_process[p.name, t] <= K_cap_min[p] * x[p.name, t] + z_exp[p.name, t]
)
end
# Plants: Disposal limit
eq_disposal_limit = _init(model, :eq_disposal_limit)
for p in plants, m in keys(p.output), t in T
isfinite(p.disposal_limit[m][t]) || continue
eq_disposal_limit[p.name, m.name, t] =
@constraint(model, z_disp[p.name, m.name, t] <= p.disposal_limit[m][t])
end
# Plants: Plant remains open
eq_keep_open = _init(model, :eq_keep_open)
for p in plants, t in T
eq_keep_open[p.name, t] = @constraint(model, x[p.name, t] >= x[p.name, t-1])
end
# Plants: Building period
eq_building_period = _init(model, :eq_building_period)
for p in plants, t in T
if t instance.building_period if t instance.building_period
eq_building_period[p.name, t] = @constraint(model, open_plant[n, t] == 0)
@constraint(model, x[p.name, t] - x[p.name, t-1] <= 0) end
end end
end end
# Centers: Definition of total center input # Stage 2: Flows, disposal, capacity & storage
eq_z_input = _init(model, :eq_z_input) # =====================================================================
for c in centers, t in T @stage 2 begin
eq_z_input[c.name, t] = @constraint( @uncertain graph
pn = graph.process_nodes
psn = graph.plant_shipping_nodes
csn = graph.collection_shipping_nodes
arcs = graph.arcs
A = length(arcs)
PN = length(pn)
CSN = length(csn)
PSN = length(psn)
# Var: flow
@recourse(
model, model,
z_input[c.name, t] == flow[a in 1:A, t in 1:T],
sum(y[src.name, c.name, m.name, t] for (src, m) in E_in[c]) lower_bound = 0,
) )
end
# Centers: Calculate amount collected # Var: plant_dispose
eq_z_collected = _init(model, :eq_z_collected) @recourse(
for c in centers, m in c.outputs, t in T
M = length(c.var_output[m])
eq_z_collected[c.name, m.name, t] = @constraint(
model, model,
z_collected[c.name, m.name, t] == plant_dispose[n in 1:PSN, t in 1:T],
lower_bound = 0,
upper_bound = psn[n].location.disposal_limit[psn[n].product][t],
)
# Var: collection_dispose
@recourse(
model,
collection_dispose[n in 1:CSN, t in 1:T],
lower_bound = 0,
upper_bound = graph.collection_shipping_nodes[n].location.amount[t],
)
# Var: collection_shortfall
@recourse(
model,
collection_shortfall[n in 1:CSN, t in 1:T],
lower_bound = 0,
)
# Var: store
@recourse(
model,
store[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = pn[n].location.storage_limit,
)
# Var: process
@recourse(
model,
process[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
)
# Var: capacity
@recourse(
model,
capacity[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = pn[n].location.sizes[2].capacity,
)
# Var: expansion
@recourse(
model,
expansion[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = (
pn[n].location.sizes[2].capacity -
pn[n].location.sizes[1].capacity
),
)
# Objective function
@objective(
model,
Min,
sum( sum(
z_input[c.name, t-offset] * c.var_output[m][offset+1] for # Transportation costs
offset = 0:min(M - 1, t - 1) pn[n].location.input.transportation_cost[t] *
) + c.fixed_output[m][t] a.values["distance"] *
) flow[a.index,t]
end
# Centers: Collected products must be disposed or sent for n in 1:PN
eq_balance = _init(model, :eq_balance) for a in pn[n].incoming_arcs
for c in centers, m in c.outputs, t in T for t in 1:T
eq_balance[c.name, m.name, t] = @constraint(
model,
z_collected[c.name, m.name, t] ==
sum(y[c.name, dst.name, m.name, t] for (dst, m2) in E_out[c] if m == m2) +
z_disp[c.name, m.name, t]
)
end
# Centers: Disposal limit
eq_disposal_limit = _init(model, :eq_disposal_limit)
for c in centers, m in c.outputs, t in T
isfinite(c.disposal_limit[m][t]) || continue
eq_disposal_limit[c.name, m.name, t] =
@constraint(model, z_disp[c.name, m.name, t] <= c.disposal_limit[m][t])
end
# Global disposal limit
eq_disposal_limit = _init(model, :eq_disposal_limit)
for m in products, t in T
isfinite(m.disposal_limit[t]) || continue
eq_disposal_limit[m.name, t] = @constraint(
model,
sum(z_disp[p.name, m.name, t] for p in plants if m in keys(p.output)) +
sum(z_disp[c.name, m.name, t] for c in centers if m in c.outputs) <=
m.disposal_limit[t]
)
end
# Transportation emissions
eq_emission_tr = _init(model, :eq_emission_tr)
for (p1, p2, m) in E, t in T, g in keys(m.tr_emissions)
eq_emission_tr[g, p1.name, p2.name, m.name, t] = @constraint(
model,
z_em_tr[g, p1.name, p2.name, m.name, t] ==
distances[p1, p2, m] * m.tr_emissions[g][t] * y[p1.name, p2.name, m.name, t]
)
end
# Plant emissions
eq_emission_plant = _init(model, :eq_emission_plant)
for p in plants, t in T, g in keys(p.emissions)
eq_emission_plant[g, p.name, t] = @constraint(
model,
z_em_plant[g, p.name, t] == p.emissions[g][t] * z_process[p.name, t]
)
end
# Storage limit at plants
eq_storage_limit = _init(model, :eq_storage_limit)
for p in plants, m in keys(p.storage_limit), t in T
if isfinite(p.storage_limit[m][t])
eq_storage_limit[p.name, m.name, t] =
@constraint(model, z_storage[p.name, m.name, t] <= p.storage_limit[m][t])
end
end
# All stored materials must be processed by end of time horizon
eq_storage_final = _init(model, :eq_storage_final)
for p in plants, m in keys(p.input_mix)
eq_storage_final[p.name, m.name] =
@constraint(model, z_storage[p.name, m.name, instance.time_horizon] == 0)
end
# Global emissions limit
eq_emission_limit = _init(model, :eq_emission_limit)
for emission in instance.emissions, t in T
isfinite(emission.limit[t]) || continue
eq_emission_limit[emission.name, t] = @constraint(
model,
sum(
z_em_plant[emission.name, p.name, t] for
p in plants if emission.name in keys(p.emissions)
) + sum( ) + sum(
z_em_tr[emission.name, p1.name, p2.name, m.name, t] for # Fixed operating costs (expansion)
(p1, p2, m) in E if emission.name in keys(m.tr_emissions) slope_fix_oper_cost(pn[n].location, t) * expansion[n, t] +
) <= emission.limit[t]
# Processing costs
pn[n].location.sizes[1].variable_operating_cost[t] * process[n, t] +
# Storage costs
pn[n].location.storage_cost[t] * store[n, t] +
# Expansion costs
(
t < T ? (
(
slope_open(pn[n].location, t) -
slope_open(pn[n].location, t + 1)
) * expansion[n, t]
) : slope_open(pn[n].location, t) * expansion[n, t]
)
for n in 1:PN
for t in 1:T
) + sum(
# Disposal costs (plants)
psn[n].location.disposal_cost[psn[n].product][t] * plant_dispose[n, t]
for n in 1:PSN
for t in 1:T
) + sum(
# Disposal costs (collection centers)
csn[n].location.product.disposal_cost[t] * collection_dispose[n, t]
for n in 1:CSN
for t in 1:T
) + sum(
# Collection shortfall
1e4 * collection_shortfall[n, t]
for n in 1:CSN
for t in 1:T
)
)
# Process node constraints
for t = 1:T, n in 1:PN
node = pn[n]
# Output amount is implied by amount processed
for arc in node.outgoing_arcs
@constraint(
model,
flow[arc.index, t] == arc.values["weight"] * process[n, t]
) )
end end
if variable_names # If plant is closed, capacity is zero
_set_names!(model) @constraint(
model,
capacity[n, t] <= node.location.sizes[2].capacity * is_open[n, t]
)
# If plant is open, capacity is greater than base
@constraint(
model,
capacity[n, t] >= node.location.sizes[1].capacity * is_open[n, t]
)
# Capacity is linked to expansion
@constraint(
model,
capacity[n, t] <=
node.location.sizes[1].capacity + expansion[n, t]
)
# Can only process up to capacity
@constraint(model, process[n, t] <= capacity[n, t])
if t > 1
# Plant capacity can only increase over time
@constraint(model, capacity[n, t] >= capacity[n, t-1])
@constraint(model, expansion[n, t] >= expansion[n, t-1])
end
# Amount received equals amount processed plus stored
store_in = 0
if t > 1
store_in = store[n, t-1]
end
if t == T
@constraint(model, store[n, t] == 0)
end
@constraint(
model,
sum(
flow[arc.index, t]
for arc in node.incoming_arcs
) + store_in == store[n, t] + process[n, t]
)
end
# Material flow at collection shipping nodes
@constraint(
model,
eq_balance_centers[
n in 1:CSN,
t in 1:T,
],
sum(
flow[arc.index, t]
for arc in csn[n].outgoing_arcs
) == csn[n].location.amount[t] - collection_dispose[n, t] - collection_shortfall[n, t]
)
# Material flow at plant shipping nodes
@constraint(
model,
eq_balance_plant[
n in 1:PSN,
t in 1:T,
],
sum(flow[a.index, t] for a in psn[n].incoming_arcs) ==
sum(flow[a.index, t] for a in psn[n].outgoing_arcs) +
plant_dispose[n, t]
)
# Enforce product disposal limit at collection centers
for t in 1:T, prod in instance.products
if isempty(prod.collection_centers)
continue
end
@constraint(
model,
sum(
collection_dispose[n, t]
for n in 1:CSN
if csn[n].product.name == prod.name
) <= prod.disposal_limit[t]
)
end
end
end
ξ = [
@scenario graph = graphs[i] probability = probs[i]
for i in 1:length(graphs)
]
if method == :ef
sp = instantiate(model, ξ; optimizer=optimizer)
elseif method == :lshaped
sp = instantiate(model, ξ; optimizer=LShaped.Optimizer)
set_optimizer_attribute(sp, MasterOptimizer(), optimizer)
set_optimizer_attribute(sp, SubProblemOptimizer(), optimizer)
set_optimizer_attribute(sp, RelativeTolerance(), tol)
else
error("unknown method: $method")
end
return sp
end
function slope_open(plant, t)
if plant.sizes[2].capacity <= plant.sizes[1].capacity
0.0
else
(plant.sizes[2].opening_cost[t] - plant.sizes[1].opening_cost[t]) /
(plant.sizes[2].capacity - plant.sizes[1].capacity)
end
end
function slope_fix_oper_cost(plant, t)
if plant.sizes[2].capacity <= plant.sizes[1].capacity
0.0
else
(plant.sizes[2].fixed_operating_cost[t] - plant.sizes[1].fixed_operating_cost[t]) /
(plant.sizes[2].capacity - plant.sizes[1].capacity)
end end
return model
end end

View File

@@ -1,110 +0,0 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Geodesy
using NearestNeighbors
using DataFrames
using CRC
using ZipFile
using Statistics
using TimerOutputs
crc32 = crc(CRC_32)
function _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
::EuclideanDistance,
)::Float64
x = LLA(source_lat, source_lon, 0.0)
y = LLA(dest_lat, dest_lon, 0.0)
return round(euclidean_distance(x, y) / 1000.0, digits = 3)
end
function _download_file(url, output, expected_crc32)::Nothing
if isfile(output)
return
end
mkpath(dirname(output))
@info "Downloading: $url"
fname = download(url)
actual_crc32 = open(crc32, fname)
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
cp(fname, output)
return
end
function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
if isfile(expected_output_file)
return
end
mkpath(outputdir)
@info "Downloading: $url"
zip_filename = download(url)
actual_crc32 = open(crc32, zip_filename)
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
open(zip_filename) do zip_file
zr = ZipFile.Reader(zip_file)
for file in zr.files
open(joinpath(outputdir, file.name), "w") do output_file
write(output_file, read(file))
end
end
end
return
end
function _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
metric::KnnDrivingDistance,
)::Float64
if metric.tree === nothing
basedir = joinpath(dirname(@__FILE__), "data")
csv_filename = joinpath(basedir, "dist_driving.csv")
# Download pre-computed driving data
@timeit "Download data" begin
if !isfile(csv_filename)
_download_zip(
"https://axavier.org/RELOG/0.6/data/dist_driving_0b9a6ad6.zip",
basedir,
csv_filename,
0x0b9a6ad6,
)
end
end
@timeit "Fit KNN model" begin
df = DataFrame(CSV.File(csv_filename, missingstring = "NaN"))
dropmissing!(df)
coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
metric.ratios = Matrix(df[!, [:ratio]])
metric.tree = KDTree(coords)
end
end
@timeit "Compute Euclidean distance" begin
dist_euclidean = _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
EuclideanDistance(),
)
end
@timeit "Predict driving distance" begin
idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
ratio_pred = mean(metric.ratios[idxs])
dist_pred = round(dist_euclidean * ratio_pred, digits = 3)
isfinite(dist_pred) || error("non-finite distance detected: $dist_pred")
end
return dist_pred
end

265
src/model/getsol.jl Normal file
View File

@@ -0,0 +1,265 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using JuMP, LinearAlgebra, Geodesy, ProgressBars, Printf, DataStructures
function get_solution(
instance,
graph,
model,
scenario_index::Int=1;
marginal_costs=false,
)
value(x) = StochasticPrograms.value(x, scenario_index)
ivalue(x) = StochasticPrograms.value(x)
shadow_price(x) = StochasticPrograms.shadow_price(x, scenario_index)
T = instance.time
pn = graph.process_nodes
psn = graph.plant_shipping_nodes
csn = graph.collection_shipping_nodes
arcs = graph.arcs
A = length(arcs)
PN = length(pn)
CSN = length(csn)
PSN = length(psn)
flow = model[2, :flow]
output = OrderedDict(
"Plants" => OrderedDict(),
"Products" => OrderedDict(),
"Costs" => OrderedDict(
"Fixed operating (\$)" => zeros(T),
"Variable operating (\$)" => zeros(T),
"Opening (\$)" => zeros(T),
"Transportation (\$)" => zeros(T),
"Disposal (\$)" => zeros(T),
"Expansion (\$)" => zeros(T),
"Storage (\$)" => zeros(T),
"Total (\$)" => zeros(T),
),
"Energy" =>
OrderedDict("Plants (GJ)" => zeros(T), "Transportation (GJ)" => zeros(T)),
"Emissions" => OrderedDict(
"Plants (tonne)" => OrderedDict(),
"Transportation (tonne)" => OrderedDict(),
),
)
pn = graph.process_nodes
psn = graph.plant_shipping_nodes
plant_to_process_node_index = OrderedDict(
pn[n].location => n
for n in 1:length(pn)
)
plant_to_shipping_node_indices = OrderedDict(p => [] for p in instance.plants)
for n in 1:length(psn)
push!(plant_to_shipping_node_indices[psn[n].location], n)
end
# Products
for n in 1:CSN
node = csn[n]
location_dict = OrderedDict{Any,Any}(
"Latitude (deg)" => node.location.latitude,
"Longitude (deg)" => node.location.longitude,
"Amount (tonne)" => node.location.amount,
"Dispose (tonne)" => [
value(model[2, :collection_dispose][n, t])
for t = 1:T
],
"Disposal cost (\$)" => [
value(model[2, :collection_dispose][n, t]) *
node.location.product.disposal_cost[t]
for t = 1:T
]
)
if marginal_costs
location_dict["Marginal cost (\$/tonne)"] = [
round(abs(shadow_price(model[2, :eq_balance_centers][n, t])), digits=2) for t = 1:T
]
end
if node.product.name keys(output["Products"])
output["Products"][node.product.name] = OrderedDict()
end
output["Products"][node.product.name][node.location.name] = location_dict
end
# Plants
for plant in instance.plants
skip_plant = true
n = plant_to_process_node_index[plant]
process_node = pn[n]
plant_dict = OrderedDict{Any,Any}(
"Input" => OrderedDict(),
"Output" =>
OrderedDict("Send" => OrderedDict(), "Dispose" => OrderedDict()),
"Input product" => plant.input.name,
"Total input (tonne)" => [0.0 for t = 1:T],
"Total output" => OrderedDict(),
"Latitude (deg)" => plant.latitude,
"Longitude (deg)" => plant.longitude,
"Capacity (tonne)" =>
[value(model[2, :capacity][n, t]) for t = 1:T],
"Opening cost (\$)" => [
ivalue(model[1, :open_plant][n, t]) *
plant.sizes[1].opening_cost[t] for t = 1:T
],
"Fixed operating cost (\$)" => [
ivalue(model[1, :is_open][n, t]) *
plant.sizes[1].fixed_operating_cost[t] +
value(model[2, :expansion][n, t]) *
slope_fix_oper_cost(plant, t) for t = 1:T
],
"Expansion cost (\$)" => [
(
if t == 1
slope_open(plant, t) * value(model[2, :expansion][n, t])
else
slope_open(plant, t) * (
value(model[2, :expansion][n, t]) -
value(model[2, :expansion][n, t-1])
)
end
) for t = 1:T
],
"Process (tonne)" =>
[value(model[2, :process][n, t]) for t = 1:T],
"Variable operating cost (\$)" => [
value(model[2, :process][n, t]) *
plant.sizes[1].variable_operating_cost[t] for t = 1:T
],
"Storage (tonne)" =>
[value(model[2, :store][n, t]) for t = 1:T],
"Storage cost (\$)" => [
value(model[2, :store][n, t]) * plant.storage_cost[t]
for t = 1:T
],
)
output["Costs"]["Fixed operating (\$)"] += plant_dict["Fixed operating cost (\$)"]
output["Costs"]["Variable operating (\$)"] +=
plant_dict["Variable operating cost (\$)"]
output["Costs"]["Opening (\$)"] += plant_dict["Opening cost (\$)"]
output["Costs"]["Expansion (\$)"] += plant_dict["Expansion cost (\$)"]
output["Costs"]["Storage (\$)"] += plant_dict["Storage cost (\$)"]
# Inputs
for a in process_node.incoming_arcs
vals = [value(flow[a.index, t]) for t = 1:T]
if sum(vals) <= 1e-3
continue
end
skip_plant = false
dict = OrderedDict{Any,Any}(
"Amount (tonne)" => vals,
"Distance (km)" => a.values["distance"],
"Latitude (deg)" => a.source.location.latitude,
"Longitude (deg)" => a.source.location.longitude,
"Transportation cost (\$)" =>
a.source.product.transportation_cost .* vals .* a.values["distance"],
"Transportation energy (J)" =>
vals .* a.values["distance"] .* a.source.product.transportation_energy,
"Emissions (tonne)" => OrderedDict(),
)
emissions_dict = output["Emissions"]["Transportation (tonne)"]
for (em_name, em_values) in a.source.product.transportation_emissions
dict["Emissions (tonne)"][em_name] =
em_values .* dict["Amount (tonne)"] .* a.values["distance"]
if em_name keys(emissions_dict)
emissions_dict[em_name] = zeros(T)
end
emissions_dict[em_name] += dict["Emissions (tonne)"][em_name]
end
if a.source.location isa CollectionCenter
plant_name = "Origin"
location_name = a.source.location.name
else
plant_name = a.source.location.plant_name
location_name = a.source.location.location_name
end
if plant_name keys(plant_dict["Input"])
plant_dict["Input"][plant_name] = OrderedDict()
end
plant_dict["Input"][plant_name][location_name] = dict
plant_dict["Total input (tonne)"] += vals
output["Costs"]["Transportation (\$)"] += dict["Transportation cost (\$)"]
output["Energy"]["Transportation (GJ)"] +=
dict["Transportation energy (J)"] / 1e9
end
plant_dict["Energy (GJ)"] = plant_dict["Total input (tonne)"] .* plant.energy
output["Energy"]["Plants (GJ)"] += plant_dict["Energy (GJ)"]
plant_dict["Emissions (tonne)"] = OrderedDict()
emissions_dict = output["Emissions"]["Plants (tonne)"]
for (em_name, em_values) in plant.emissions
plant_dict["Emissions (tonne)"][em_name] =
em_values .* plant_dict["Total input (tonne)"]
if em_name keys(emissions_dict)
emissions_dict[em_name] = zeros(T)
end
emissions_dict[em_name] += plant_dict["Emissions (tonne)"][em_name]
end
# Outputs
for n2 in plant_to_shipping_node_indices[plant]
shipping_node = psn[n2]
product_name = shipping_node.product.name
plant_dict["Total output"][product_name] = zeros(T)
plant_dict["Output"]["Send"][product_name] = product_dict = OrderedDict()
disposal_amount =
[value(model[2, :plant_dispose][n2, t]) for t = 1:T]
if sum(disposal_amount) > 1e-5
skip_plant = false
plant_dict["Output"]["Dispose"][product_name] =
disposal_dict = OrderedDict()
disposal_dict["Amount (tonne)"] =
[value(model[2, :plant_dispose][n2, t]) for t = 1:T]
disposal_dict["Cost (\$)"] = [
disposal_dict["Amount (tonne)"][t] *
plant.disposal_cost[shipping_node.product][t] for t = 1:T
]
plant_dict["Total output"][product_name] += disposal_amount
output["Costs"]["Disposal (\$)"] += disposal_dict["Cost (\$)"]
end
for a in shipping_node.outgoing_arcs
vals = [value(flow[a.index, t]) for t = 1:T]
if sum(vals) <= 1e-3
continue
end
skip_plant = false
dict = OrderedDict(
"Amount (tonne)" => vals,
"Distance (km)" => a.values["distance"],
"Latitude (deg)" => a.dest.location.latitude,
"Longitude (deg)" => a.dest.location.longitude,
)
if a.dest.location.plant_name keys(product_dict)
product_dict[a.dest.location.plant_name] = OrderedDict()
end
product_dict[a.dest.location.plant_name][a.dest.location.location_name] =
dict
plant_dict["Total output"][product_name] += vals
end
end
if !skip_plant
if plant.plant_name keys(output["Plants"])
output["Plants"][plant.plant_name] = OrderedDict()
end
output["Plants"][plant.plant_name][plant.location_name] = plant_dict
end
end
output["Costs"]["Total (\$)"] = sum(values(output["Costs"]))
return output
end

View File

@@ -1,120 +0,0 @@
# This file extends some JuMP functions so that decision variables can be safely
# replaced by (constant) floating point numbers.
using Printf
using JuMP
import JuMP: value, fix, set_name
function value(x::Float64)
return x
end
function fix(x::Float64, v::Float64; force)
return abs(x - v) < 1e-6 || error("Value mismatch: $x != $v")
end
function set_name(::Number, ::String)
# nop
end
function _init(model::JuMP.Model, key::Symbol)::OrderedDict
if !(key in keys(object_dictionary(model)))
model[key] = OrderedDict()
end
return model[key]
end
function _set_names!(model::JuMP.Model)
@info "Setting variable and constraint names..."
time_varnames = @elapsed begin
_set_names!(object_dictionary(model))
end
@info @sprintf("Set names in %.2f seconds", time_varnames)
end
function _set_names!(dict::Dict)
for name in keys(dict)
dict[name] isa AbstractDict || continue
for idx in keys(dict[name])
if dict[name][idx] isa AffExpr
continue
end
idx_str = join(map(string, idx), ",")
set_name(dict[name][idx], "$name[$idx_str]")
end
end
end
"""
_add_pwl_constraints(model, xvar, yvars, xpts, ypts)
Add piecewise-linear constraints to a JuMP model for multiple y variables.
Creates constraints y_i = f_i(x) where each f_i is a piecewise-linear function
defined by the breakpoints (xpts, ypts[:, i]).
# Arguments
- `model`: JuMP model
- `xvar`: The x variable (JuMP variable)
- `yvars`: Vector of y variables (JuMP variables)
- `xpts`: Vector of x values for breakpoints (must be in non-decreasing order)
- `ypts`: Matrix of y values where ypts[i, j] is the y value for the j-th variable
at the i-th breakpoint
# Example
```julia
@variable(model, y1)
@variable(model, y2)
ypts_matrix = [1.5 2.0; 0.0 1.5; 3.0 0.5] # 3 breakpoints, 2 y variables
_add_pwl_constraints(model, x, [y1, y2], [0.0, 1.0, 2.0], ypts_matrix, name="multiPWL")
```
"""
function _add_pwl_constraints(model, xvar, yvars, xpts, ypts)
# Input validation
ypts isa AbstractMatrix || throw(ArgumentError("ypts must be a matrix"))
length(xpts) == size(ypts, 1) ||
throw(ArgumentError("xpts length must match number of rows in ypts"))
length(yvars) == size(ypts, 2) ||
throw(ArgumentError("Number of y variables must match number of columns in ypts"))
length(xpts) >= 1 || throw(ArgumentError("At least one breakpoint is required"))
# Check that xpts is increasing
for i = 2:length(xpts)
xpts[i] > xpts[i-1] || throw(ArgumentError("xpts must be in increasing order"))
end
n_points = length(xpts)
n_yvars = length(yvars)
if n_points == 1
# Single point case: y_j = ypts[1,j], x = xpts[1]
@constraint(model, xvar == xpts[1])
for j = 1:n_yvars
@constraint(model, yvars[j] == ypts[1, j])
end
elseif n_points == 2
# Two points case: single linear segment for each y variable
x1, x2 = xpts[1], xpts[2]
# Linear relationship for each y variable: y_j = y1_j + slope_j * (x-x1)
for j = 1:n_yvars
y1, y2 = ypts[1, j], ypts[2, j]
slope = (y2 - y1) / (x2 - x1)
@constraint(model, yvars[j] == y1 + slope * (xvar - x1))
end
else
# Multiple segments case (3+ points): use SOS2 formulation
λ = @variable(model, [1:n_points], lower_bound = 0, upper_bound = 1)
@constraint(model, λ in SOS2())
@constraint(model, sum(λ) == 1)
@constraint(model, xvar == sum(xpts[i] * λ[i] for i = 1:n_points))
for j = 1:n_yvars
@constraint(model, yvars[j] == sum(ypts[i, j] * λ[i] for i = 1:n_points))
end
end
return
end

137
src/model/solve.jl Normal file
View File

@@ -0,0 +1,137 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using JuMP, LinearAlgebra, Geodesy, HiGHS, ProgressBars, Printf, DataStructures
function _get_default_milp_optimizer()
return optimizer_with_attributes(HiGHS.Optimizer)
end
function _get_default_lp_optimizer()
return optimizer_with_attributes(HiGHS.Optimizer)
end
function _print_graph_stats(instance::Instance, graph::Graph)::Nothing
@info @sprintf(" %12d time periods", instance.time)
@info @sprintf(" %12d process nodes", length(graph.process_nodes))
@info @sprintf(" %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
@info @sprintf(
" %12d shipping nodes (collection)",
length(graph.collection_shipping_nodes)
)
@info @sprintf(" %12d arcs", length(graph.arcs))
return
end
function solve_stochastic(;
scenarios::Vector{String},
probs::Vector{Float64},
optimizer,
method=:ef,
tol=0.1,
)
@info "Reading instance files..."
instances = [parsefile(sc) for sc in scenarios]
@info "Building graphs..."
graphs = [build_graph(inst) for inst in instances]
@info "Building stochastic model..."
sp = RELOG.build_model(instances[1], graphs, probs; optimizer, method, tol)
@info "Optimizing stochastic model..."
optimize!(sp)
@info "Extracting solution..."
solutions = [
get_solution(instances[i], graphs[i], sp, i)
for i in 1:length(instances)
]
return solutions
end
function solve(
instance::Instance;
optimizer=HiGHS.Optimizer,
marginal_costs=true,
return_model=false
)
@info "Building graph..."
graph = RELOG.build_graph(instance)
_print_graph_stats(instance, graph)
@info "Building model..."
model = RELOG.build_model(instance, [graph], [1.0]; optimizer)
@info "Optimizing model..."
optimize!(model)
if !has_values(model)
error("No solution available")
end
@info "Extracting solution..."
solution = get_solution(instance, graph, model, 1)
if marginal_costs
@info "Re-optimizing with integer variables fixed..."
open_plant_vals = value.(model[1, :open_plant])
is_open_vals = value.(model[1, :is_open])
for n in 1:length(graph.process_nodes), t in 1:instance.time
unset_binary(model[1, :open_plant][n, t])
unset_binary(model[1, :is_open][n, t])
fix(
model[1, :open_plant][n, t],
open_plant_vals[n, t]
)
fix(
model[1, :is_open][n, t],
is_open_vals[n, t]
)
end
optimize!(model)
if has_values(model)
@info "Extracting solution..."
solution = get_solution(instance, graph, model, 1, marginal_costs=true)
else
@warn "Error computing marginal costs. Ignoring."
end
end
if return_model
return solution, model
else
return solution
end
end
function solve(filename::AbstractString; heuristic=false, kwargs...)
@info "Reading $filename..."
instance = RELOG.parsefile(filename)
if heuristic && instance.time > 1
@info "Solving single-period version..."
compressed = _compress(instance)
csol, model = solve(compressed; marginal_costs=false, return_model=true, kwargs...)
@info "Filtering candidate locations..."
selected_pairs = []
for (plant_name, plant_dict) in csol["Plants"]
for (location_name, location_dict) in plant_dict
push!(selected_pairs, (plant_name, location_name))
end
end
filtered_plants = []
for p in instance.plants
if (p.plant_name, p.location_name) in selected_pairs
push!(filtered_plants, p)
end
end
instance.plants = filtered_plants
@info "Solving original version..."
end
sol = solve(instance; kwargs...)
return sol
end

View File

@@ -1,101 +0,0 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function centers_report(model)::DataFrame
df = DataFrame()
df."center" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."year" = Int[]
df."input product" = String[]
df."input amount (tonne)" = Float64[]
df."revenue (\$)" = Float64[]
df."operating cost (\$)" = Float64[]
centers = model.ext[:instance].centers
T = 1:model.ext[:instance].time_horizon
E_in = model.ext[:E_in]
for c in centers, t in T
input_name = (c.input === nothing) ? "" : c.input.name
input = value(model[:z_input][c.name, t])
if isempty(E_in[c])
revenue = 0
else
revenue = sum(
c.revenue[t] * value(model[:y][p.name, c.name, m.name, t]) for
(p, m) in E_in[c]
)
end
push!(
df,
Dict(
"center" => c.name,
"latitude" => c.latitude,
"longitude" => c.longitude,
"year" => t,
"input product" => input_name,
"input amount (tonne)" => _round(input),
"revenue (\$)" => _round(revenue),
"operating cost (\$)" => _round(c.operating_cost[t]),
),
)
end
return df
end
function center_outputs_report(model)::DataFrame
df = DataFrame()
df."center" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."output product" = String[]
df."year" = Int[]
df."amount collected (tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
df."disposal limit (tonne)" = Float64[]
df."collection cost (\$)" = Float64[]
df."disposal cost (\$)" = Float64[]
centers = model.ext[:instance].centers
T = 1:model.ext[:instance].time_horizon
E_out = model.ext[:E_out]
for c in centers, m in c.outputs, t in T
collected = value(model[:z_collected][c.name, m.name, t])
disposed = value(model[:z_disp][c.name, m.name, t])
disposal_cost = c.disposal_cost[m][t] * disposed
if isempty(E_out[c])
collection_cost = 0
else
collection_cost = sum(
c.collection_cost[m][t] * value(model[:y][c.name, p.name, m.name, t])
for (p, m) in E_out[c]
)
end
push!(
df,
Dict(
"center" => c.name,
"latitude" => c.latitude,
"longitude" => c.longitude,
"output product" => m.name,
"year" => t,
"amount collected (tonne)" => _round(collected),
"amount disposed (tonne)" => _round(disposed),
"disposal limit (tonne)" => _round(c.disposal_limit[m][t]),
"collection cost (\$)" => _round(collection_cost),
"disposal cost (\$)" => _round(disposal_cost),
),
)
end
return df
end
write_centers_report(solution, filename) = CSV.write(filename, centers_report(solution))
write_center_outputs_report(solution, filename) =
CSV.write(filename, center_outputs_report(solution))

View File

@@ -0,0 +1,38 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function plant_emissions_report(solution)::DataFrame
df = DataFrame()
df."plant type" = String[]
df."location name" = String[]
df."year" = Int[]
df."emission type" = String[]
df."emission amount (tonne)" = Float64[]
T = length(solution["Energy"]["Plants (GJ)"])
for (plant_name, plant_dict) in solution["Plants"]
for (location_name, location_dict) in plant_dict
for (emission_name, emission_amount) in location_dict["Emissions (tonne)"]
for year = 1:T
push!(
df,
[
plant_name,
location_name,
year,
emission_name,
round(emission_amount[year], digits = 2),
],
)
end
end
end
end
return df
end
write_plant_emissions_report(solution, filename) =
CSV.write(filename, plant_emissions_report(solution))

View File

@@ -0,0 +1,66 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function plant_outputs_report(solution)::DataFrame
df = DataFrame()
df."plant type" = String[]
df."location name" = String[]
df."year" = Int[]
df."product name" = String[]
df."amount produced (tonne)" = Float64[]
df."amount sent (tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
df."disposal cost (\$)" = Float64[]
T = length(solution["Energy"]["Plants (GJ)"])
for (plant_name, plant_dict) in solution["Plants"]
for (location_name, location_dict) in plant_dict
for (product_name, amount_produced) in location_dict["Total output"]
send_dict = location_dict["Output"]["Send"]
disposal_dict = location_dict["Output"]["Dispose"]
sent = zeros(T)
if product_name in keys(send_dict)
for (dst_plant_name, dst_plant_dict) in send_dict[product_name]
for (dst_location_name, dst_location_dict) in dst_plant_dict
sent += dst_location_dict["Amount (tonne)"]
end
end
end
sent = round.(sent, digits = 2)
disposal_amount = zeros(T)
disposal_cost = zeros(T)
if product_name in keys(disposal_dict)
disposal_amount += disposal_dict[product_name]["Amount (tonne)"]
disposal_cost += disposal_dict[product_name]["Cost (\$)"]
end
disposal_amount = round.(disposal_amount, digits = 2)
disposal_cost = round.(disposal_cost, digits = 2)
for year = 1:T
push!(
df,
[
plant_name,
location_name,
year,
product_name,
round(amount_produced[year], digits = 2),
sent[year],
disposal_amount[year],
disposal_cost[year],
],
)
end
end
end
end
return df
end
write_plant_outputs_report(solution, filename) =
CSV.write(filename, plant_outputs_report(solution))

View File

@@ -5,201 +5,75 @@
using DataFrames using DataFrames
using CSV using CSV
function plants_report(model)::DataFrame function plants_report(solution)::DataFrame
df = DataFrame() df = DataFrame()
df."plant" = String[] df."plant type" = String[]
df."latitude" = Float64[] df."location name" = String[]
df."longitude" = Float64[]
df."initial capacity" = Float64[]
df."current capacity" = Float64[]
df."year" = Int[] df."year" = Int[]
df."operational?" = Bool[] df."latitude (deg)" = Float64[]
df."input amount (tonne)" = Float64[] df."longitude (deg)" = Float64[]
df."stored amount (tonne)" = Float64[] df."capacity (tonne)" = Float64[]
df."processed amount (tonne)" = Float64[] df."amount processed (tonne)" = Float64[]
df."amount received (tonne)" = Float64[]
df."amount in storage (tonne)" = Float64[]
df."utilization factor (%)" = Float64[]
df."energy (GJ)" = Float64[]
df."opening cost (\$)" = Float64[] df."opening cost (\$)" = Float64[]
df."expansion cost (\$)" = Float64[]
df."fixed operating cost (\$)" = Float64[] df."fixed operating cost (\$)" = Float64[]
df."variable operating cost (\$)" = Float64[] df."variable operating cost (\$)" = Float64[]
df."expansion cost (\$)" = Float64[]
df."storage cost (\$)" = Float64[] df."storage cost (\$)" = Float64[]
df."total cost (\$)" = Float64[]
plants = model.ext[:instance].plants T = length(solution["Energy"]["Plants (GJ)"])
T = 1:model.ext[:instance].time_horizon for (plant_name, plant_dict) in solution["Plants"]
for (location_name, location_dict) in plant_dict
for p in plants, t in T for year = 1:T
operational = JuMP.value(model[:x][p.name, t]) > 0.5 capacity = round(location_dict["Capacity (tonne)"][year], digits = 2)
input = value(model[:z_input][p.name, t]) received = round(location_dict["Total input (tonne)"][year], digits = 2)
processed = value(model[:z_process][p.name, t]) processed = round(location_dict["Process (tonne)"][year], digits = 2)
in_storage = round(location_dict["Storage (tonne)"][year], digits = 2)
# Calculate total stored amount across all input materials utilization_factor = round(processed / capacity * 100.0, digits = 2)
stored = sum(value(model[:z_storage][p.name, m.name, t]) for m in keys(p.input_mix)) energy = round(location_dict["Energy (GJ)"][year], digits = 2)
latitude = round(location_dict["Latitude (deg)"], digits = 6)
# Calculate total storage cost longitude = round(location_dict["Longitude (deg)"], digits = 6)
storage_cost = sum( opening_cost = round(location_dict["Opening cost (\$)"][year], digits = 2)
p.storage_cost[m][t] * value(model[:z_storage][p.name, m.name, t]) for expansion_cost =
m in keys(p.storage_cost) round(location_dict["Expansion cost (\$)"][year], digits = 2)
fixed_cost =
round(location_dict["Fixed operating cost (\$)"][year], digits = 2)
var_cost =
round(location_dict["Variable operating cost (\$)"][year], digits = 2)
storage_cost = round(location_dict["Storage cost (\$)"][year], digits = 2)
total_cost = round(
opening_cost + expansion_cost + fixed_cost + var_cost + storage_cost,
digits = 2,
) )
var_operating_cost = input * p.capacities[1].var_operating_cost[t]
opening_cost = 0
curr_capacity = 0
expansion_cost = 0
fix_operating_cost = 0
if value(model[:x][p.name, t]) > 0.5 && value(model[:x][p.name, t-1]) < 0.5
opening_cost = p.capacities[1].opening_cost[t]
end
if operational
curr_expansion = JuMP.value(model[:z_exp][p.name, t])
prev_expansion = JuMP.value(model[:z_exp][p.name, t-1])
curr_capacity = p.capacities[1].size + curr_expansion
expansion_cost = R_expand(p, t) * (curr_expansion - prev_expansion)
fix_operating_cost =
p.capacities[1].fix_operating_cost[t] + R_fix_exp(p, t) * curr_expansion
end
push!( push!(
df, df,
Dict( [
"plant" => p.name, plant_name,
"latitude" => p.latitude, location_name,
"longitude" => p.longitude, year,
"initial capacity" => p.initial_capacity, latitude,
"current capacity" => curr_capacity, longitude,
"year" => t, capacity,
"operational?" => operational, processed,
"input amount (tonne)" => _round(input), received,
"stored amount (tonne)" => _round(stored), in_storage,
"processed amount (tonne)" => _round(processed), utilization_factor,
"opening cost (\$)" => _round(opening_cost), energy,
"fixed operating cost (\$)" => _round(fix_operating_cost), opening_cost,
"variable operating cost (\$)" => _round(var_operating_cost), expansion_cost,
"expansion cost (\$)" => _round(expansion_cost), fixed_cost,
"storage cost (\$)" => _round(storage_cost), var_cost,
), storage_cost,
total_cost,
],
) )
end end
return df
end end
function plant_inputs_report(model)::DataFrame
df = DataFrame()
df."plant" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."input product" = String[]
df."year" = Int[]
df."amount received (tonne)" = Float64[]
df."current storage level (tonne)" = Float64[]
df."storage limit (tonne)" = Float64[]
df."storage cost (\$)" = Float64[]
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, m in keys(p.input_mix), t in T
amount_received = sum(
value(model[:y][src.name, p.name, m.name, t]) for
(src, prod) in model.ext[:E_in][p] if prod == m
)
storage_level = value(model[:z_storage][p.name, m.name, t])
storage_cost = p.storage_cost[m][t] * storage_level
push!(
df,
Dict(
"plant" => p.name,
"latitude" => p.latitude,
"longitude" => p.longitude,
"input product" => m.name,
"year" => t,
"amount received (tonne)" => _round(amount_received),
"current storage level (tonne)" => _round(storage_level),
"storage limit (tonne)" => _round(p.storage_limit[m][t]),
"storage cost (\$)" => _round(storage_cost),
),
)
end
return df
end
function plant_outputs_report(model)::DataFrame
df = DataFrame()
df."plant" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."output product" = String[]
df."year" = Int[]
df."amount produced (tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
df."disposal limit (tonne)" = Float64[]
df."disposal cost (\$)" = Float64[]
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, m in keys(p.output), t in T
produced = JuMP.value(model[:z_prod][p.name, m.name, t])
disposed = JuMP.value(model[:z_disp][p.name, m.name, t])
disposal_cost = p.disposal_cost[m][t] * disposed
push!(
df,
Dict(
"plant" => p.name,
"latitude" => p.latitude,
"longitude" => p.longitude,
"output product" => m.name,
"year" => t,
"amount produced (tonne)" => _round(produced),
"amount disposed (tonne)" => _round(disposed),
"disposal limit (tonne)" => _round(p.disposal_limit[m][t]),
"disposal cost (\$)" => _round(disposal_cost),
),
)
end
return df
end
function plant_emissions_report(model)::DataFrame
df = DataFrame()
df."plant" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."emission" = String[]
df."year" = Int[]
df."processed amount (tonne)" = Float64[]
df."emission factor (tonne/tonne)" = Float64[]
df."emissions amount (tonne)" = Float64[]
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, t in T, g in keys(p.emissions)
processed_amount = JuMP.value(model[:z_process][p.name, t])
processed_amount > 1e-3 || continue
emissions = JuMP.value(model[:z_em_plant][g, p.name, t])
emission_factor = p.emissions[g][t]
push!(
df,
Dict(
"plant" => p.name,
"latitude" => p.latitude,
"longitude" => p.longitude,
"emission" => g,
"year" => t,
"processed amount (tonne)" => _round(processed_amount),
"emission factor (tonne/tonne)" => _round(emission_factor),
"emissions amount (tonne)" => _round(emissions),
),
)
end end
return df return df
end end
write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution)) write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution))
write_plant_inputs_report(solution, filename) =
CSV.write(filename, plant_inputs_report(solution))
write_plant_outputs_report(solution, filename) =
CSV.write(filename, plant_outputs_report(solution))
write_plant_emissions_report(solution, filename) =
CSV.write(filename, plant_emissions_report(solution))

52
src/reports/products.jl Normal file
View File

@@ -0,0 +1,52 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function products_report(solution)::DataFrame
df = DataFrame()
df."product name" = String[]
df."location name" = String[]
df."latitude (deg)" = Float64[]
df."longitude (deg)" = Float64[]
df."year" = Int[]
df."amount (tonne)" = Float64[]
df."marginal cost (\$/tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
df."disposal cost (\$)" = Float64[]
T = length(solution["Energy"]["Plants (GJ)"])
for (prod_name, prod_dict) in solution["Products"]
for (location_name, location_dict) in prod_dict
for year = 1:T
marginal_cost = NaN
if "Marginal cost (\$/tonne)" in keys(location_dict)
marginal_cost = location_dict["Marginal cost (\$/tonne)"][year]
end
latitude = round(location_dict["Latitude (deg)"], digits = 6)
longitude = round(location_dict["Longitude (deg)"], digits = 6)
amount = location_dict["Amount (tonne)"][year]
amount_disposed = location_dict["Dispose (tonne)"][year]
disposal_cost = location_dict["Disposal cost (\$)"][year]
push!(
df,
[
prod_name,
location_name,
latitude,
longitude,
year,
amount,
marginal_cost,
amount_disposed,
disposal_cost,
],
)
end
end
end
return df
end
write_products_report(solution, filename) = CSV.write(filename, products_report(solution))

75
src/reports/tr.jl Normal file
View File

@@ -0,0 +1,75 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function transportation_report(solution)::DataFrame
df = DataFrame()
df."source type" = String[]
df."source location name" = String[]
df."source latitude (deg)" = Float64[]
df."source longitude (deg)" = Float64[]
df."destination type" = String[]
df."destination location name" = String[]
df."destination latitude (deg)" = Float64[]
df."destination longitude (deg)" = Float64[]
df."product" = String[]
df."year" = Int[]
df."distance (km)" = Float64[]
df."amount (tonne)" = Float64[]
df."amount-distance (tonne-km)" = Float64[]
df."transportation cost (\$)" = Float64[]
df."transportation energy (GJ)" = Float64[]
T = length(solution["Energy"]["Plants (GJ)"])
for (dst_plant_name, dst_plant_dict) in solution["Plants"]
for (dst_location_name, dst_location_dict) in dst_plant_dict
for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
for (src_location_name, src_location_dict) in src_plant_dict
for year = 1:T
push!(
df,
[
src_plant_name,
src_location_name,
round(src_location_dict["Latitude (deg)"], digits = 6),
round(src_location_dict["Longitude (deg)"], digits = 6),
dst_plant_name,
dst_location_name,
round(dst_location_dict["Latitude (deg)"], digits = 6),
round(dst_location_dict["Longitude (deg)"], digits = 6),
dst_location_dict["Input product"],
year,
round(src_location_dict["Distance (km)"], digits = 2),
round(
src_location_dict["Amount (tonne)"][year],
digits = 2,
),
round(
src_location_dict["Amount (tonne)"][year] *
src_location_dict["Distance (km)"],
digits = 2,
),
round(
src_location_dict["Transportation cost (\$)"][year],
digits = 2,
),
round(
src_location_dict["Transportation energy (J)"][year] /
1e9,
digits = 2,
),
],
)
end
end
end
end
end
return df
end
write_transportation_report(solution, filename) =
CSV.write(filename, transportation_report(solution))

View File

@@ -0,0 +1,71 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function transportation_emissions_report(solution)::DataFrame
df = DataFrame()
df."source type" = String[]
df."source location name" = String[]
df."source latitude (deg)" = Float64[]
df."source longitude (deg)" = Float64[]
df."destination type" = String[]
df."destination location name" = String[]
df."destination latitude (deg)" = Float64[]
df."destination longitude (deg)" = Float64[]
df."product" = String[]
df."year" = Int[]
df."distance (km)" = Float64[]
df."shipped amount (tonne)" = Float64[]
df."shipped amount-distance (tonne-km)" = Float64[]
df."emission type" = String[]
df."emission amount (tonne)" = Float64[]
T = length(solution["Energy"]["Plants (GJ)"])
for (dst_plant_name, dst_plant_dict) in solution["Plants"]
for (dst_location_name, dst_location_dict) in dst_plant_dict
for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
for (src_location_name, src_location_dict) in src_plant_dict
for (emission_name, emission_amount) in
src_location_dict["Emissions (tonne)"]
for year = 1:T
push!(
df,
[
src_plant_name,
src_location_name,
round(src_location_dict["Latitude (deg)"], digits = 6),
round(src_location_dict["Longitude (deg)"], digits = 6),
dst_plant_name,
dst_location_name,
round(dst_location_dict["Latitude (deg)"], digits = 6),
round(dst_location_dict["Longitude (deg)"], digits = 6),
dst_location_dict["Input product"],
year,
round(src_location_dict["Distance (km)"], digits = 2),
round(
src_location_dict["Amount (tonne)"][year],
digits = 2,
),
round(
src_location_dict["Amount (tonne)"][year] *
src_location_dict["Distance (km)"],
digits = 2,
),
emission_name,
round(emission_amount[year], digits = 2),
],
)
end
end
end
end
end
end
return df
end
write_transportation_emissions_report(solution, filename) =
CSV.write(filename, transportation_emissions_report(solution))

View File

@@ -1,99 +0,0 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function transportation_report(model)::DataFrame
df = DataFrame()
df."source" = String[]
df."destination" = String[]
df."product" = String[]
df."year" = Int[]
df."amount sent (tonne)" = Float64[]
df."distance (km)" = Float64[]
df."transportation cost (\$)" = Float64[]
df."center revenue (\$)" = Float64[]
df."center collection cost (\$)" = Float64[]
E = model.ext[:E]
distances = model.ext[:distances]
T = 1:model.ext[:instance].time_horizon
for (p1, p2, m) in E, t in T
amount = value(model[:y][p1.name, p2.name, m.name, t])
amount > 1e-3 || continue
distance = distances[p1, p2, m]
tr_cost = distance * amount * m.tr_cost[t]
revenue = 0
if isa(p2, Center)
revenue = p2.revenue[t] * amount
end
collection_cost = 0
if isa(p1, Center)
collection_cost = p1.collection_cost[m][t] * amount
end
push!(
df,
Dict(
"source" => p1.name,
"destination" => p2.name,
"product" => m.name,
"year" => t,
"amount sent (tonne)" => _round(amount),
"distance (km)" => _round(distance),
"transportation cost (\$)" => _round(tr_cost),
"center revenue (\$)" => _round(revenue),
"center collection cost (\$)" => _round(collection_cost),
),
)
end
return df
end
function transportation_emissions_report(model)::DataFrame
df = DataFrame()
df."source" = String[]
df."destination" = String[]
df."product" = String[]
df."emission" = String[]
df."year" = Int[]
df."amount sent (tonne)" = Float64[]
df."distance (km)" = Float64[]
df."emission factor (tonne/km/tonne)" = Float64[]
df."emission amount (tonne)" = Float64[]
E = model.ext[:E]
distances = model.ext[:distances]
T = 1:model.ext[:instance].time_horizon
for (p1, p2, m) in E, t in T, g in keys(m.tr_emissions)
amount = value(model[:y][p1.name, p2.name, m.name, t])
amount > 1e-3 || continue
distance = distances[p1, p2, m]
emission_factor = m.tr_emissions[g][t]
emissions = value(model[:z_em_tr][g, p1.name, p2.name, m.name, t])
push!(
df,
Dict(
"source" => p1.name,
"destination" => p2.name,
"product" => m.name,
"emission" => g,
"year" => t,
"amount sent (tonne)" => _round(amount),
"distance (km)" => _round(distance),
"emission factor (tonne/km/tonne)" => _round(emission_factor),
"emission amount (tonne)" => _round(emissions),
),
)
end
return df
end
write_transportation_report(solution, filename) =
CSV.write(filename, transportation_report(solution))
write_transportation_emissions_report(solution, filename) =
CSV.write(filename, transportation_emissions_report(solution))

14
src/reports/write.jl Normal file
View File

@@ -0,0 +1,14 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
import Base: write
function write(solution::AbstractDict, filename::AbstractString)
@info "Writing solution: $filename"
open(filename, "w") do file
JSON.print(file, solution, 2)
end
end

203
src/schemas/input.json Normal file
View File

@@ -0,0 +1,203 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"$id": "https://anl-ceeesa.github.io/RELOG/input",
"title": "Schema for RELOG Input File",
"definitions": {
"TimeSeries": {
"type": "array",
"items": {
"type": "number"
}
},
"Parameters": {
"type": "object",
"properties": {
"time horizon (years)": {
"type": "number"
}
},
"required": [
"time horizon (years)"
]
},
"Plant": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"input": {
"type": "string"
},
"outputs (tonne/tonne)": {
"type": "object",
"additionalProperties": {
"type": "number"
}
},
"energy (GJ/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"emissions (tonne/tonne)": {
"type": "object",
"additionalProperties": {
"$ref": "#/definitions/TimeSeries"
}
},
"locations": {
"$ref": "#/definitions/PlantLocation"
}
},
"required": [
"input",
"locations"
]
}
},
"PlantLocation": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"location": {
"type": "string"
},
"latitude (deg)": {
"type": "number"
},
"longitude (deg)": {
"type": "number"
},
"disposal": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"cost ($/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"limit (tonne)": {
"$ref": "#/definitions/TimeSeries"
}
},
"required": [
"cost ($/tonne)"
]
}
},
"storage": {
"type": "object",
"properties": {
"cost ($/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"limit (tonne)": {
"type": "number"
}
},
"required": [
"cost ($/tonne)",
"limit (tonne)"
]
},
"capacities (tonne)": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"variable operating cost ($/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"fixed operating cost ($)": {
"$ref": "#/definitions/TimeSeries"
},
"opening cost ($)": {
"$ref": "#/definitions/TimeSeries"
}
},
"required": [
"variable operating cost ($/tonne)",
"fixed operating cost ($)",
"opening cost ($)"
]
}
}
},
"required": [
"capacities (tonne)"
]
}
},
"InitialAmount": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"location": {
"type": "string"
},
"latitude (deg)": {
"type": "number"
},
"longitude (deg)": {
"type": "number"
},
"amount (tonne)": {
"$ref": "#/definitions/TimeSeries"
}
},
"required": [
"amount (tonne)"
]
}
},
"Product": {
"type": "object",
"additionalProperties": {
"type": "object",
"properties": {
"transportation cost ($/km/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"transportation energy (J/km/tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"transportation emissions (tonne/km/tonne)": {
"type": "object",
"additionalProperties": {
"$ref": "#/definitions/TimeSeries"
}
},
"initial amounts": {
"$ref": "#/definitions/InitialAmount"
},
"disposal limit (tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"disposal cost ($/tonne)": {
"$ref": "#/definitions/TimeSeries"
}
},
"required": [
"transportation cost ($/km/tonne)"
]
}
}
},
"type": "object",
"properties": {
"parameters": {
"$ref": "#/definitions/Parameters"
},
"plants": {
"$ref": "#/definitions/Plant"
},
"products": {
"$ref": "#/definitions/Product"
}
},
"required": [
"parameters",
"plants",
"products"
]
}

30
src/sysimage.jl Normal file
View File

@@ -0,0 +1,30 @@
using PackageCompiler
using TOML
using Logging
Logging.disable_logging(Logging.Info)
mkpath("build")
printstyled("Generating precompilation statements...\n", color = :light_green)
run(`julia --project=. --trace-compile=build/precompile.jl $ARGS`)
printstyled("Finding dependencies...\n", color = :light_green)
project = TOML.parsefile("Project.toml")
manifest = TOML.parsefile("Manifest.toml")
deps = Symbol[]
for dep in keys(project["deps"])
if "path" in keys(manifest[dep][1])
printstyled(" skip $(dep)\n", color = :light_black)
else
println(" add $(dep)")
push!(deps, Symbol(dep))
end
end
printstyled("Building system image...\n", color = :light_green)
create_sysimage(
deps,
precompile_statements_file = "build/precompile.jl",
sysimage_path = "build/sysimage.so",
)

View File

@@ -1,14 +0,0 @@
name = "RELOGT"
uuid = "d5238ab2-e29b-4856-ba0f-d2b80f40b47d"
authors = ["Alinson S. Xavier <git@axavier.org>"]
version = "0.1.0"
[deps]
HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
RELOG = "7cafaa7a-b311-45f0-b313-80bf15b5e5e5"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

View File

@@ -1,188 +0,0 @@
using OrderedCollections
using JSON
using RELOG
dict = OrderedDict
function run_boat_example()
cities_a = dict(
"Chicago" => [41.881832, -87.623177],
"New York City" => [40.712776, -74.005974],
"Los Angeles" => [34.052235, -118.243683],
"Houston" => [29.760427, -95.369804],
"Phoenix" => [33.448376, -112.074036],
"Philadelphia" => [39.952583, -75.165222],
"San Antonio" => [29.424122, -98.493629],
"San Diego" => [32.715736, -117.161087],
"Dallas" => [32.776664, -96.796988],
"San Jose" => [37.338208, -121.886329],
)
cities_b = dict(
"Chicago" => [41.881832, -87.623177],
"Phoenix" => [33.448376, -112.074036],
"Dallas" => [32.776664, -96.796988],
)
parameters = dict(
"time horizon (years)" => 5,
"building period (years)" => [1],
"distance metric" => "Euclidean",
)
nail_factory = dict(
"input" => nothing,
"outputs" => ["Nail"],
"fixed output (tonne)" => dict("Nail" => 1),
"variable output (tonne/tonne)" => dict("Nail" => 0),
"revenue (\$/tonne)" => nothing,
"collection cost (\$/tonne)" => dict("Nail" => 1000),
"operating cost (\$)" => 0,
"disposal limit (tonne)" => dict("Nail" => nothing),
"disposal cost (\$/tonne)" => dict("Nail" => 0),
)
forest = dict(
"input" => nothing,
"outputs" => ["Wood"],
"fixed output (tonne)" => dict("Wood" => 100),
"variable output (tonne/tonne)" => dict("Wood" => 0),
"revenue (\$/tonne)" => nothing,
"collection cost (\$/tonne)" => dict("Wood" => 250),
"operating cost (\$)" => 0,
"disposal limit (tonne)" => dict("Wood" => nothing),
"disposal cost (\$/tonne)" => dict("Wood" => 0),
)
retail = dict(
"input" => "NewBoat",
"outputs" => ["UsedBoat"],
"fixed output (tonne)" => dict("UsedBoat" => 0),
"variable output (tonne/tonne)" => dict("UsedBoat" => [0.10, 0.25, 0.10]),
"revenue (\$/tonne)" => 12_000,
"collection cost (\$/tonne)" => dict("UsedBoat" => 100),
"operating cost (\$)" => 125_000,
"disposal limit (tonne)" => dict("UsedBoat" => 0),
"disposal cost (\$/tonne)" => dict("UsedBoat" => 0),
)
prod = dict(
"transportation cost (\$/km/tonne)" => 0.30,
"transportation energy (J/km/tonne)" => 7_500,
"transportation emissions (tonne/km/tonne)" =>
dict("CO2" => 2.68, "NH4" => 1.02),
"disposal limit (tonne)" => nothing,
)
boat_factory = dict(
"input mix (%)" => dict("Wood" => 95, "Nail" => 5),
"output (tonne)" => dict("NewBoat" => 1.0),
"processing emissions (tonne)" => dict("CO2" => 5),
"storage cost (\$/tonne)" => dict("Wood" => 500, "Nail" => 200),
"storage limit (tonne)" => dict("Wood" => 5, "Nail" => 1),
"disposal cost (\$/tonne)" => dict("NewBoat" => 0),
"disposal limit (tonne)" => dict("NewBoat" => 0),
"capacities" => [
dict(
"size (tonne)" => 500,
"opening cost (\$)" => 1_00_000,
"fixed operating cost (\$)" => 250_000,
"variable operating cost (\$/tonne)" => 5,
),
dict(
"size (tonne)" => 1000,
"opening cost (\$)" => 2_000_000,
"fixed operating cost (\$)" => 500_000,
"variable operating cost (\$/tonne)" => 5,
),
],
"initial capacity (tonne)" => 0,
)
recycling_plant = dict(
"input mix (%)" => dict("UsedBoat" => 100),
"output (tonne)" => dict("Nail" => 0.025, "Wood" => 0.475),
"processing emissions (tonne)" => dict("CO2" => 5),
"storage cost (\$/tonne)" => dict("UsedBoat" => 0),
"storage limit (tonne)" => dict("UsedBoat" => 0),
"disposal cost (\$/tonne)" => dict("Nail" => 0, "Wood" => 0),
"disposal limit (tonne)" => dict("Nail" => 0, "Wood" => 0),
"capacities" => [
dict(
"size (tonne)" => 500,
"opening cost (\$)" => 500_000,
"fixed operating cost (\$)" => 125_000,
"variable operating cost (\$/tonne)" => 2.5,
),
dict(
"size (tonne)" => 1000,
"opening cost (\$)" => 1_000_000,
"fixed operating cost (\$)" => 250_000,
"variable operating cost (\$/tonne)" => 2.5,
),
],
"initial capacity (tonne)" => 0,
)
lat_lon_dict(city_location) =
dict("latitude (deg)" => city_location[1], "longitude (deg)" => city_location[2])
data = dict(
"parameters" => parameters,
"products" =>
dict("Nail" => prod, "Wood" => prod, "NewBoat" => prod, "UsedBoat" => prod),
"centers" => merge(
dict(
"NailFactory ($city_name)" =>
merge(nail_factory, lat_lon_dict(city_location)) for
(city_name, city_location) in cities_b
),
dict(
"Forest ($city_name)" => merge(forest, lat_lon_dict(city_location))
for (city_name, city_location) in cities_b
),
dict(
"Retail ($city_name)" => merge(retail, lat_lon_dict(city_location))
for (city_name, city_location) in cities_a
),
),
"plants" => merge(
dict(
"BoatFactory ($city_name)" =>
merge(boat_factory, lat_lon_dict(city_location)) for
(city_name, city_location) in cities_a
),
dict(
"RecyclingPlant ($city_name)" =>
merge(recycling_plant, lat_lon_dict(city_location)) for
(city_name, city_location) in cities_a
),
),
)
# Generate instance file
open(fixture("boat_example.json"), "w") do file
JSON.print(file, data, 2)
end
# Load and solve example
instance = RELOG.parsefile(fixture("boat_example.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
optimize!(model)
# Write reports
mkpath(fixture("boat_example"))
write_to_file(model, fixture("boat_example/model.lp"))
RELOG.write_plants_report(model, fixture("boat_example/plants.csv"))
RELOG.write_plant_inputs_report(model, fixture("boat_example/plant_inputs.csv"))
RELOG.write_plant_outputs_report(model, fixture("boat_example/plant_outputs.csv"))
RELOG.write_plant_emissions_report(model, fixture("boat_example/plant_emissions.csv"))
RELOG.write_centers_report(model, fixture("boat_example/centers.csv"))
RELOG.write_center_outputs_report(model, fixture("boat_example/center_outputs.csv"))
RELOG.write_transportation_report(model, fixture("boat_example/transportation.csv"))
RELOG.write_transportation_emissions_report(
model,
fixture("boat_example/tr_emissions.csv"),
)
return
end

File diff suppressed because it is too large Load Diff

View File

@@ -1,81 +0,0 @@
center,latitude,longitude,output product,year,amount collected (tonne),amount disposed (tonne),disposal limit (tonne),collection cost ($),disposal cost ($)
NailFactory (Chicago),41.881832,-87.623177,Nail,1,1.0,0.0,Inf,1000.0,0.0
NailFactory (Chicago),41.881832,-87.623177,Nail,2,1.0,0.0,Inf,1000.0,0.0
NailFactory (Chicago),41.881832,-87.623177,Nail,3,1.0,0.0,Inf,1000.0,0.0
NailFactory (Chicago),41.881832,-87.623177,Nail,4,1.0,0.0,Inf,1000.0,0.0
NailFactory (Chicago),41.881832,-87.623177,Nail,5,1.0,0.0,Inf,1000.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,Nail,1,1.0,0.0,Inf,1000.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,Nail,2,1.0,0.0,Inf,1000.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,Nail,3,1.0,0.0,Inf,1000.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,Nail,4,1.0,0.0,Inf,1000.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,Nail,5,1.0,0.0,Inf,1000.0,0.0
NailFactory (Dallas),32.776664,-96.796988,Nail,1,1.0,0.0,Inf,1000.0,0.0
NailFactory (Dallas),32.776664,-96.796988,Nail,2,1.0,0.0,Inf,1000.0,0.0
NailFactory (Dallas),32.776664,-96.796988,Nail,3,1.0,0.0,Inf,1000.0,0.0
NailFactory (Dallas),32.776664,-96.796988,Nail,4,1.0,0.0,Inf,1000.0,0.0
NailFactory (Dallas),32.776664,-96.796988,Nail,5,1.0,0.0,Inf,1000.0,0.0
Forest (Chicago),41.881832,-87.623177,Wood,1,100.0,100.0,Inf,0.0,0.0
Forest (Chicago),41.881832,-87.623177,Wood,2,100.0,100.0,Inf,0.0,0.0
Forest (Chicago),41.881832,-87.623177,Wood,3,100.0,100.0,Inf,0.0,0.0
Forest (Chicago),41.881832,-87.623177,Wood,4,100.0,100.0,Inf,0.0,0.0
Forest (Chicago),41.881832,-87.623177,Wood,5,100.0,100.0,Inf,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,Wood,1,100.0,100.0,Inf,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,Wood,2,100.0,100.0,Inf,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,Wood,3,100.0,100.0,Inf,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,Wood,4,100.0,100.0,Inf,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,Wood,5,100.0,100.0,Inf,0.0,0.0
Forest (Dallas),32.776664,-96.796988,Wood,1,100.0,43.0,Inf,14250.0,0.0
Forest (Dallas),32.776664,-96.796988,Wood,2,100.0,43.0,Inf,14250.0,0.0
Forest (Dallas),32.776664,-96.796988,Wood,3,100.0,43.0,Inf,14250.0,0.0
Forest (Dallas),32.776664,-96.796988,Wood,4,100.0,43.0,Inf,14250.0,0.0
Forest (Dallas),32.776664,-96.796988,Wood,5,100.0,43.0,Inf,14250.0,0.0
Retail (Chicago),41.881832,-87.623177,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (Chicago),41.881832,-87.623177,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (Chicago),41.881832,-87.623177,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (Chicago),41.881832,-87.623177,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (Chicago),41.881832,-87.623177,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (New York City),40.712776,-74.005974,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (New York City),40.712776,-74.005974,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (New York City),40.712776,-74.005974,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (New York City),40.712776,-74.005974,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (New York City),40.712776,-74.005974,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (Los Angeles),34.052235,-118.243683,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (Los Angeles),34.052235,-118.243683,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (Los Angeles),34.052235,-118.243683,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (Los Angeles),34.052235,-118.243683,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (Los Angeles),34.052235,-118.243683,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (Houston),29.760427,-95.369804,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (Houston),29.760427,-95.369804,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (Houston),29.760427,-95.369804,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (Houston),29.760427,-95.369804,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (Houston),29.760427,-95.369804,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (Phoenix),33.448376,-112.074036,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (Phoenix),33.448376,-112.074036,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (Phoenix),33.448376,-112.074036,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (Phoenix),33.448376,-112.074036,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (Phoenix),33.448376,-112.074036,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (Philadelphia),39.952583,-75.165222,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (Philadelphia),39.952583,-75.165222,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (Philadelphia),39.952583,-75.165222,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (Philadelphia),39.952583,-75.165222,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (Philadelphia),39.952583,-75.165222,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (San Antonio),29.424122,-98.493629,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (San Antonio),29.424122,-98.493629,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (San Antonio),29.424122,-98.493629,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (San Antonio),29.424122,-98.493629,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (San Antonio),29.424122,-98.493629,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (San Diego),32.715736,-117.161087,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (San Diego),32.715736,-117.161087,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (San Diego),32.715736,-117.161087,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (San Diego),32.715736,-117.161087,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (San Diego),32.715736,-117.161087,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
Retail (Dallas),32.776664,-96.796988,UsedBoat,1,6.31579,0.0,0.0,631.57895,0.0
Retail (Dallas),32.776664,-96.796988,UsedBoat,2,22.93629,0.0,0.0,2293.62881,0.0
Retail (Dallas),32.776664,-96.796988,UsedBoat,3,31.7714,0.0,0.0,3177.13952,0.0
Retail (Dallas),32.776664,-96.796988,UsedBoat,4,33.80867,0.0,0.0,3380.86724,0.0
Retail (Dallas),32.776664,-96.796988,UsedBoat,5,34.54174,0.0,0.0,3454.17409,0.0
Retail (San Jose),37.338208,-121.886329,UsedBoat,1,0.0,0.0,0.0,0.0,0.0
Retail (San Jose),37.338208,-121.886329,UsedBoat,2,0.0,0.0,0.0,0.0,0.0
Retail (San Jose),37.338208,-121.886329,UsedBoat,3,0.0,0.0,0.0,0.0,0.0
Retail (San Jose),37.338208,-121.886329,UsedBoat,4,0.0,0.0,0.0,0.0,0.0
Retail (San Jose),37.338208,-121.886329,UsedBoat,5,0.0,0.0,0.0,0.0,0.0
1 center latitude longitude output product year amount collected (tonne) amount disposed (tonne) disposal limit (tonne) collection cost ($) disposal cost ($)
2 NailFactory (Chicago) 41.881832 -87.623177 Nail 1 1.0 0.0 Inf 1000.0 0.0
3 NailFactory (Chicago) 41.881832 -87.623177 Nail 2 1.0 0.0 Inf 1000.0 0.0
4 NailFactory (Chicago) 41.881832 -87.623177 Nail 3 1.0 0.0 Inf 1000.0 0.0
5 NailFactory (Chicago) 41.881832 -87.623177 Nail 4 1.0 0.0 Inf 1000.0 0.0
6 NailFactory (Chicago) 41.881832 -87.623177 Nail 5 1.0 0.0 Inf 1000.0 0.0
7 NailFactory (Phoenix) 33.448376 -112.074036 Nail 1 1.0 0.0 Inf 1000.0 0.0
8 NailFactory (Phoenix) 33.448376 -112.074036 Nail 2 1.0 0.0 Inf 1000.0 0.0
9 NailFactory (Phoenix) 33.448376 -112.074036 Nail 3 1.0 0.0 Inf 1000.0 0.0
10 NailFactory (Phoenix) 33.448376 -112.074036 Nail 4 1.0 0.0 Inf 1000.0 0.0
11 NailFactory (Phoenix) 33.448376 -112.074036 Nail 5 1.0 0.0 Inf 1000.0 0.0
12 NailFactory (Dallas) 32.776664 -96.796988 Nail 1 1.0 0.0 Inf 1000.0 0.0
13 NailFactory (Dallas) 32.776664 -96.796988 Nail 2 1.0 0.0 Inf 1000.0 0.0
14 NailFactory (Dallas) 32.776664 -96.796988 Nail 3 1.0 0.0 Inf 1000.0 0.0
15 NailFactory (Dallas) 32.776664 -96.796988 Nail 4 1.0 0.0 Inf 1000.0 0.0
16 NailFactory (Dallas) 32.776664 -96.796988 Nail 5 1.0 0.0 Inf 1000.0 0.0
17 Forest (Chicago) 41.881832 -87.623177 Wood 1 100.0 100.0 Inf 0.0 0.0
18 Forest (Chicago) 41.881832 -87.623177 Wood 2 100.0 100.0 Inf 0.0 0.0
19 Forest (Chicago) 41.881832 -87.623177 Wood 3 100.0 100.0 Inf 0.0 0.0
20 Forest (Chicago) 41.881832 -87.623177 Wood 4 100.0 100.0 Inf 0.0 0.0
21 Forest (Chicago) 41.881832 -87.623177 Wood 5 100.0 100.0 Inf 0.0 0.0
22 Forest (Phoenix) 33.448376 -112.074036 Wood 1 100.0 100.0 Inf 0.0 0.0
23 Forest (Phoenix) 33.448376 -112.074036 Wood 2 100.0 100.0 Inf 0.0 0.0
24 Forest (Phoenix) 33.448376 -112.074036 Wood 3 100.0 100.0 Inf 0.0 0.0
25 Forest (Phoenix) 33.448376 -112.074036 Wood 4 100.0 100.0 Inf 0.0 0.0
26 Forest (Phoenix) 33.448376 -112.074036 Wood 5 100.0 100.0 Inf 0.0 0.0
27 Forest (Dallas) 32.776664 -96.796988 Wood 1 100.0 43.0 Inf 14250.0 0.0
28 Forest (Dallas) 32.776664 -96.796988 Wood 2 100.0 43.0 Inf 14250.0 0.0
29 Forest (Dallas) 32.776664 -96.796988 Wood 3 100.0 43.0 Inf 14250.0 0.0
30 Forest (Dallas) 32.776664 -96.796988 Wood 4 100.0 43.0 Inf 14250.0 0.0
31 Forest (Dallas) 32.776664 -96.796988 Wood 5 100.0 43.0 Inf 14250.0 0.0
32 Retail (Chicago) 41.881832 -87.623177 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
33 Retail (Chicago) 41.881832 -87.623177 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
34 Retail (Chicago) 41.881832 -87.623177 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
35 Retail (Chicago) 41.881832 -87.623177 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
36 Retail (Chicago) 41.881832 -87.623177 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
37 Retail (New York City) 40.712776 -74.005974 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
38 Retail (New York City) 40.712776 -74.005974 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
39 Retail (New York City) 40.712776 -74.005974 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
40 Retail (New York City) 40.712776 -74.005974 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
41 Retail (New York City) 40.712776 -74.005974 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
42 Retail (Los Angeles) 34.052235 -118.243683 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
43 Retail (Los Angeles) 34.052235 -118.243683 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
44 Retail (Los Angeles) 34.052235 -118.243683 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
45 Retail (Los Angeles) 34.052235 -118.243683 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
46 Retail (Los Angeles) 34.052235 -118.243683 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
47 Retail (Houston) 29.760427 -95.369804 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
48 Retail (Houston) 29.760427 -95.369804 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
49 Retail (Houston) 29.760427 -95.369804 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
50 Retail (Houston) 29.760427 -95.369804 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
51 Retail (Houston) 29.760427 -95.369804 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
52 Retail (Phoenix) 33.448376 -112.074036 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
53 Retail (Phoenix) 33.448376 -112.074036 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
54 Retail (Phoenix) 33.448376 -112.074036 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
55 Retail (Phoenix) 33.448376 -112.074036 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
56 Retail (Phoenix) 33.448376 -112.074036 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
57 Retail (Philadelphia) 39.952583 -75.165222 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
58 Retail (Philadelphia) 39.952583 -75.165222 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
59 Retail (Philadelphia) 39.952583 -75.165222 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
60 Retail (Philadelphia) 39.952583 -75.165222 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
61 Retail (Philadelphia) 39.952583 -75.165222 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
62 Retail (San Antonio) 29.424122 -98.493629 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
63 Retail (San Antonio) 29.424122 -98.493629 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
64 Retail (San Antonio) 29.424122 -98.493629 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
65 Retail (San Antonio) 29.424122 -98.493629 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
66 Retail (San Antonio) 29.424122 -98.493629 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
67 Retail (San Diego) 32.715736 -117.161087 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
68 Retail (San Diego) 32.715736 -117.161087 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
69 Retail (San Diego) 32.715736 -117.161087 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
70 Retail (San Diego) 32.715736 -117.161087 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
71 Retail (San Diego) 32.715736 -117.161087 UsedBoat 5 0.0 0.0 0.0 0.0 0.0
72 Retail (Dallas) 32.776664 -96.796988 UsedBoat 1 6.31579 0.0 0.0 631.57895 0.0
73 Retail (Dallas) 32.776664 -96.796988 UsedBoat 2 22.93629 0.0 0.0 2293.62881 0.0
74 Retail (Dallas) 32.776664 -96.796988 UsedBoat 3 31.7714 0.0 0.0 3177.13952 0.0
75 Retail (Dallas) 32.776664 -96.796988 UsedBoat 4 33.80867 0.0 0.0 3380.86724 0.0
76 Retail (Dallas) 32.776664 -96.796988 UsedBoat 5 34.54174 0.0 0.0 3454.17409 0.0
77 Retail (San Jose) 37.338208 -121.886329 UsedBoat 1 0.0 0.0 0.0 0.0 0.0
78 Retail (San Jose) 37.338208 -121.886329 UsedBoat 2 0.0 0.0 0.0 0.0 0.0
79 Retail (San Jose) 37.338208 -121.886329 UsedBoat 3 0.0 0.0 0.0 0.0 0.0
80 Retail (San Jose) 37.338208 -121.886329 UsedBoat 4 0.0 0.0 0.0 0.0 0.0
81 Retail (San Jose) 37.338208 -121.886329 UsedBoat 5 0.0 0.0 0.0 0.0 0.0

View File

@@ -1,81 +0,0 @@
center,latitude,longitude,year,input product,input amount (tonne),revenue ($),operating cost ($)
NailFactory (Chicago),41.881832,-87.623177,1,,0.0,0.0,0.0
NailFactory (Chicago),41.881832,-87.623177,2,,0.0,0.0,0.0
NailFactory (Chicago),41.881832,-87.623177,3,,0.0,0.0,0.0
NailFactory (Chicago),41.881832,-87.623177,4,,0.0,0.0,0.0
NailFactory (Chicago),41.881832,-87.623177,5,,0.0,0.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,1,,0.0,0.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,2,,0.0,0.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,3,,0.0,0.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,4,,0.0,0.0,0.0
NailFactory (Phoenix),33.448376,-112.074036,5,,0.0,0.0,0.0
NailFactory (Dallas),32.776664,-96.796988,1,,0.0,0.0,0.0
NailFactory (Dallas),32.776664,-96.796988,2,,0.0,0.0,0.0
NailFactory (Dallas),32.776664,-96.796988,3,,0.0,0.0,0.0
NailFactory (Dallas),32.776664,-96.796988,4,,0.0,0.0,0.0
NailFactory (Dallas),32.776664,-96.796988,5,,0.0,0.0,0.0
Forest (Chicago),41.881832,-87.623177,1,,0.0,0.0,0.0
Forest (Chicago),41.881832,-87.623177,2,,0.0,0.0,0.0
Forest (Chicago),41.881832,-87.623177,3,,0.0,0.0,0.0
Forest (Chicago),41.881832,-87.623177,4,,0.0,0.0,0.0
Forest (Chicago),41.881832,-87.623177,5,,0.0,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,1,,0.0,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,2,,0.0,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,3,,0.0,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,4,,0.0,0.0,0.0
Forest (Phoenix),33.448376,-112.074036,5,,0.0,0.0,0.0
Forest (Dallas),32.776664,-96.796988,1,,0.0,0.0,0.0
Forest (Dallas),32.776664,-96.796988,2,,0.0,0.0,0.0
Forest (Dallas),32.776664,-96.796988,3,,0.0,0.0,0.0
Forest (Dallas),32.776664,-96.796988,4,,0.0,0.0,0.0
Forest (Dallas),32.776664,-96.796988,5,,0.0,0.0,0.0
Retail (Chicago),41.881832,-87.623177,1,NewBoat,0.0,0.0,125000.0
Retail (Chicago),41.881832,-87.623177,2,NewBoat,0.0,0.0,125000.0
Retail (Chicago),41.881832,-87.623177,3,NewBoat,0.0,0.0,125000.0
Retail (Chicago),41.881832,-87.623177,4,NewBoat,0.0,0.0,125000.0
Retail (Chicago),41.881832,-87.623177,5,NewBoat,0.0,0.0,125000.0
Retail (New York City),40.712776,-74.005974,1,NewBoat,0.0,0.0,125000.0
Retail (New York City),40.712776,-74.005974,2,NewBoat,0.0,0.0,125000.0
Retail (New York City),40.712776,-74.005974,3,NewBoat,0.0,0.0,125000.0
Retail (New York City),40.712776,-74.005974,4,NewBoat,0.0,0.0,125000.0
Retail (New York City),40.712776,-74.005974,5,NewBoat,0.0,0.0,125000.0
Retail (Los Angeles),34.052235,-118.243683,1,NewBoat,0.0,0.0,125000.0
Retail (Los Angeles),34.052235,-118.243683,2,NewBoat,0.0,0.0,125000.0
Retail (Los Angeles),34.052235,-118.243683,3,NewBoat,0.0,0.0,125000.0
Retail (Los Angeles),34.052235,-118.243683,4,NewBoat,0.0,0.0,125000.0
Retail (Los Angeles),34.052235,-118.243683,5,NewBoat,0.0,0.0,125000.0
Retail (Houston),29.760427,-95.369804,1,NewBoat,0.0,0.0,125000.0
Retail (Houston),29.760427,-95.369804,2,NewBoat,0.0,0.0,125000.0
Retail (Houston),29.760427,-95.369804,3,NewBoat,0.0,0.0,125000.0
Retail (Houston),29.760427,-95.369804,4,NewBoat,0.0,0.0,125000.0
Retail (Houston),29.760427,-95.369804,5,NewBoat,0.0,0.0,125000.0
Retail (Phoenix),33.448376,-112.074036,1,NewBoat,0.0,0.0,125000.0
Retail (Phoenix),33.448376,-112.074036,2,NewBoat,0.0,0.0,125000.0
Retail (Phoenix),33.448376,-112.074036,3,NewBoat,0.0,0.0,125000.0
Retail (Phoenix),33.448376,-112.074036,4,NewBoat,0.0,0.0,125000.0
Retail (Phoenix),33.448376,-112.074036,5,NewBoat,0.0,0.0,125000.0
Retail (Philadelphia),39.952583,-75.165222,1,NewBoat,0.0,0.0,125000.0
Retail (Philadelphia),39.952583,-75.165222,2,NewBoat,0.0,0.0,125000.0
Retail (Philadelphia),39.952583,-75.165222,3,NewBoat,0.0,0.0,125000.0
Retail (Philadelphia),39.952583,-75.165222,4,NewBoat,0.0,0.0,125000.0
Retail (Philadelphia),39.952583,-75.165222,5,NewBoat,0.0,0.0,125000.0
Retail (San Antonio),29.424122,-98.493629,1,NewBoat,0.0,0.0,125000.0
Retail (San Antonio),29.424122,-98.493629,2,NewBoat,0.0,0.0,125000.0
Retail (San Antonio),29.424122,-98.493629,3,NewBoat,0.0,0.0,125000.0
Retail (San Antonio),29.424122,-98.493629,4,NewBoat,0.0,0.0,125000.0
Retail (San Antonio),29.424122,-98.493629,5,NewBoat,0.0,0.0,125000.0
Retail (San Diego),32.715736,-117.161087,1,NewBoat,0.0,0.0,125000.0
Retail (San Diego),32.715736,-117.161087,2,NewBoat,0.0,0.0,125000.0
Retail (San Diego),32.715736,-117.161087,3,NewBoat,0.0,0.0,125000.0
Retail (San Diego),32.715736,-117.161087,4,NewBoat,0.0,0.0,125000.0
Retail (San Diego),32.715736,-117.161087,5,NewBoat,0.0,0.0,125000.0
Retail (Dallas),32.776664,-96.796988,1,NewBoat,63.15789,757894.73684,125000.0
Retail (Dallas),32.776664,-96.796988,2,NewBoat,71.46814,857617.72853,125000.0
Retail (Dallas),32.776664,-96.796988,3,NewBoat,75.8857,910628.37148,125000.0
Retail (Dallas),32.776664,-96.796988,4,NewBoat,76.90434,922852.03459,125000.0
Retail (Dallas),32.776664,-96.796988,5,NewBoat,77.27087,927250.44516,125000.0
Retail (San Jose),37.338208,-121.886329,1,NewBoat,0.0,0.0,125000.0
Retail (San Jose),37.338208,-121.886329,2,NewBoat,0.0,0.0,125000.0
Retail (San Jose),37.338208,-121.886329,3,NewBoat,0.0,0.0,125000.0
Retail (San Jose),37.338208,-121.886329,4,NewBoat,0.0,0.0,125000.0
Retail (San Jose),37.338208,-121.886329,5,NewBoat,0.0,0.0,125000.0
1 center latitude longitude year input product input amount (tonne) revenue ($) operating cost ($)
2 NailFactory (Chicago) 41.881832 -87.623177 1 0.0 0.0 0.0
3 NailFactory (Chicago) 41.881832 -87.623177 2 0.0 0.0 0.0
4 NailFactory (Chicago) 41.881832 -87.623177 3 0.0 0.0 0.0
5 NailFactory (Chicago) 41.881832 -87.623177 4 0.0 0.0 0.0
6 NailFactory (Chicago) 41.881832 -87.623177 5 0.0 0.0 0.0
7 NailFactory (Phoenix) 33.448376 -112.074036 1 0.0 0.0 0.0
8 NailFactory (Phoenix) 33.448376 -112.074036 2 0.0 0.0 0.0
9 NailFactory (Phoenix) 33.448376 -112.074036 3 0.0 0.0 0.0
10 NailFactory (Phoenix) 33.448376 -112.074036 4 0.0 0.0 0.0
11 NailFactory (Phoenix) 33.448376 -112.074036 5 0.0 0.0 0.0
12 NailFactory (Dallas) 32.776664 -96.796988 1 0.0 0.0 0.0
13 NailFactory (Dallas) 32.776664 -96.796988 2 0.0 0.0 0.0
14 NailFactory (Dallas) 32.776664 -96.796988 3 0.0 0.0 0.0
15 NailFactory (Dallas) 32.776664 -96.796988 4 0.0 0.0 0.0
16 NailFactory (Dallas) 32.776664 -96.796988 5 0.0 0.0 0.0
17 Forest (Chicago) 41.881832 -87.623177 1 0.0 0.0 0.0
18 Forest (Chicago) 41.881832 -87.623177 2 0.0 0.0 0.0
19 Forest (Chicago) 41.881832 -87.623177 3 0.0 0.0 0.0
20 Forest (Chicago) 41.881832 -87.623177 4 0.0 0.0 0.0
21 Forest (Chicago) 41.881832 -87.623177 5 0.0 0.0 0.0
22 Forest (Phoenix) 33.448376 -112.074036 1 0.0 0.0 0.0
23 Forest (Phoenix) 33.448376 -112.074036 2 0.0 0.0 0.0
24 Forest (Phoenix) 33.448376 -112.074036 3 0.0 0.0 0.0
25 Forest (Phoenix) 33.448376 -112.074036 4 0.0 0.0 0.0
26 Forest (Phoenix) 33.448376 -112.074036 5 0.0 0.0 0.0
27 Forest (Dallas) 32.776664 -96.796988 1 0.0 0.0 0.0
28 Forest (Dallas) 32.776664 -96.796988 2 0.0 0.0 0.0
29 Forest (Dallas) 32.776664 -96.796988 3 0.0 0.0 0.0
30 Forest (Dallas) 32.776664 -96.796988 4 0.0 0.0 0.0
31 Forest (Dallas) 32.776664 -96.796988 5 0.0 0.0 0.0
32 Retail (Chicago) 41.881832 -87.623177 1 NewBoat 0.0 0.0 125000.0
33 Retail (Chicago) 41.881832 -87.623177 2 NewBoat 0.0 0.0 125000.0
34 Retail (Chicago) 41.881832 -87.623177 3 NewBoat 0.0 0.0 125000.0
35 Retail (Chicago) 41.881832 -87.623177 4 NewBoat 0.0 0.0 125000.0
36 Retail (Chicago) 41.881832 -87.623177 5 NewBoat 0.0 0.0 125000.0
37 Retail (New York City) 40.712776 -74.005974 1 NewBoat 0.0 0.0 125000.0
38 Retail (New York City) 40.712776 -74.005974 2 NewBoat 0.0 0.0 125000.0
39 Retail (New York City) 40.712776 -74.005974 3 NewBoat 0.0 0.0 125000.0
40 Retail (New York City) 40.712776 -74.005974 4 NewBoat 0.0 0.0 125000.0
41 Retail (New York City) 40.712776 -74.005974 5 NewBoat 0.0 0.0 125000.0
42 Retail (Los Angeles) 34.052235 -118.243683 1 NewBoat 0.0 0.0 125000.0
43 Retail (Los Angeles) 34.052235 -118.243683 2 NewBoat 0.0 0.0 125000.0
44 Retail (Los Angeles) 34.052235 -118.243683 3 NewBoat 0.0 0.0 125000.0
45 Retail (Los Angeles) 34.052235 -118.243683 4 NewBoat 0.0 0.0 125000.0
46 Retail (Los Angeles) 34.052235 -118.243683 5 NewBoat 0.0 0.0 125000.0
47 Retail (Houston) 29.760427 -95.369804 1 NewBoat 0.0 0.0 125000.0
48 Retail (Houston) 29.760427 -95.369804 2 NewBoat 0.0 0.0 125000.0
49 Retail (Houston) 29.760427 -95.369804 3 NewBoat 0.0 0.0 125000.0
50 Retail (Houston) 29.760427 -95.369804 4 NewBoat 0.0 0.0 125000.0
51 Retail (Houston) 29.760427 -95.369804 5 NewBoat 0.0 0.0 125000.0
52 Retail (Phoenix) 33.448376 -112.074036 1 NewBoat 0.0 0.0 125000.0
53 Retail (Phoenix) 33.448376 -112.074036 2 NewBoat 0.0 0.0 125000.0
54 Retail (Phoenix) 33.448376 -112.074036 3 NewBoat 0.0 0.0 125000.0
55 Retail (Phoenix) 33.448376 -112.074036 4 NewBoat 0.0 0.0 125000.0
56 Retail (Phoenix) 33.448376 -112.074036 5 NewBoat 0.0 0.0 125000.0
57 Retail (Philadelphia) 39.952583 -75.165222 1 NewBoat 0.0 0.0 125000.0
58 Retail (Philadelphia) 39.952583 -75.165222 2 NewBoat 0.0 0.0 125000.0
59 Retail (Philadelphia) 39.952583 -75.165222 3 NewBoat 0.0 0.0 125000.0
60 Retail (Philadelphia) 39.952583 -75.165222 4 NewBoat 0.0 0.0 125000.0
61 Retail (Philadelphia) 39.952583 -75.165222 5 NewBoat 0.0 0.0 125000.0
62 Retail (San Antonio) 29.424122 -98.493629 1 NewBoat 0.0 0.0 125000.0
63 Retail (San Antonio) 29.424122 -98.493629 2 NewBoat 0.0 0.0 125000.0
64 Retail (San Antonio) 29.424122 -98.493629 3 NewBoat 0.0 0.0 125000.0
65 Retail (San Antonio) 29.424122 -98.493629 4 NewBoat 0.0 0.0 125000.0
66 Retail (San Antonio) 29.424122 -98.493629 5 NewBoat 0.0 0.0 125000.0
67 Retail (San Diego) 32.715736 -117.161087 1 NewBoat 0.0 0.0 125000.0
68 Retail (San Diego) 32.715736 -117.161087 2 NewBoat 0.0 0.0 125000.0
69 Retail (San Diego) 32.715736 -117.161087 3 NewBoat 0.0 0.0 125000.0
70 Retail (San Diego) 32.715736 -117.161087 4 NewBoat 0.0 0.0 125000.0
71 Retail (San Diego) 32.715736 -117.161087 5 NewBoat 0.0 0.0 125000.0
72 Retail (Dallas) 32.776664 -96.796988 1 NewBoat 63.15789 757894.73684 125000.0
73 Retail (Dallas) 32.776664 -96.796988 2 NewBoat 71.46814 857617.72853 125000.0
74 Retail (Dallas) 32.776664 -96.796988 3 NewBoat 75.8857 910628.37148 125000.0
75 Retail (Dallas) 32.776664 -96.796988 4 NewBoat 76.90434 922852.03459 125000.0
76 Retail (Dallas) 32.776664 -96.796988 5 NewBoat 77.27087 927250.44516 125000.0
77 Retail (San Jose) 37.338208 -121.886329 1 NewBoat 0.0 0.0 125000.0
78 Retail (San Jose) 37.338208 -121.886329 2 NewBoat 0.0 0.0 125000.0
79 Retail (San Jose) 37.338208 -121.886329 3 NewBoat 0.0 0.0 125000.0
80 Retail (San Jose) 37.338208 -121.886329 4 NewBoat 0.0 0.0 125000.0
81 Retail (San Jose) 37.338208 -121.886329 5 NewBoat 0.0 0.0 125000.0

View File

@@ -1,11 +0,0 @@
plant,latitude,longitude,emission,year,processed amount (tonne),emission factor (tonne/tonne),emissions amount (tonne)
BoatFactory (Dallas),32.776664,-96.796988,CO2,1,63.15789,5.0,315.78947
BoatFactory (Dallas),32.776664,-96.796988,CO2,2,71.46814,5.0,357.34072
BoatFactory (Dallas),32.776664,-96.796988,CO2,3,75.8857,5.0,379.42849
BoatFactory (Dallas),32.776664,-96.796988,CO2,4,76.90434,5.0,384.52168
BoatFactory (Dallas),32.776664,-96.796988,CO2,5,77.27087,5.0,386.35435
RecyclingPlant (Dallas),32.776664,-96.796988,CO2,1,6.31579,5.0,31.57895
RecyclingPlant (Dallas),32.776664,-96.796988,CO2,2,22.93629,5.0,114.68144
RecyclingPlant (Dallas),32.776664,-96.796988,CO2,3,31.7714,5.0,158.85698
RecyclingPlant (Dallas),32.776664,-96.796988,CO2,4,33.80867,5.0,169.04336
RecyclingPlant (Dallas),32.776664,-96.796988,CO2,5,34.54174,5.0,172.7087
1 plant latitude longitude emission year processed amount (tonne) emission factor (tonne/tonne) emissions amount (tonne)
2 BoatFactory (Dallas) 32.776664 -96.796988 CO2 1 63.15789 5.0 315.78947
3 BoatFactory (Dallas) 32.776664 -96.796988 CO2 2 71.46814 5.0 357.34072
4 BoatFactory (Dallas) 32.776664 -96.796988 CO2 3 75.8857 5.0 379.42849
5 BoatFactory (Dallas) 32.776664 -96.796988 CO2 4 76.90434 5.0 384.52168
6 BoatFactory (Dallas) 32.776664 -96.796988 CO2 5 77.27087 5.0 386.35435
7 RecyclingPlant (Dallas) 32.776664 -96.796988 CO2 1 6.31579 5.0 31.57895
8 RecyclingPlant (Dallas) 32.776664 -96.796988 CO2 2 22.93629 5.0 114.68144
9 RecyclingPlant (Dallas) 32.776664 -96.796988 CO2 3 31.7714 5.0 158.85698
10 RecyclingPlant (Dallas) 32.776664 -96.796988 CO2 4 33.80867 5.0 169.04336
11 RecyclingPlant (Dallas) 32.776664 -96.796988 CO2 5 34.54174 5.0 172.7087

View File

@@ -1,151 +0,0 @@
plant,latitude,longitude,input product,year,amount received (tonne),current storage level (tonne),storage limit (tonne),storage cost ($)
BoatFactory (Chicago),41.881832,-87.623177,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (New York City),40.712776,-74.005974,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (Houston),29.760427,-95.369804,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,Nail,5,0.0,0.0,1.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Wood,1,60.0,0.0,5.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Wood,2,67.89474,0.0,5.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Wood,3,72.09141,0.0,5.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Wood,4,73.05912,0.0,5.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Wood,5,73.40733,0.0,5.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Nail,1,3.15789,0.0,1.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Nail,2,3.57341,0.0,1.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Nail,3,3.79428,0.0,1.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Nail,4,3.84522,0.0,1.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,Nail,5,3.86354,0.0,1.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Wood,1,0.0,0.0,5.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Wood,2,0.0,0.0,5.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Wood,3,0.0,0.0,5.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Wood,4,0.0,0.0,5.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Wood,5,0.0,0.0,5.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Nail,1,0.0,0.0,1.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Nail,2,0.0,0.0,1.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Nail,3,0.0,0.0,1.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Nail,4,0.0,0.0,1.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,Nail,5,0.0,0.0,1.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,UsedBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,UsedBoat,1,6.31579,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,UsedBoat,2,22.93629,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,UsedBoat,3,31.7714,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,UsedBoat,4,33.80867,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,UsedBoat,5,34.54174,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,UsedBoat,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,UsedBoat,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,UsedBoat,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,UsedBoat,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,UsedBoat,5,0.0,0.0,0.0,0.0
1 plant latitude longitude input product year amount received (tonne) current storage level (tonne) storage limit (tonne) storage cost ($)
2 BoatFactory (Chicago) 41.881832 -87.623177 Wood 1 0.0 0.0 5.0 0.0
3 BoatFactory (Chicago) 41.881832 -87.623177 Wood 2 0.0 0.0 5.0 0.0
4 BoatFactory (Chicago) 41.881832 -87.623177 Wood 3 0.0 0.0 5.0 0.0
5 BoatFactory (Chicago) 41.881832 -87.623177 Wood 4 0.0 0.0 5.0 0.0
6 BoatFactory (Chicago) 41.881832 -87.623177 Wood 5 0.0 0.0 5.0 0.0
7 BoatFactory (Chicago) 41.881832 -87.623177 Nail 1 0.0 0.0 1.0 0.0
8 BoatFactory (Chicago) 41.881832 -87.623177 Nail 2 0.0 0.0 1.0 0.0
9 BoatFactory (Chicago) 41.881832 -87.623177 Nail 3 0.0 0.0 1.0 0.0
10 BoatFactory (Chicago) 41.881832 -87.623177 Nail 4 0.0 0.0 1.0 0.0
11 BoatFactory (Chicago) 41.881832 -87.623177 Nail 5 0.0 0.0 1.0 0.0
12 BoatFactory (New York City) 40.712776 -74.005974 Wood 1 0.0 0.0 5.0 0.0
13 BoatFactory (New York City) 40.712776 -74.005974 Wood 2 0.0 0.0 5.0 0.0
14 BoatFactory (New York City) 40.712776 -74.005974 Wood 3 0.0 0.0 5.0 0.0
15 BoatFactory (New York City) 40.712776 -74.005974 Wood 4 0.0 0.0 5.0 0.0
16 BoatFactory (New York City) 40.712776 -74.005974 Wood 5 0.0 0.0 5.0 0.0
17 BoatFactory (New York City) 40.712776 -74.005974 Nail 1 0.0 0.0 1.0 0.0
18 BoatFactory (New York City) 40.712776 -74.005974 Nail 2 0.0 0.0 1.0 0.0
19 BoatFactory (New York City) 40.712776 -74.005974 Nail 3 0.0 0.0 1.0 0.0
20 BoatFactory (New York City) 40.712776 -74.005974 Nail 4 0.0 0.0 1.0 0.0
21 BoatFactory (New York City) 40.712776 -74.005974 Nail 5 0.0 0.0 1.0 0.0
22 BoatFactory (Los Angeles) 34.052235 -118.243683 Wood 1 0.0 0.0 5.0 0.0
23 BoatFactory (Los Angeles) 34.052235 -118.243683 Wood 2 0.0 0.0 5.0 0.0
24 BoatFactory (Los Angeles) 34.052235 -118.243683 Wood 3 0.0 0.0 5.0 0.0
25 BoatFactory (Los Angeles) 34.052235 -118.243683 Wood 4 0.0 0.0 5.0 0.0
26 BoatFactory (Los Angeles) 34.052235 -118.243683 Wood 5 0.0 0.0 5.0 0.0
27 BoatFactory (Los Angeles) 34.052235 -118.243683 Nail 1 0.0 0.0 1.0 0.0
28 BoatFactory (Los Angeles) 34.052235 -118.243683 Nail 2 0.0 0.0 1.0 0.0
29 BoatFactory (Los Angeles) 34.052235 -118.243683 Nail 3 0.0 0.0 1.0 0.0
30 BoatFactory (Los Angeles) 34.052235 -118.243683 Nail 4 0.0 0.0 1.0 0.0
31 BoatFactory (Los Angeles) 34.052235 -118.243683 Nail 5 0.0 0.0 1.0 0.0
32 BoatFactory (Houston) 29.760427 -95.369804 Wood 1 0.0 0.0 5.0 0.0
33 BoatFactory (Houston) 29.760427 -95.369804 Wood 2 0.0 0.0 5.0 0.0
34 BoatFactory (Houston) 29.760427 -95.369804 Wood 3 0.0 0.0 5.0 0.0
35 BoatFactory (Houston) 29.760427 -95.369804 Wood 4 0.0 0.0 5.0 0.0
36 BoatFactory (Houston) 29.760427 -95.369804 Wood 5 0.0 0.0 5.0 0.0
37 BoatFactory (Houston) 29.760427 -95.369804 Nail 1 0.0 0.0 1.0 0.0
38 BoatFactory (Houston) 29.760427 -95.369804 Nail 2 0.0 0.0 1.0 0.0
39 BoatFactory (Houston) 29.760427 -95.369804 Nail 3 0.0 0.0 1.0 0.0
40 BoatFactory (Houston) 29.760427 -95.369804 Nail 4 0.0 0.0 1.0 0.0
41 BoatFactory (Houston) 29.760427 -95.369804 Nail 5 0.0 0.0 1.0 0.0
42 BoatFactory (Phoenix) 33.448376 -112.074036 Wood 1 0.0 0.0 5.0 0.0
43 BoatFactory (Phoenix) 33.448376 -112.074036 Wood 2 0.0 0.0 5.0 0.0
44 BoatFactory (Phoenix) 33.448376 -112.074036 Wood 3 0.0 0.0 5.0 0.0
45 BoatFactory (Phoenix) 33.448376 -112.074036 Wood 4 0.0 0.0 5.0 0.0
46 BoatFactory (Phoenix) 33.448376 -112.074036 Wood 5 0.0 0.0 5.0 0.0
47 BoatFactory (Phoenix) 33.448376 -112.074036 Nail 1 0.0 0.0 1.0 0.0
48 BoatFactory (Phoenix) 33.448376 -112.074036 Nail 2 0.0 0.0 1.0 0.0
49 BoatFactory (Phoenix) 33.448376 -112.074036 Nail 3 0.0 0.0 1.0 0.0
50 BoatFactory (Phoenix) 33.448376 -112.074036 Nail 4 0.0 0.0 1.0 0.0
51 BoatFactory (Phoenix) 33.448376 -112.074036 Nail 5 0.0 0.0 1.0 0.0
52 BoatFactory (Philadelphia) 39.952583 -75.165222 Wood 1 0.0 0.0 5.0 0.0
53 BoatFactory (Philadelphia) 39.952583 -75.165222 Wood 2 0.0 0.0 5.0 0.0
54 BoatFactory (Philadelphia) 39.952583 -75.165222 Wood 3 0.0 0.0 5.0 0.0
55 BoatFactory (Philadelphia) 39.952583 -75.165222 Wood 4 0.0 0.0 5.0 0.0
56 BoatFactory (Philadelphia) 39.952583 -75.165222 Wood 5 0.0 0.0 5.0 0.0
57 BoatFactory (Philadelphia) 39.952583 -75.165222 Nail 1 0.0 0.0 1.0 0.0
58 BoatFactory (Philadelphia) 39.952583 -75.165222 Nail 2 0.0 0.0 1.0 0.0
59 BoatFactory (Philadelphia) 39.952583 -75.165222 Nail 3 0.0 0.0 1.0 0.0
60 BoatFactory (Philadelphia) 39.952583 -75.165222 Nail 4 0.0 0.0 1.0 0.0
61 BoatFactory (Philadelphia) 39.952583 -75.165222 Nail 5 0.0 0.0 1.0 0.0
62 BoatFactory (San Antonio) 29.424122 -98.493629 Wood 1 0.0 0.0 5.0 0.0
63 BoatFactory (San Antonio) 29.424122 -98.493629 Wood 2 0.0 0.0 5.0 0.0
64 BoatFactory (San Antonio) 29.424122 -98.493629 Wood 3 0.0 0.0 5.0 0.0
65 BoatFactory (San Antonio) 29.424122 -98.493629 Wood 4 0.0 0.0 5.0 0.0
66 BoatFactory (San Antonio) 29.424122 -98.493629 Wood 5 0.0 0.0 5.0 0.0
67 BoatFactory (San Antonio) 29.424122 -98.493629 Nail 1 0.0 0.0 1.0 0.0
68 BoatFactory (San Antonio) 29.424122 -98.493629 Nail 2 0.0 0.0 1.0 0.0
69 BoatFactory (San Antonio) 29.424122 -98.493629 Nail 3 0.0 0.0 1.0 0.0
70 BoatFactory (San Antonio) 29.424122 -98.493629 Nail 4 0.0 0.0 1.0 0.0
71 BoatFactory (San Antonio) 29.424122 -98.493629 Nail 5 0.0 0.0 1.0 0.0
72 BoatFactory (San Diego) 32.715736 -117.161087 Wood 1 0.0 0.0 5.0 0.0
73 BoatFactory (San Diego) 32.715736 -117.161087 Wood 2 0.0 0.0 5.0 0.0
74 BoatFactory (San Diego) 32.715736 -117.161087 Wood 3 0.0 0.0 5.0 0.0
75 BoatFactory (San Diego) 32.715736 -117.161087 Wood 4 0.0 0.0 5.0 0.0
76 BoatFactory (San Diego) 32.715736 -117.161087 Wood 5 0.0 0.0 5.0 0.0
77 BoatFactory (San Diego) 32.715736 -117.161087 Nail 1 0.0 0.0 1.0 0.0
78 BoatFactory (San Diego) 32.715736 -117.161087 Nail 2 0.0 0.0 1.0 0.0
79 BoatFactory (San Diego) 32.715736 -117.161087 Nail 3 0.0 0.0 1.0 0.0
80 BoatFactory (San Diego) 32.715736 -117.161087 Nail 4 0.0 0.0 1.0 0.0
81 BoatFactory (San Diego) 32.715736 -117.161087 Nail 5 0.0 0.0 1.0 0.0
82 BoatFactory (Dallas) 32.776664 -96.796988 Wood 1 60.0 0.0 5.0 0.0
83 BoatFactory (Dallas) 32.776664 -96.796988 Wood 2 67.89474 0.0 5.0 0.0
84 BoatFactory (Dallas) 32.776664 -96.796988 Wood 3 72.09141 0.0 5.0 0.0
85 BoatFactory (Dallas) 32.776664 -96.796988 Wood 4 73.05912 0.0 5.0 0.0
86 BoatFactory (Dallas) 32.776664 -96.796988 Wood 5 73.40733 0.0 5.0 0.0
87 BoatFactory (Dallas) 32.776664 -96.796988 Nail 1 3.15789 0.0 1.0 0.0
88 BoatFactory (Dallas) 32.776664 -96.796988 Nail 2 3.57341 0.0 1.0 0.0
89 BoatFactory (Dallas) 32.776664 -96.796988 Nail 3 3.79428 0.0 1.0 0.0
90 BoatFactory (Dallas) 32.776664 -96.796988 Nail 4 3.84522 0.0 1.0 0.0
91 BoatFactory (Dallas) 32.776664 -96.796988 Nail 5 3.86354 0.0 1.0 0.0
92 BoatFactory (San Jose) 37.338208 -121.886329 Wood 1 0.0 0.0 5.0 0.0
93 BoatFactory (San Jose) 37.338208 -121.886329 Wood 2 0.0 0.0 5.0 0.0
94 BoatFactory (San Jose) 37.338208 -121.886329 Wood 3 0.0 0.0 5.0 0.0
95 BoatFactory (San Jose) 37.338208 -121.886329 Wood 4 0.0 0.0 5.0 0.0
96 BoatFactory (San Jose) 37.338208 -121.886329 Wood 5 0.0 0.0 5.0 0.0
97 BoatFactory (San Jose) 37.338208 -121.886329 Nail 1 0.0 0.0 1.0 0.0
98 BoatFactory (San Jose) 37.338208 -121.886329 Nail 2 0.0 0.0 1.0 0.0
99 BoatFactory (San Jose) 37.338208 -121.886329 Nail 3 0.0 0.0 1.0 0.0
100 BoatFactory (San Jose) 37.338208 -121.886329 Nail 4 0.0 0.0 1.0 0.0
101 BoatFactory (San Jose) 37.338208 -121.886329 Nail 5 0.0 0.0 1.0 0.0
102 RecyclingPlant (Chicago) 41.881832 -87.623177 UsedBoat 1 0.0 0.0 0.0 0.0
103 RecyclingPlant (Chicago) 41.881832 -87.623177 UsedBoat 2 0.0 0.0 0.0 0.0
104 RecyclingPlant (Chicago) 41.881832 -87.623177 UsedBoat 3 0.0 0.0 0.0 0.0
105 RecyclingPlant (Chicago) 41.881832 -87.623177 UsedBoat 4 0.0 0.0 0.0 0.0
106 RecyclingPlant (Chicago) 41.881832 -87.623177 UsedBoat 5 0.0 0.0 0.0 0.0
107 RecyclingPlant (New York City) 40.712776 -74.005974 UsedBoat 1 0.0 0.0 0.0 0.0
108 RecyclingPlant (New York City) 40.712776 -74.005974 UsedBoat 2 0.0 0.0 0.0 0.0
109 RecyclingPlant (New York City) 40.712776 -74.005974 UsedBoat 3 0.0 0.0 0.0 0.0
110 RecyclingPlant (New York City) 40.712776 -74.005974 UsedBoat 4 0.0 0.0 0.0 0.0
111 RecyclingPlant (New York City) 40.712776 -74.005974 UsedBoat 5 0.0 0.0 0.0 0.0
112 RecyclingPlant (Los Angeles) 34.052235 -118.243683 UsedBoat 1 0.0 0.0 0.0 0.0
113 RecyclingPlant (Los Angeles) 34.052235 -118.243683 UsedBoat 2 0.0 0.0 0.0 0.0
114 RecyclingPlant (Los Angeles) 34.052235 -118.243683 UsedBoat 3 0.0 0.0 0.0 0.0
115 RecyclingPlant (Los Angeles) 34.052235 -118.243683 UsedBoat 4 0.0 0.0 0.0 0.0
116 RecyclingPlant (Los Angeles) 34.052235 -118.243683 UsedBoat 5 0.0 0.0 0.0 0.0
117 RecyclingPlant (Houston) 29.760427 -95.369804 UsedBoat 1 0.0 0.0 0.0 0.0
118 RecyclingPlant (Houston) 29.760427 -95.369804 UsedBoat 2 0.0 0.0 0.0 0.0
119 RecyclingPlant (Houston) 29.760427 -95.369804 UsedBoat 3 0.0 0.0 0.0 0.0
120 RecyclingPlant (Houston) 29.760427 -95.369804 UsedBoat 4 0.0 0.0 0.0 0.0
121 RecyclingPlant (Houston) 29.760427 -95.369804 UsedBoat 5 0.0 0.0 0.0 0.0
122 RecyclingPlant (Phoenix) 33.448376 -112.074036 UsedBoat 1 0.0 0.0 0.0 0.0
123 RecyclingPlant (Phoenix) 33.448376 -112.074036 UsedBoat 2 0.0 0.0 0.0 0.0
124 RecyclingPlant (Phoenix) 33.448376 -112.074036 UsedBoat 3 0.0 0.0 0.0 0.0
125 RecyclingPlant (Phoenix) 33.448376 -112.074036 UsedBoat 4 0.0 0.0 0.0 0.0
126 RecyclingPlant (Phoenix) 33.448376 -112.074036 UsedBoat 5 0.0 0.0 0.0 0.0
127 RecyclingPlant (Philadelphia) 39.952583 -75.165222 UsedBoat 1 0.0 0.0 0.0 0.0
128 RecyclingPlant (Philadelphia) 39.952583 -75.165222 UsedBoat 2 0.0 0.0 0.0 0.0
129 RecyclingPlant (Philadelphia) 39.952583 -75.165222 UsedBoat 3 0.0 0.0 0.0 0.0
130 RecyclingPlant (Philadelphia) 39.952583 -75.165222 UsedBoat 4 0.0 0.0 0.0 0.0
131 RecyclingPlant (Philadelphia) 39.952583 -75.165222 UsedBoat 5 0.0 0.0 0.0 0.0
132 RecyclingPlant (San Antonio) 29.424122 -98.493629 UsedBoat 1 0.0 0.0 0.0 0.0
133 RecyclingPlant (San Antonio) 29.424122 -98.493629 UsedBoat 2 0.0 0.0 0.0 0.0
134 RecyclingPlant (San Antonio) 29.424122 -98.493629 UsedBoat 3 0.0 0.0 0.0 0.0
135 RecyclingPlant (San Antonio) 29.424122 -98.493629 UsedBoat 4 0.0 0.0 0.0 0.0
136 RecyclingPlant (San Antonio) 29.424122 -98.493629 UsedBoat 5 0.0 0.0 0.0 0.0
137 RecyclingPlant (San Diego) 32.715736 -117.161087 UsedBoat 1 0.0 0.0 0.0 0.0
138 RecyclingPlant (San Diego) 32.715736 -117.161087 UsedBoat 2 0.0 0.0 0.0 0.0
139 RecyclingPlant (San Diego) 32.715736 -117.161087 UsedBoat 3 0.0 0.0 0.0 0.0
140 RecyclingPlant (San Diego) 32.715736 -117.161087 UsedBoat 4 0.0 0.0 0.0 0.0
141 RecyclingPlant (San Diego) 32.715736 -117.161087 UsedBoat 5 0.0 0.0 0.0 0.0
142 RecyclingPlant (Dallas) 32.776664 -96.796988 UsedBoat 1 6.31579 0.0 0.0 0.0
143 RecyclingPlant (Dallas) 32.776664 -96.796988 UsedBoat 2 22.93629 0.0 0.0 0.0
144 RecyclingPlant (Dallas) 32.776664 -96.796988 UsedBoat 3 31.7714 0.0 0.0 0.0
145 RecyclingPlant (Dallas) 32.776664 -96.796988 UsedBoat 4 33.80867 0.0 0.0 0.0
146 RecyclingPlant (Dallas) 32.776664 -96.796988 UsedBoat 5 34.54174 0.0 0.0 0.0
147 RecyclingPlant (San Jose) 37.338208 -121.886329 UsedBoat 1 0.0 0.0 0.0 0.0
148 RecyclingPlant (San Jose) 37.338208 -121.886329 UsedBoat 2 0.0 0.0 0.0 0.0
149 RecyclingPlant (San Jose) 37.338208 -121.886329 UsedBoat 3 0.0 0.0 0.0 0.0
150 RecyclingPlant (San Jose) 37.338208 -121.886329 UsedBoat 4 0.0 0.0 0.0 0.0
151 RecyclingPlant (San Jose) 37.338208 -121.886329 UsedBoat 5 0.0 0.0 0.0 0.0

View File

@@ -1,151 +0,0 @@
plant,latitude,longitude,output product,year,amount produced (tonne),amount disposed (tonne),disposal limit (tonne),disposal cost ($)
BoatFactory (Chicago),41.881832,-87.623177,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,NewBoat,5,0.0,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,NewBoat,1,63.15789,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,NewBoat,2,71.46814,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,NewBoat,3,75.8857,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,NewBoat,4,76.90434,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,NewBoat,5,77.27087,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,NewBoat,1,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,NewBoat,2,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,NewBoat,3,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,NewBoat,4,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,NewBoat,5,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,Wood,5,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Nail,1,0.15789,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Nail,2,0.57341,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Nail,3,0.79428,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Nail,4,0.84522,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Nail,5,0.86354,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Wood,1,3.0,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Wood,2,10.89474,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Wood,3,15.09141,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Wood,4,16.05912,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,Wood,5,16.40733,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Nail,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Nail,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Nail,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Nail,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Nail,5,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Wood,1,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Wood,2,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Wood,3,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Wood,4,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,Wood,5,0.0,0.0,0.0,0.0
1 plant latitude longitude output product year amount produced (tonne) amount disposed (tonne) disposal limit (tonne) disposal cost ($)
2 BoatFactory (Chicago) 41.881832 -87.623177 NewBoat 1 0.0 0.0 0.0 0.0
3 BoatFactory (Chicago) 41.881832 -87.623177 NewBoat 2 0.0 0.0 0.0 0.0
4 BoatFactory (Chicago) 41.881832 -87.623177 NewBoat 3 0.0 0.0 0.0 0.0
5 BoatFactory (Chicago) 41.881832 -87.623177 NewBoat 4 0.0 0.0 0.0 0.0
6 BoatFactory (Chicago) 41.881832 -87.623177 NewBoat 5 0.0 0.0 0.0 0.0
7 BoatFactory (New York City) 40.712776 -74.005974 NewBoat 1 0.0 0.0 0.0 0.0
8 BoatFactory (New York City) 40.712776 -74.005974 NewBoat 2 0.0 0.0 0.0 0.0
9 BoatFactory (New York City) 40.712776 -74.005974 NewBoat 3 0.0 0.0 0.0 0.0
10 BoatFactory (New York City) 40.712776 -74.005974 NewBoat 4 0.0 0.0 0.0 0.0
11 BoatFactory (New York City) 40.712776 -74.005974 NewBoat 5 0.0 0.0 0.0 0.0
12 BoatFactory (Los Angeles) 34.052235 -118.243683 NewBoat 1 0.0 0.0 0.0 0.0
13 BoatFactory (Los Angeles) 34.052235 -118.243683 NewBoat 2 0.0 0.0 0.0 0.0
14 BoatFactory (Los Angeles) 34.052235 -118.243683 NewBoat 3 0.0 0.0 0.0 0.0
15 BoatFactory (Los Angeles) 34.052235 -118.243683 NewBoat 4 0.0 0.0 0.0 0.0
16 BoatFactory (Los Angeles) 34.052235 -118.243683 NewBoat 5 0.0 0.0 0.0 0.0
17 BoatFactory (Houston) 29.760427 -95.369804 NewBoat 1 0.0 0.0 0.0 0.0
18 BoatFactory (Houston) 29.760427 -95.369804 NewBoat 2 0.0 0.0 0.0 0.0
19 BoatFactory (Houston) 29.760427 -95.369804 NewBoat 3 0.0 0.0 0.0 0.0
20 BoatFactory (Houston) 29.760427 -95.369804 NewBoat 4 0.0 0.0 0.0 0.0
21 BoatFactory (Houston) 29.760427 -95.369804 NewBoat 5 0.0 0.0 0.0 0.0
22 BoatFactory (Phoenix) 33.448376 -112.074036 NewBoat 1 0.0 0.0 0.0 0.0
23 BoatFactory (Phoenix) 33.448376 -112.074036 NewBoat 2 0.0 0.0 0.0 0.0
24 BoatFactory (Phoenix) 33.448376 -112.074036 NewBoat 3 0.0 0.0 0.0 0.0
25 BoatFactory (Phoenix) 33.448376 -112.074036 NewBoat 4 0.0 0.0 0.0 0.0
26 BoatFactory (Phoenix) 33.448376 -112.074036 NewBoat 5 0.0 0.0 0.0 0.0
27 BoatFactory (Philadelphia) 39.952583 -75.165222 NewBoat 1 0.0 0.0 0.0 0.0
28 BoatFactory (Philadelphia) 39.952583 -75.165222 NewBoat 2 0.0 0.0 0.0 0.0
29 BoatFactory (Philadelphia) 39.952583 -75.165222 NewBoat 3 0.0 0.0 0.0 0.0
30 BoatFactory (Philadelphia) 39.952583 -75.165222 NewBoat 4 0.0 0.0 0.0 0.0
31 BoatFactory (Philadelphia) 39.952583 -75.165222 NewBoat 5 0.0 0.0 0.0 0.0
32 BoatFactory (San Antonio) 29.424122 -98.493629 NewBoat 1 0.0 0.0 0.0 0.0
33 BoatFactory (San Antonio) 29.424122 -98.493629 NewBoat 2 0.0 0.0 0.0 0.0
34 BoatFactory (San Antonio) 29.424122 -98.493629 NewBoat 3 0.0 0.0 0.0 0.0
35 BoatFactory (San Antonio) 29.424122 -98.493629 NewBoat 4 0.0 0.0 0.0 0.0
36 BoatFactory (San Antonio) 29.424122 -98.493629 NewBoat 5 0.0 0.0 0.0 0.0
37 BoatFactory (San Diego) 32.715736 -117.161087 NewBoat 1 0.0 0.0 0.0 0.0
38 BoatFactory (San Diego) 32.715736 -117.161087 NewBoat 2 0.0 0.0 0.0 0.0
39 BoatFactory (San Diego) 32.715736 -117.161087 NewBoat 3 0.0 0.0 0.0 0.0
40 BoatFactory (San Diego) 32.715736 -117.161087 NewBoat 4 0.0 0.0 0.0 0.0
41 BoatFactory (San Diego) 32.715736 -117.161087 NewBoat 5 0.0 0.0 0.0 0.0
42 BoatFactory (Dallas) 32.776664 -96.796988 NewBoat 1 63.15789 0.0 0.0 0.0
43 BoatFactory (Dallas) 32.776664 -96.796988 NewBoat 2 71.46814 0.0 0.0 0.0
44 BoatFactory (Dallas) 32.776664 -96.796988 NewBoat 3 75.8857 0.0 0.0 0.0
45 BoatFactory (Dallas) 32.776664 -96.796988 NewBoat 4 76.90434 0.0 0.0 0.0
46 BoatFactory (Dallas) 32.776664 -96.796988 NewBoat 5 77.27087 0.0 0.0 0.0
47 BoatFactory (San Jose) 37.338208 -121.886329 NewBoat 1 0.0 0.0 0.0 0.0
48 BoatFactory (San Jose) 37.338208 -121.886329 NewBoat 2 0.0 0.0 0.0 0.0
49 BoatFactory (San Jose) 37.338208 -121.886329 NewBoat 3 0.0 0.0 0.0 0.0
50 BoatFactory (San Jose) 37.338208 -121.886329 NewBoat 4 0.0 0.0 0.0 0.0
51 BoatFactory (San Jose) 37.338208 -121.886329 NewBoat 5 0.0 0.0 0.0 0.0
52 RecyclingPlant (Chicago) 41.881832 -87.623177 Nail 1 0.0 0.0 0.0 0.0
53 RecyclingPlant (Chicago) 41.881832 -87.623177 Nail 2 0.0 0.0 0.0 0.0
54 RecyclingPlant (Chicago) 41.881832 -87.623177 Nail 3 0.0 0.0 0.0 0.0
55 RecyclingPlant (Chicago) 41.881832 -87.623177 Nail 4 0.0 0.0 0.0 0.0
56 RecyclingPlant (Chicago) 41.881832 -87.623177 Nail 5 0.0 0.0 0.0 0.0
57 RecyclingPlant (Chicago) 41.881832 -87.623177 Wood 1 0.0 0.0 0.0 0.0
58 RecyclingPlant (Chicago) 41.881832 -87.623177 Wood 2 0.0 0.0 0.0 0.0
59 RecyclingPlant (Chicago) 41.881832 -87.623177 Wood 3 0.0 0.0 0.0 0.0
60 RecyclingPlant (Chicago) 41.881832 -87.623177 Wood 4 0.0 0.0 0.0 0.0
61 RecyclingPlant (Chicago) 41.881832 -87.623177 Wood 5 0.0 0.0 0.0 0.0
62 RecyclingPlant (New York City) 40.712776 -74.005974 Nail 1 0.0 0.0 0.0 0.0
63 RecyclingPlant (New York City) 40.712776 -74.005974 Nail 2 0.0 0.0 0.0 0.0
64 RecyclingPlant (New York City) 40.712776 -74.005974 Nail 3 0.0 0.0 0.0 0.0
65 RecyclingPlant (New York City) 40.712776 -74.005974 Nail 4 0.0 0.0 0.0 0.0
66 RecyclingPlant (New York City) 40.712776 -74.005974 Nail 5 0.0 0.0 0.0 0.0
67 RecyclingPlant (New York City) 40.712776 -74.005974 Wood 1 0.0 0.0 0.0 0.0
68 RecyclingPlant (New York City) 40.712776 -74.005974 Wood 2 0.0 0.0 0.0 0.0
69 RecyclingPlant (New York City) 40.712776 -74.005974 Wood 3 0.0 0.0 0.0 0.0
70 RecyclingPlant (New York City) 40.712776 -74.005974 Wood 4 0.0 0.0 0.0 0.0
71 RecyclingPlant (New York City) 40.712776 -74.005974 Wood 5 0.0 0.0 0.0 0.0
72 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Nail 1 0.0 0.0 0.0 0.0
73 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Nail 2 0.0 0.0 0.0 0.0
74 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Nail 3 0.0 0.0 0.0 0.0
75 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Nail 4 0.0 0.0 0.0 0.0
76 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Nail 5 0.0 0.0 0.0 0.0
77 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Wood 1 0.0 0.0 0.0 0.0
78 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Wood 2 0.0 0.0 0.0 0.0
79 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Wood 3 0.0 0.0 0.0 0.0
80 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Wood 4 0.0 0.0 0.0 0.0
81 RecyclingPlant (Los Angeles) 34.052235 -118.243683 Wood 5 0.0 0.0 0.0 0.0
82 RecyclingPlant (Houston) 29.760427 -95.369804 Nail 1 0.0 0.0 0.0 0.0
83 RecyclingPlant (Houston) 29.760427 -95.369804 Nail 2 0.0 0.0 0.0 0.0
84 RecyclingPlant (Houston) 29.760427 -95.369804 Nail 3 0.0 0.0 0.0 0.0
85 RecyclingPlant (Houston) 29.760427 -95.369804 Nail 4 0.0 0.0 0.0 0.0
86 RecyclingPlant (Houston) 29.760427 -95.369804 Nail 5 0.0 0.0 0.0 0.0
87 RecyclingPlant (Houston) 29.760427 -95.369804 Wood 1 0.0 0.0 0.0 0.0
88 RecyclingPlant (Houston) 29.760427 -95.369804 Wood 2 0.0 0.0 0.0 0.0
89 RecyclingPlant (Houston) 29.760427 -95.369804 Wood 3 0.0 0.0 0.0 0.0
90 RecyclingPlant (Houston) 29.760427 -95.369804 Wood 4 0.0 0.0 0.0 0.0
91 RecyclingPlant (Houston) 29.760427 -95.369804 Wood 5 0.0 0.0 0.0 0.0
92 RecyclingPlant (Phoenix) 33.448376 -112.074036 Nail 1 0.0 0.0 0.0 0.0
93 RecyclingPlant (Phoenix) 33.448376 -112.074036 Nail 2 0.0 0.0 0.0 0.0
94 RecyclingPlant (Phoenix) 33.448376 -112.074036 Nail 3 0.0 0.0 0.0 0.0
95 RecyclingPlant (Phoenix) 33.448376 -112.074036 Nail 4 0.0 0.0 0.0 0.0
96 RecyclingPlant (Phoenix) 33.448376 -112.074036 Nail 5 0.0 0.0 0.0 0.0
97 RecyclingPlant (Phoenix) 33.448376 -112.074036 Wood 1 0.0 0.0 0.0 0.0
98 RecyclingPlant (Phoenix) 33.448376 -112.074036 Wood 2 0.0 0.0 0.0 0.0
99 RecyclingPlant (Phoenix) 33.448376 -112.074036 Wood 3 0.0 0.0 0.0 0.0
100 RecyclingPlant (Phoenix) 33.448376 -112.074036 Wood 4 0.0 0.0 0.0 0.0
101 RecyclingPlant (Phoenix) 33.448376 -112.074036 Wood 5 0.0 0.0 0.0 0.0
102 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Nail 1 0.0 0.0 0.0 0.0
103 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Nail 2 0.0 0.0 0.0 0.0
104 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Nail 3 0.0 0.0 0.0 0.0
105 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Nail 4 0.0 0.0 0.0 0.0
106 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Nail 5 0.0 0.0 0.0 0.0
107 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Wood 1 0.0 0.0 0.0 0.0
108 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Wood 2 0.0 0.0 0.0 0.0
109 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Wood 3 0.0 0.0 0.0 0.0
110 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Wood 4 0.0 0.0 0.0 0.0
111 RecyclingPlant (Philadelphia) 39.952583 -75.165222 Wood 5 0.0 0.0 0.0 0.0
112 RecyclingPlant (San Antonio) 29.424122 -98.493629 Nail 1 0.0 0.0 0.0 0.0
113 RecyclingPlant (San Antonio) 29.424122 -98.493629 Nail 2 0.0 0.0 0.0 0.0
114 RecyclingPlant (San Antonio) 29.424122 -98.493629 Nail 3 0.0 0.0 0.0 0.0
115 RecyclingPlant (San Antonio) 29.424122 -98.493629 Nail 4 0.0 0.0 0.0 0.0
116 RecyclingPlant (San Antonio) 29.424122 -98.493629 Nail 5 0.0 0.0 0.0 0.0
117 RecyclingPlant (San Antonio) 29.424122 -98.493629 Wood 1 0.0 0.0 0.0 0.0
118 RecyclingPlant (San Antonio) 29.424122 -98.493629 Wood 2 0.0 0.0 0.0 0.0
119 RecyclingPlant (San Antonio) 29.424122 -98.493629 Wood 3 0.0 0.0 0.0 0.0
120 RecyclingPlant (San Antonio) 29.424122 -98.493629 Wood 4 0.0 0.0 0.0 0.0
121 RecyclingPlant (San Antonio) 29.424122 -98.493629 Wood 5 0.0 0.0 0.0 0.0
122 RecyclingPlant (San Diego) 32.715736 -117.161087 Nail 1 0.0 0.0 0.0 0.0
123 RecyclingPlant (San Diego) 32.715736 -117.161087 Nail 2 0.0 0.0 0.0 0.0
124 RecyclingPlant (San Diego) 32.715736 -117.161087 Nail 3 0.0 0.0 0.0 0.0
125 RecyclingPlant (San Diego) 32.715736 -117.161087 Nail 4 0.0 0.0 0.0 0.0
126 RecyclingPlant (San Diego) 32.715736 -117.161087 Nail 5 0.0 0.0 0.0 0.0
127 RecyclingPlant (San Diego) 32.715736 -117.161087 Wood 1 0.0 0.0 0.0 0.0
128 RecyclingPlant (San Diego) 32.715736 -117.161087 Wood 2 0.0 0.0 0.0 0.0
129 RecyclingPlant (San Diego) 32.715736 -117.161087 Wood 3 0.0 0.0 0.0 0.0
130 RecyclingPlant (San Diego) 32.715736 -117.161087 Wood 4 0.0 0.0 0.0 0.0
131 RecyclingPlant (San Diego) 32.715736 -117.161087 Wood 5 0.0 0.0 0.0 0.0
132 RecyclingPlant (Dallas) 32.776664 -96.796988 Nail 1 0.15789 0.0 0.0 0.0
133 RecyclingPlant (Dallas) 32.776664 -96.796988 Nail 2 0.57341 0.0 0.0 0.0
134 RecyclingPlant (Dallas) 32.776664 -96.796988 Nail 3 0.79428 0.0 0.0 0.0
135 RecyclingPlant (Dallas) 32.776664 -96.796988 Nail 4 0.84522 0.0 0.0 0.0
136 RecyclingPlant (Dallas) 32.776664 -96.796988 Nail 5 0.86354 0.0 0.0 0.0
137 RecyclingPlant (Dallas) 32.776664 -96.796988 Wood 1 3.0 0.0 0.0 0.0
138 RecyclingPlant (Dallas) 32.776664 -96.796988 Wood 2 10.89474 0.0 0.0 0.0
139 RecyclingPlant (Dallas) 32.776664 -96.796988 Wood 3 15.09141 0.0 0.0 0.0
140 RecyclingPlant (Dallas) 32.776664 -96.796988 Wood 4 16.05912 0.0 0.0 0.0
141 RecyclingPlant (Dallas) 32.776664 -96.796988 Wood 5 16.40733 0.0 0.0 0.0
142 RecyclingPlant (San Jose) 37.338208 -121.886329 Nail 1 0.0 0.0 0.0 0.0
143 RecyclingPlant (San Jose) 37.338208 -121.886329 Nail 2 0.0 0.0 0.0 0.0
144 RecyclingPlant (San Jose) 37.338208 -121.886329 Nail 3 0.0 0.0 0.0 0.0
145 RecyclingPlant (San Jose) 37.338208 -121.886329 Nail 4 0.0 0.0 0.0 0.0
146 RecyclingPlant (San Jose) 37.338208 -121.886329 Nail 5 0.0 0.0 0.0 0.0
147 RecyclingPlant (San Jose) 37.338208 -121.886329 Wood 1 0.0 0.0 0.0 0.0
148 RecyclingPlant (San Jose) 37.338208 -121.886329 Wood 2 0.0 0.0 0.0 0.0
149 RecyclingPlant (San Jose) 37.338208 -121.886329 Wood 3 0.0 0.0 0.0 0.0
150 RecyclingPlant (San Jose) 37.338208 -121.886329 Wood 4 0.0 0.0 0.0 0.0
151 RecyclingPlant (San Jose) 37.338208 -121.886329 Wood 5 0.0 0.0 0.0 0.0

View File

@@ -1,101 +0,0 @@
plant,latitude,longitude,initial capacity,current capacity,year,operational?,input amount (tonne),stored amount (tonne),processed amount (tonne),opening cost ($),fixed operating cost ($),variable operating cost ($),expansion cost ($),storage cost ($)
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,1,true,63.15789,0.0,63.15789,100000.0,250000.0,315.78947,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,2,true,71.46814,0.0,71.46814,0.0,250000.0,357.34072,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,3,true,75.8857,0.0,75.8857,0.0,250000.0,379.42849,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,4,true,76.90434,0.0,76.90434,0.0,250000.0,384.52168,0.0,0.0
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,5,true,77.27087,0.0,77.27087,0.0,250000.0,386.35435,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,1,true,6.31579,0.0,6.31579,500000.0,125000.0,15.78947,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,2,true,22.93629,0.0,22.93629,0.0,125000.0,57.34072,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,3,true,31.7714,0.0,31.7714,0.0,125000.0,79.42849,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,4,true,33.80867,0.0,33.80867,0.0,125000.0,84.52168,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,5,true,34.54174,0.0,34.54174,0.0,125000.0,86.35435,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,1,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,2,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,3,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,4,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,5,false,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
1 plant latitude longitude initial capacity current capacity year operational? input amount (tonne) stored amount (tonne) processed amount (tonne) opening cost ($) fixed operating cost ($) variable operating cost ($) expansion cost ($) storage cost ($)
2 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
5 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
7 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
8 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
9 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
10 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
11 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
12 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
13 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
14 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
15 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
16 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
17 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
18 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
19 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
20 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
21 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
22 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
23 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
24 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
25 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
26 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
27 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
28 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
29 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
30 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
31 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
32 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
33 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
34 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
35 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
36 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
37 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
38 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
39 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
40 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
41 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
42 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 1 true 63.15789 0.0 63.15789 100000.0 250000.0 315.78947 0.0 0.0
43 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 2 true 71.46814 0.0 71.46814 0.0 250000.0 357.34072 0.0 0.0
44 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 3 true 75.8857 0.0 75.8857 0.0 250000.0 379.42849 0.0 0.0
45 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 4 true 76.90434 0.0 76.90434 0.0 250000.0 384.52168 0.0 0.0
46 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 5 true 77.27087 0.0 77.27087 0.0 250000.0 386.35435 0.0 0.0
47 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
48 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
49 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
50 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
51 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
52 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
53 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
54 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
55 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
56 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
57 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
58 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
59 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
60 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
61 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
62 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
63 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
64 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
65 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
66 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
67 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
68 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
69 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
70 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
71 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
72 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
73 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
74 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
75 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
77 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
78 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
79 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
80 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
81 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
82 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
83 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
84 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
85 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
86 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
87 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
88 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
89 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
90 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
91 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
92 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 1 true 6.31579 0.0 6.31579 500000.0 125000.0 15.78947 0.0 0.0
93 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 2 true 22.93629 0.0 22.93629 0.0 125000.0 57.34072 0.0 0.0
94 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 3 true 31.7714 0.0 31.7714 0.0 125000.0 79.42849 0.0 0.0
95 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 4 true 33.80867 0.0 33.80867 0.0 125000.0 84.52168 0.0 0.0
96 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 5 true 34.54174 0.0 34.54174 0.0 125000.0 86.35435 0.0 0.0
97 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 1 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
98 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 2 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
99 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 3 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
100 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 4 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
101 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 5 false 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

View File

@@ -1,81 +0,0 @@
source,destination,product,emission,year,amount sent (tonne),distance (km),emission factor (tonne/km/tonne),emission amount (tonne)
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,CO2,1,0.15789,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,NH4,1,0.15789,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,CO2,2,0.57341,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,NH4,2,0.57341,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,CO2,3,0.79428,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,NH4,3,0.79428,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,CO2,4,0.84522,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,NH4,4,0.84522,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,CO2,5,0.86354,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,NH4,5,0.86354,0.0,1.02,0.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,CO2,1,1.0,1293.093,2.68,3465.48924
NailFactory (Chicago),BoatFactory (Dallas),Nail,NH4,1,1.0,1293.093,1.02,1318.95486
NailFactory (Chicago),BoatFactory (Dallas),Nail,CO2,2,1.0,1293.093,2.68,3465.48924
NailFactory (Chicago),BoatFactory (Dallas),Nail,NH4,2,1.0,1293.093,1.02,1318.95486
NailFactory (Chicago),BoatFactory (Dallas),Nail,CO2,3,1.0,1293.093,2.68,3465.48924
NailFactory (Chicago),BoatFactory (Dallas),Nail,NH4,3,1.0,1293.093,1.02,1318.95486
NailFactory (Chicago),BoatFactory (Dallas),Nail,CO2,4,1.0,1293.093,2.68,3465.48924
NailFactory (Chicago),BoatFactory (Dallas),Nail,NH4,4,1.0,1293.093,1.02,1318.95486
NailFactory (Chicago),BoatFactory (Dallas),Nail,CO2,5,1.0,1293.093,2.68,3465.48924
NailFactory (Chicago),BoatFactory (Dallas),Nail,NH4,5,1.0,1293.093,1.02,1318.95486
NailFactory (Phoenix),BoatFactory (Dallas),Nail,CO2,1,1.0,1423.57,2.68,3815.1676
NailFactory (Phoenix),BoatFactory (Dallas),Nail,NH4,1,1.0,1423.57,1.02,1452.0414
NailFactory (Phoenix),BoatFactory (Dallas),Nail,CO2,2,1.0,1423.57,2.68,3815.1676
NailFactory (Phoenix),BoatFactory (Dallas),Nail,NH4,2,1.0,1423.57,1.02,1452.0414
NailFactory (Phoenix),BoatFactory (Dallas),Nail,CO2,3,1.0,1423.57,2.68,3815.1676
NailFactory (Phoenix),BoatFactory (Dallas),Nail,NH4,3,1.0,1423.57,1.02,1452.0414
NailFactory (Phoenix),BoatFactory (Dallas),Nail,CO2,4,1.0,1423.57,2.68,3815.1676
NailFactory (Phoenix),BoatFactory (Dallas),Nail,NH4,4,1.0,1423.57,1.02,1452.0414
NailFactory (Phoenix),BoatFactory (Dallas),Nail,CO2,5,1.0,1423.57,2.68,3815.1676
NailFactory (Phoenix),BoatFactory (Dallas),Nail,NH4,5,1.0,1423.57,1.02,1452.0414
NailFactory (Dallas),BoatFactory (Dallas),Nail,CO2,1,1.0,0.0,2.68,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,NH4,1,1.0,0.0,1.02,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,CO2,2,1.0,0.0,2.68,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,NH4,2,1.0,0.0,1.02,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,CO2,3,1.0,0.0,2.68,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,NH4,3,1.0,0.0,1.02,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,CO2,4,1.0,0.0,2.68,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,NH4,4,1.0,0.0,1.02,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,CO2,5,1.0,0.0,2.68,0.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,NH4,5,1.0,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,CO2,1,3.0,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,NH4,1,3.0,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,CO2,2,10.89474,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,NH4,2,10.89474,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,CO2,3,15.09141,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,NH4,3,15.09141,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,CO2,4,16.05912,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,NH4,4,16.05912,0.0,1.02,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,CO2,5,16.40733,0.0,2.68,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,NH4,5,16.40733,0.0,1.02,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,CO2,1,57.0,0.0,2.68,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,NH4,1,57.0,0.0,1.02,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,CO2,2,57.0,0.0,2.68,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,NH4,2,57.0,0.0,1.02,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,CO2,3,57.0,0.0,2.68,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,NH4,3,57.0,0.0,1.02,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,CO2,4,57.0,0.0,2.68,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,NH4,4,57.0,0.0,1.02,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,CO2,5,57.0,0.0,2.68,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,NH4,5,57.0,0.0,1.02,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,CO2,1,63.15789,0.0,2.68,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,NH4,1,63.15789,0.0,1.02,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,CO2,2,71.46814,0.0,2.68,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,NH4,2,71.46814,0.0,1.02,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,CO2,3,75.8857,0.0,2.68,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,NH4,3,75.8857,0.0,1.02,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,CO2,4,76.90434,0.0,2.68,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,NH4,4,76.90434,0.0,1.02,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,CO2,5,77.27087,0.0,2.68,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,NH4,5,77.27087,0.0,1.02,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,CO2,1,6.31579,0.0,2.68,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,NH4,1,6.31579,0.0,1.02,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,CO2,2,22.93629,0.0,2.68,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,NH4,2,22.93629,0.0,1.02,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,CO2,3,31.7714,0.0,2.68,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,NH4,3,31.7714,0.0,1.02,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,CO2,4,33.80867,0.0,2.68,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,NH4,4,33.80867,0.0,1.02,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,CO2,5,34.54174,0.0,2.68,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,NH4,5,34.54174,0.0,1.02,0.0
1 source destination product emission year amount sent (tonne) distance (km) emission factor (tonne/km/tonne) emission amount (tonne)
2 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail CO2 1 0.15789 0.0 2.68 0.0
3 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail NH4 1 0.15789 0.0 1.02 0.0
4 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail CO2 2 0.57341 0.0 2.68 0.0
5 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail NH4 2 0.57341 0.0 1.02 0.0
6 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail CO2 3 0.79428 0.0 2.68 0.0
7 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail NH4 3 0.79428 0.0 1.02 0.0
8 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail CO2 4 0.84522 0.0 2.68 0.0
9 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail NH4 4 0.84522 0.0 1.02 0.0
10 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail CO2 5 0.86354 0.0 2.68 0.0
11 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail NH4 5 0.86354 0.0 1.02 0.0
12 NailFactory (Chicago) BoatFactory (Dallas) Nail CO2 1 1.0 1293.093 2.68 3465.48924
13 NailFactory (Chicago) BoatFactory (Dallas) Nail NH4 1 1.0 1293.093 1.02 1318.95486
14 NailFactory (Chicago) BoatFactory (Dallas) Nail CO2 2 1.0 1293.093 2.68 3465.48924
15 NailFactory (Chicago) BoatFactory (Dallas) Nail NH4 2 1.0 1293.093 1.02 1318.95486
16 NailFactory (Chicago) BoatFactory (Dallas) Nail CO2 3 1.0 1293.093 2.68 3465.48924
17 NailFactory (Chicago) BoatFactory (Dallas) Nail NH4 3 1.0 1293.093 1.02 1318.95486
18 NailFactory (Chicago) BoatFactory (Dallas) Nail CO2 4 1.0 1293.093 2.68 3465.48924
19 NailFactory (Chicago) BoatFactory (Dallas) Nail NH4 4 1.0 1293.093 1.02 1318.95486
20 NailFactory (Chicago) BoatFactory (Dallas) Nail CO2 5 1.0 1293.093 2.68 3465.48924
21 NailFactory (Chicago) BoatFactory (Dallas) Nail NH4 5 1.0 1293.093 1.02 1318.95486
22 NailFactory (Phoenix) BoatFactory (Dallas) Nail CO2 1 1.0 1423.57 2.68 3815.1676
23 NailFactory (Phoenix) BoatFactory (Dallas) Nail NH4 1 1.0 1423.57 1.02 1452.0414
24 NailFactory (Phoenix) BoatFactory (Dallas) Nail CO2 2 1.0 1423.57 2.68 3815.1676
25 NailFactory (Phoenix) BoatFactory (Dallas) Nail NH4 2 1.0 1423.57 1.02 1452.0414
26 NailFactory (Phoenix) BoatFactory (Dallas) Nail CO2 3 1.0 1423.57 2.68 3815.1676
27 NailFactory (Phoenix) BoatFactory (Dallas) Nail NH4 3 1.0 1423.57 1.02 1452.0414
28 NailFactory (Phoenix) BoatFactory (Dallas) Nail CO2 4 1.0 1423.57 2.68 3815.1676
29 NailFactory (Phoenix) BoatFactory (Dallas) Nail NH4 4 1.0 1423.57 1.02 1452.0414
30 NailFactory (Phoenix) BoatFactory (Dallas) Nail CO2 5 1.0 1423.57 2.68 3815.1676
31 NailFactory (Phoenix) BoatFactory (Dallas) Nail NH4 5 1.0 1423.57 1.02 1452.0414
32 NailFactory (Dallas) BoatFactory (Dallas) Nail CO2 1 1.0 0.0 2.68 0.0
33 NailFactory (Dallas) BoatFactory (Dallas) Nail NH4 1 1.0 0.0 1.02 0.0
34 NailFactory (Dallas) BoatFactory (Dallas) Nail CO2 2 1.0 0.0 2.68 0.0
35 NailFactory (Dallas) BoatFactory (Dallas) Nail NH4 2 1.0 0.0 1.02 0.0
36 NailFactory (Dallas) BoatFactory (Dallas) Nail CO2 3 1.0 0.0 2.68 0.0
37 NailFactory (Dallas) BoatFactory (Dallas) Nail NH4 3 1.0 0.0 1.02 0.0
38 NailFactory (Dallas) BoatFactory (Dallas) Nail CO2 4 1.0 0.0 2.68 0.0
39 NailFactory (Dallas) BoatFactory (Dallas) Nail NH4 4 1.0 0.0 1.02 0.0
40 NailFactory (Dallas) BoatFactory (Dallas) Nail CO2 5 1.0 0.0 2.68 0.0
41 NailFactory (Dallas) BoatFactory (Dallas) Nail NH4 5 1.0 0.0 1.02 0.0
42 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood CO2 1 3.0 0.0 2.68 0.0
43 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood NH4 1 3.0 0.0 1.02 0.0
44 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood CO2 2 10.89474 0.0 2.68 0.0
45 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood NH4 2 10.89474 0.0 1.02 0.0
46 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood CO2 3 15.09141 0.0 2.68 0.0
47 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood NH4 3 15.09141 0.0 1.02 0.0
48 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood CO2 4 16.05912 0.0 2.68 0.0
49 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood NH4 4 16.05912 0.0 1.02 0.0
50 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood CO2 5 16.40733 0.0 2.68 0.0
51 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood NH4 5 16.40733 0.0 1.02 0.0
52 Forest (Dallas) BoatFactory (Dallas) Wood CO2 1 57.0 0.0 2.68 0.0
53 Forest (Dallas) BoatFactory (Dallas) Wood NH4 1 57.0 0.0 1.02 0.0
54 Forest (Dallas) BoatFactory (Dallas) Wood CO2 2 57.0 0.0 2.68 0.0
55 Forest (Dallas) BoatFactory (Dallas) Wood NH4 2 57.0 0.0 1.02 0.0
56 Forest (Dallas) BoatFactory (Dallas) Wood CO2 3 57.0 0.0 2.68 0.0
57 Forest (Dallas) BoatFactory (Dallas) Wood NH4 3 57.0 0.0 1.02 0.0
58 Forest (Dallas) BoatFactory (Dallas) Wood CO2 4 57.0 0.0 2.68 0.0
59 Forest (Dallas) BoatFactory (Dallas) Wood NH4 4 57.0 0.0 1.02 0.0
60 Forest (Dallas) BoatFactory (Dallas) Wood CO2 5 57.0 0.0 2.68 0.0
61 Forest (Dallas) BoatFactory (Dallas) Wood NH4 5 57.0 0.0 1.02 0.0
62 BoatFactory (Dallas) Retail (Dallas) NewBoat CO2 1 63.15789 0.0 2.68 0.0
63 BoatFactory (Dallas) Retail (Dallas) NewBoat NH4 1 63.15789 0.0 1.02 0.0
64 BoatFactory (Dallas) Retail (Dallas) NewBoat CO2 2 71.46814 0.0 2.68 0.0
65 BoatFactory (Dallas) Retail (Dallas) NewBoat NH4 2 71.46814 0.0 1.02 0.0
66 BoatFactory (Dallas) Retail (Dallas) NewBoat CO2 3 75.8857 0.0 2.68 0.0
67 BoatFactory (Dallas) Retail (Dallas) NewBoat NH4 3 75.8857 0.0 1.02 0.0
68 BoatFactory (Dallas) Retail (Dallas) NewBoat CO2 4 76.90434 0.0 2.68 0.0
69 BoatFactory (Dallas) Retail (Dallas) NewBoat NH4 4 76.90434 0.0 1.02 0.0
70 BoatFactory (Dallas) Retail (Dallas) NewBoat CO2 5 77.27087 0.0 2.68 0.0
71 BoatFactory (Dallas) Retail (Dallas) NewBoat NH4 5 77.27087 0.0 1.02 0.0
72 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat CO2 1 6.31579 0.0 2.68 0.0
73 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat NH4 1 6.31579 0.0 1.02 0.0
74 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat CO2 2 22.93629 0.0 2.68 0.0
75 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat NH4 2 22.93629 0.0 1.02 0.0
76 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat CO2 3 31.7714 0.0 2.68 0.0
77 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat NH4 3 31.7714 0.0 1.02 0.0
78 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat CO2 4 33.80867 0.0 2.68 0.0
79 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat NH4 4 33.80867 0.0 1.02 0.0
80 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat CO2 5 34.54174 0.0 2.68 0.0
81 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat NH4 5 34.54174 0.0 1.02 0.0

View File

@@ -1,41 +0,0 @@
source,destination,product,year,amount sent (tonne),distance (km),transportation cost ($),center revenue ($),center collection cost ($)
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,1,0.15789,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,2,0.57341,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,3,0.79428,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,4,0.84522,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Nail,5,0.86354,0.0,0.0,0.0,0.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,1,1.0,1293.093,387.9279,0.0,1000.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,2,1.0,1293.093,387.9279,0.0,1000.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,3,1.0,1293.093,387.9279,0.0,1000.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,4,1.0,1293.093,387.9279,0.0,1000.0
NailFactory (Chicago),BoatFactory (Dallas),Nail,5,1.0,1293.093,387.9279,0.0,1000.0
NailFactory (Phoenix),BoatFactory (Dallas),Nail,1,1.0,1423.57,427.071,0.0,1000.0
NailFactory (Phoenix),BoatFactory (Dallas),Nail,2,1.0,1423.57,427.071,0.0,1000.0
NailFactory (Phoenix),BoatFactory (Dallas),Nail,3,1.0,1423.57,427.071,0.0,1000.0
NailFactory (Phoenix),BoatFactory (Dallas),Nail,4,1.0,1423.57,427.071,0.0,1000.0
NailFactory (Phoenix),BoatFactory (Dallas),Nail,5,1.0,1423.57,427.071,0.0,1000.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,1,1.0,0.0,0.0,0.0,1000.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,2,1.0,0.0,0.0,0.0,1000.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,3,1.0,0.0,0.0,0.0,1000.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,4,1.0,0.0,0.0,0.0,1000.0
NailFactory (Dallas),BoatFactory (Dallas),Nail,5,1.0,0.0,0.0,0.0,1000.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,1,3.0,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,2,10.89474,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,3,15.09141,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,4,16.05912,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),BoatFactory (Dallas),Wood,5,16.40733,0.0,0.0,0.0,0.0
Forest (Dallas),BoatFactory (Dallas),Wood,1,57.0,0.0,0.0,0.0,14250.0
Forest (Dallas),BoatFactory (Dallas),Wood,2,57.0,0.0,0.0,0.0,14250.0
Forest (Dallas),BoatFactory (Dallas),Wood,3,57.0,0.0,0.0,0.0,14250.0
Forest (Dallas),BoatFactory (Dallas),Wood,4,57.0,0.0,0.0,0.0,14250.0
Forest (Dallas),BoatFactory (Dallas),Wood,5,57.0,0.0,0.0,0.0,14250.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,1,63.15789,0.0,0.0,757894.73684,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,2,71.46814,0.0,0.0,857617.72853,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,3,75.8857,0.0,0.0,910628.37148,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,4,76.90434,0.0,0.0,922852.03459,0.0
BoatFactory (Dallas),Retail (Dallas),NewBoat,5,77.27087,0.0,0.0,927250.44516,0.0
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,1,6.31579,0.0,0.0,0.0,631.57895
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,2,22.93629,0.0,0.0,0.0,2293.62881
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,3,31.7714,0.0,0.0,0.0,3177.13952
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,4,33.80867,0.0,0.0,0.0,3380.86724
Retail (Dallas),RecyclingPlant (Dallas),UsedBoat,5,34.54174,0.0,0.0,0.0,3454.17409
1 source destination product year amount sent (tonne) distance (km) transportation cost ($) center revenue ($) center collection cost ($)
2 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail 1 0.15789 0.0 0.0 0.0 0.0
3 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail 2 0.57341 0.0 0.0 0.0 0.0
4 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail 3 0.79428 0.0 0.0 0.0 0.0
5 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail 4 0.84522 0.0 0.0 0.0 0.0
6 RecyclingPlant (Dallas) BoatFactory (Dallas) Nail 5 0.86354 0.0 0.0 0.0 0.0
7 NailFactory (Chicago) BoatFactory (Dallas) Nail 1 1.0 1293.093 387.9279 0.0 1000.0
8 NailFactory (Chicago) BoatFactory (Dallas) Nail 2 1.0 1293.093 387.9279 0.0 1000.0
9 NailFactory (Chicago) BoatFactory (Dallas) Nail 3 1.0 1293.093 387.9279 0.0 1000.0
10 NailFactory (Chicago) BoatFactory (Dallas) Nail 4 1.0 1293.093 387.9279 0.0 1000.0
11 NailFactory (Chicago) BoatFactory (Dallas) Nail 5 1.0 1293.093 387.9279 0.0 1000.0
12 NailFactory (Phoenix) BoatFactory (Dallas) Nail 1 1.0 1423.57 427.071 0.0 1000.0
13 NailFactory (Phoenix) BoatFactory (Dallas) Nail 2 1.0 1423.57 427.071 0.0 1000.0
14 NailFactory (Phoenix) BoatFactory (Dallas) Nail 3 1.0 1423.57 427.071 0.0 1000.0
15 NailFactory (Phoenix) BoatFactory (Dallas) Nail 4 1.0 1423.57 427.071 0.0 1000.0
16 NailFactory (Phoenix) BoatFactory (Dallas) Nail 5 1.0 1423.57 427.071 0.0 1000.0
17 NailFactory (Dallas) BoatFactory (Dallas) Nail 1 1.0 0.0 0.0 0.0 1000.0
18 NailFactory (Dallas) BoatFactory (Dallas) Nail 2 1.0 0.0 0.0 0.0 1000.0
19 NailFactory (Dallas) BoatFactory (Dallas) Nail 3 1.0 0.0 0.0 0.0 1000.0
20 NailFactory (Dallas) BoatFactory (Dallas) Nail 4 1.0 0.0 0.0 0.0 1000.0
21 NailFactory (Dallas) BoatFactory (Dallas) Nail 5 1.0 0.0 0.0 0.0 1000.0
22 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood 1 3.0 0.0 0.0 0.0 0.0
23 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood 2 10.89474 0.0 0.0 0.0 0.0
24 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood 3 15.09141 0.0 0.0 0.0 0.0
25 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood 4 16.05912 0.0 0.0 0.0 0.0
26 RecyclingPlant (Dallas) BoatFactory (Dallas) Wood 5 16.40733 0.0 0.0 0.0 0.0
27 Forest (Dallas) BoatFactory (Dallas) Wood 1 57.0 0.0 0.0 0.0 14250.0
28 Forest (Dallas) BoatFactory (Dallas) Wood 2 57.0 0.0 0.0 0.0 14250.0
29 Forest (Dallas) BoatFactory (Dallas) Wood 3 57.0 0.0 0.0 0.0 14250.0
30 Forest (Dallas) BoatFactory (Dallas) Wood 4 57.0 0.0 0.0 0.0 14250.0
31 Forest (Dallas) BoatFactory (Dallas) Wood 5 57.0 0.0 0.0 0.0 14250.0
32 BoatFactory (Dallas) Retail (Dallas) NewBoat 1 63.15789 0.0 0.0 757894.73684 0.0
33 BoatFactory (Dallas) Retail (Dallas) NewBoat 2 71.46814 0.0 0.0 857617.72853 0.0
34 BoatFactory (Dallas) Retail (Dallas) NewBoat 3 75.8857 0.0 0.0 910628.37148 0.0
35 BoatFactory (Dallas) Retail (Dallas) NewBoat 4 76.90434 0.0 0.0 922852.03459 0.0
36 BoatFactory (Dallas) Retail (Dallas) NewBoat 5 77.27087 0.0 0.0 927250.44516 0.0
37 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat 1 6.31579 0.0 0.0 0.0 631.57895
38 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat 2 22.93629 0.0 0.0 0.0 2293.62881
39 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat 3 31.7714 0.0 0.0 0.0 3177.13952
40 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat 4 33.80867 0.0 0.0 0.0 3380.86724
41 Retail (Dallas) RecyclingPlant (Dallas) UsedBoat 5 34.54174 0.0 0.0 0.0 3454.17409

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

357
test/fixtures/instances/s1.json vendored Normal file
View File

@@ -0,0 +1,357 @@
{
"parameters": {
"time horizon (years)": 2
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": [
0.015,
0.015
],
"transportation energy (J/km/tonne)": [
0.12,
0.11
],
"transportation emissions (tonne/km/tonne)": {
"CO2": [
0.052,
0.050
],
"CH4": [
0.003,
0.002
]
},
"initial amounts": {
"C1": {
"latitude (deg)": 7.0,
"longitude (deg)": 7.0,
"amount (tonne)": [
934.56,
934.56
]
},
"C2": {
"latitude (deg)": 7.0,
"longitude (deg)": 19.0,
"amount (tonne)": [
198.95,
198.95
]
},
"C3": {
"latitude (deg)": 84.0,
"longitude (deg)": 76.0,
"amount (tonne)": [
212.97,
212.97
]
},
"C4": {
"latitude (deg)": 21.0,
"longitude (deg)": 16.0,
"amount (tonne)": [
352.19,
352.19
]
},
"C5": {
"latitude (deg)": 32.0,
"longitude (deg)": 92.0,
"amount (tonne)": [
510.33,
510.33
]
},
"C6": {
"latitude (deg)": 14.0,
"longitude (deg)": 62.0,
"amount (tonne)": [
471.66,
471.66
]
},
"C7": {
"latitude (deg)": 30.0,
"longitude (deg)": 83.0,
"amount (tonne)": [
785.21,
785.21
]
},
"C8": {
"latitude (deg)": 35.0,
"longitude (deg)": 40.0,
"amount (tonne)": [
706.17,
706.17
]
},
"C9": {
"latitude (deg)": 74.0,
"longitude (deg)": 52.0,
"amount (tonne)": [
30.08,
30.08
]
},
"C10": {
"latitude (deg)": 22.0,
"longitude (deg)": 54.0,
"amount (tonne)": [
536.52,
536.52
]
}
},
"disposal limit (tonne)": [
1.0,
1.0
],
"disposal cost ($/tonne)": [
-1000,
-1000
]
},
"P2": {
"transportation cost ($/km/tonne)": [
0.02,
0.02
]
},
"P3": {
"transportation cost ($/km/tonne)": [
0.0125,
0.0125
]
},
"P4": {
"transportation cost ($/km/tonne)": [
0.0175,
0.0175
]
}
},
"plants": {
"F1": {
"input": "P1",
"outputs (tonne/tonne)": {
"P2": 0.2,
"P3": 0.5
},
"energy (GJ/tonne)": [
0.12,
0.11
],
"emissions (tonne/tonne)": {
"CO2": [
0.052,
0.050
],
"CH4": [
0.003,
0.002
]
},
"locations": {
"L1": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"disposal": {
"P2": {
"cost ($/tonne)": [
-10.0,
-10.0
],
"limit (tonne)": [
1.0,
1.0
]
},
"P3": {
"cost ($/tonne)": [
-10.0,
-10.0
],
"limit (tonne)": [
1.0,
1.0
]
}
},
"capacities (tonne)": {
"250.0": {
"opening cost ($)": [
500.0,
500.0
],
"fixed operating cost ($)": [
30.0,
30.0
],
"variable operating cost ($/tonne)": [
30.0,
30.0
]
},
"1000.0": {
"opening cost ($)": [
1250.0,
1250.0
],
"fixed operating cost ($)": [
30.0,
30.0
],
"variable operating cost ($/tonne)": [
30.0,
30.0
]
}
}
},
"L2": {
"latitude (deg)": 0.5,
"longitude (deg)": 0.5,
"capacities (tonne)": {
"0.0": {
"opening cost ($)": [
1000,
1000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
},
"10000.0": {
"opening cost ($)": [
10000,
10000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
}
}
},
"F2": {
"input": "P2",
"outputs (tonne/tonne)": {
"P3": 0.05,
"P4": 0.80
},
"locations": {
"L3": {
"latitude (deg)": 25.0,
"longitude (deg)": 65.0,
"disposal": {
"P3": {
"cost ($/tonne)": [
100.0,
100.0
]
}
},
"capacities (tonne)": {
"1000.0": {
"opening cost ($)": [
3000,
3000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
},
"L4": {
"latitude (deg)": 0.75,
"longitude (deg)": 0.20,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [
3000,
3000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
}
}
},
"F3": {
"input": "P4",
"locations": {
"L5": {
"latitude (deg)": 100.0,
"longitude (deg)": 100.0,
"capacities (tonne)": {
"15000": {
"opening cost ($)": [
0.0,
0.0
],
"fixed operating cost ($)": [
0.0,
0.0
],
"variable operating cost ($/tonne)": [
-15.0,
-15.0
]
}
}
}
}
},
"F4": {
"input": "P3",
"locations": {
"L6": {
"latitude (deg)": 50.0,
"longitude (deg)": 50.0,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [
0.0,
0.0
],
"fixed operating cost ($)": [
0.0,
0.0
],
"variable operating cost ($/tonne)": [
-15.0,
-15.0
]
}
}
}
}
}
}
}

347
test/fixtures/instances/s2.json vendored Normal file
View File

@@ -0,0 +1,347 @@
{
"parameters": {
"time horizon (years)": 2
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": [
0.015,
0.015
],
"transportation energy (J/km/tonne)": [
0.12,
0.11
],
"transportation emissions (tonne/km/tonne)": {
"CO2": [
0.052,
0.050
],
"CH4": [
0.003,
0.002
]
},
"initial amounts": {
"C1": {
"location": "2018-us-county:17043",
"amount (tonne)": [
934.56,
934.56
]
},
"C2": {
"latitude (deg)": 7.0,
"longitude (deg)": 19.0,
"amount (tonne)": [
198.95,
198.95
]
},
"C3": {
"latitude (deg)": 84.0,
"longitude (deg)": 76.0,
"amount (tonne)": [
212.97,
212.97
]
},
"C4": {
"latitude (deg)": 21.0,
"longitude (deg)": 16.0,
"amount (tonne)": [
352.19,
352.19
]
},
"C5": {
"latitude (deg)": 32.0,
"longitude (deg)": 92.0,
"amount (tonne)": [
510.33,
510.33
]
},
"C6": {
"latitude (deg)": 14.0,
"longitude (deg)": 62.0,
"amount (tonne)": [
471.66,
471.66
]
},
"C7": {
"latitude (deg)": 30.0,
"longitude (deg)": 83.0,
"amount (tonne)": [
785.21,
785.21
]
},
"C8": {
"latitude (deg)": 35.0,
"longitude (deg)": 40.0,
"amount (tonne)": [
706.17,
706.17
]
},
"C9": {
"latitude (deg)": 74.0,
"longitude (deg)": 52.0,
"amount (tonne)": [
30.08,
30.08
]
},
"C10": {
"latitude (deg)": 22.0,
"longitude (deg)": 54.0,
"amount (tonne)": [
536.52,
536.52
]
}
}
},
"P2": {
"transportation cost ($/km/tonne)": [
0.02,
0.02
]
},
"P3": {
"transportation cost ($/km/tonne)": [
0.0125,
0.0125
]
},
"P4": {
"transportation cost ($/km/tonne)": [
0.0175,
0.0175
]
}
},
"plants": {
"F1": {
"input": "P1",
"outputs (tonne/tonne)": {
"P2": 0.2,
"P3": 0.5
},
"energy (GJ/tonne)": [
0.12,
0.11
],
"emissions (tonne/tonne)": {
"CO2": [
0.052,
0.050
],
"CH4": [
0.003,
0.002
]
},
"locations": {
"L1": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"disposal": {
"P2": {
"cost ($/tonne)": [
-10.0,
-10.0
],
"limit (tonne)": [
1.0,
1.0
]
},
"P3": {
"cost ($/tonne)": [
-10.0,
-10.0
],
"limit (tonne)": [
1.0,
1.0
]
}
},
"capacities (tonne)": {
"250.0": {
"opening cost ($)": [
500.0,
500.0
],
"fixed operating cost ($)": [
30.0,
30.0
],
"variable operating cost ($/tonne)": [
30.0,
30.0
]
},
"1000.0": {
"opening cost ($)": [
1250.0,
1250.0
],
"fixed operating cost ($)": [
30.0,
30.0
],
"variable operating cost ($/tonne)": [
30.0,
30.0
]
}
}
},
"L2": {
"location": "2018-us-county:17043",
"capacities (tonne)": {
"0.0": {
"opening cost ($)": [
1000,
1000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
},
"10000.0": {
"opening cost ($)": [
10000,
10000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
}
}
},
"F2": {
"input": "P2",
"outputs (tonne/tonne)": {
"P3": 0.05,
"P4": 0.80
},
"locations": {
"L3": {
"latitude (deg)": 25.0,
"longitude (deg)": 65.0,
"disposal": {
"P3": {
"cost ($/tonne)": [
100.0,
100.0
]
}
},
"capacities (tonne)": {
"1000.0": {
"opening cost ($)": [
3000,
3000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
},
"L4": {
"latitude (deg)": 0.75,
"longitude (deg)": 0.20,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [
3000,
3000
],
"fixed operating cost ($)": [
50.0,
50.0
],
"variable operating cost ($/tonne)": [
50.0,
50.0
]
}
}
}
}
},
"F3": {
"input": "P4",
"locations": {
"L5": {
"latitude (deg)": 100.0,
"longitude (deg)": 100.0,
"capacities (tonne)": {
"15000": {
"opening cost ($)": [
0.0,
0.0
],
"fixed operating cost ($)": [
0.0,
0.0
],
"variable operating cost ($/tonne)": [
-15.0,
-15.0
]
}
}
}
}
},
"F4": {
"input": "P3",
"locations": {
"L6": {
"latitude (deg)": 50.0,
"longitude (deg)": 50.0,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [
0.0,
0.0
],
"fixed operating cost ($)": [
0.0,
0.0
],
"variable operating cost ($/tonne)": [
-15.0,
-15.0
]
}
}
}
}
}
}
}

View File

@@ -0,0 +1,950 @@
{
"Energy": {
"Plants (GJ)": [
568.6368,
521.2504
],
"Transportation (GJ)": [
3.120910400232,
2.860834533546
]
},
"Costs": {
"Variable operating ($)": [
216672.818,
216672.818
],
"Transportation ($)": [
714499.27483131,
714499.27483131
],
"Disposal ($)": [
-20.0,
-20.0
],
"Total ($)": [
939896.86883131,
931282.09283131
],
"Fixed operating ($)": [
130.0,
130.0
],
"Opening ($)": [
4500.0,
0.0
],
"Expansion ($)": [
4114.776,
0.0
]
},
"Plants": {
"F3": {
"L5": {
"Opening cost ($)": [
0.0,
0.0
],
"Emissions (tonne)": {},
"Expansion cost ($)": [
0.0,
0.0
],
"Longitude (deg)": 100.0,
"Energy (GJ)": [
0.0,
0.0
],
"Total output": {},
"Capacity (tonne)": [
15000.0,
15000.0
],
"Latitude (deg)": 100.0,
"Output": {
"Send": {},
"Dispose": {}
},
"Total input (tonne)": [
757.3824000000001,
757.3824000000001
],
"Fixed operating cost ($)": [
0.0,
0.0
],
"Input": {
"F2": {
"L4": {
"Distance (km)": 8811.73,
"Amount (tonne)": [
757.3824000000001,
757.3824000000001
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
116792.36127216002,
116792.36127216002
],
"Longitude (deg)": 0.2,
"Variable operating cost ($)": [
-11360.736,
-11360.736
],
"Latitude (deg)": 0.75,
"Emissions (tonne)": {}
}
}
}
}
},
"F1": {
"L1": {
"Opening cost ($)": [
500.0,
0.0
],
"Emissions (tonne)": {
"CH4": [
3.0,
2.0
],
"CO2": [
52.0,
50.0
]
},
"Expansion cost ($)": [
750.0,
0.0
],
"Longitude (deg)": 0.0,
"Energy (GJ)": [
120.0,
110.0
],
"Total output": {
"P2": [
200.0,
200.0
],
"P3": [
500.0,
500.0
]
},
"Capacity (tonne)": [
1000.0,
1000.0
],
"Latitude (deg)": 0.0,
"Output": {
"Send": {
"P2": {
"F2": {
"L4": {
"Distance (km)": 85.87,
"Amount (tonne)": [
199.0,
199.0
],
"Longitude (deg)": 0.2,
"Latitude (deg)": 0.75
}
}
},
"P3": {
"F4": {
"L6": {
"Distance (km)": 6893.41,
"Amount (tonne)": [
499.0,
499.0
],
"Longitude (deg)": 50.0,
"Latitude (deg)": 50.0
}
}
}
},
"Dispose": {
"P2": {
"Amount (tonne)": [
1.0,
1.0
],
"Cost ($)": [
-10.0,
-10.0
]
},
"P3": {
"Amount (tonne)": [
1.0,
1.0
],
"Cost ($)": [
-10.0,
-10.0
]
}
}
},
"Total input (tonne)": [
1000.0,
1000.0
],
"Fixed operating cost ($)": [
30.0,
30.0
],
"Input": {
"Origin": {
"C3": {
"Distance (km)": 8889.75,
"Amount (tonne)": [
212.97000000000003,
212.97000000000003
],
"Transportation energy (J)": [
227190.00690000004,
208257.50632500005
],
"Transportation cost ($)": [
28398.750862500005,
28398.750862500005
],
"Longitude (deg)": 76.0,
"Variable operating cost ($)": [
6389.1,
6389.1
],
"Latitude (deg)": 84.0,
"Emissions (tonne)": {
"CH4": [
0.6389100000000001,
0.42594000000000004
],
"CO2": [
11.074440000000001,
10.648500000000002
]
}
},
"C7": {
"Distance (km)": 8526.39,
"Amount (tonne)": [
246.62,
246.62
],
"Transportation energy (J)": [
252333.396216,
231305.613198
],
"Transportation cost ($)": [
31541.674527,
31541.674527
],
"Longitude (deg)": 83.0,
"Variable operating cost ($)": [
7398.6,
7398.6
],
"Latitude (deg)": 30.0,
"Emissions (tonne)": {
"CH4": [
0.7398600000000001,
0.49324
],
"CO2": [
12.82424,
12.331000000000001
]
}
},
"C5": {
"Distance (km)": 9148.52,
"Amount (tonne)": [
510.3299999999999,
510.3299999999999
],
"Transportation energy (J)": [
560251.7053919999,
513564.0632759999
],
"Transportation cost ($)": [
70031.46317399999,
70031.46317399999
],
"Longitude (deg)": 92.0,
"Variable operating cost ($)": [
15309.899999999998,
15309.899999999998
],
"Latitude (deg)": 32.0,
"Emissions (tonne)": {
"CH4": [
1.5309899999999999,
1.02066
],
"CO2": [
26.537159999999997,
25.516499999999997
]
}
},
"C9": {
"Distance (km)": 8201.21,
"Amount (tonne)": [
30.08,
30.08
],
"Transportation energy (J)": [
29603.087615999993,
27136.163647999994
],
"Transportation cost ($)": [
3700.385951999999,
3700.385951999999
],
"Longitude (deg)": 52.0,
"Variable operating cost ($)": [
902.4,
902.4
],
"Latitude (deg)": 74.0,
"Emissions (tonne)": {
"CH4": [
0.09024,
0.06016
],
"CO2": [
1.5641599999999998,
1.504
]
}
}
}
}
},
"L2": {
"Opening cost ($)": [
999.9999999999999,
0.0
],
"Emissions (tonne)": {
"CH4": [
11.21592,
7.4772799999999995
],
"CO2": [
194.40928,
186.93200000000002
]
},
"Expansion cost ($)": [
3364.7759999999994,
0.0
],
"Longitude (deg)": 0.5,
"Energy (GJ)": [
448.6368,
411.2504
],
"Total output": {
"P2": [
747.728,
747.728
],
"P3": [
1869.32,
1869.32
]
},
"Capacity (tonne)": [
3738.6399999999994,
3738.6399999999994
],
"Latitude (deg)": 0.5,
"Output": {
"Send": {
"P2": {
"F2": {
"L4": {
"Distance (km)": 43.35,
"Amount (tonne)": [
747.728,
747.728
],
"Longitude (deg)": 0.2,
"Latitude (deg)": 0.75
}
}
},
"P3": {
"F4": {
"L6": {
"Distance (km)": 6828.89,
"Amount (tonne)": [
1869.32,
1869.32
],
"Longitude (deg)": 50.0,
"Latitude (deg)": 50.0
}
}
}
},
"Dispose": {}
},
"Total input (tonne)": [
3738.64,
3738.64
],
"Fixed operating cost ($)": [
50.0,
50.0
],
"Input": {
"Origin": {
"C1": {
"Distance (km)": 1017.44,
"Amount (tonne)": [
934.56,
934.56
],
"Transportation energy (J)": [
114103.047168,
104594.459904
],
"Transportation cost ($)": [
14262.880895999999,
14262.880895999999
],
"Longitude (deg)": 7.0,
"Variable operating cost ($)": [
46728.0,
46728.0
],
"Latitude (deg)": 7.0,
"Emissions (tonne)": {
"CH4": [
2.80368,
1.86912
],
"CO2": [
48.59712,
46.728
]
}
},
"C2": {
"Distance (km)": 2165.47,
"Amount (tonne)": [
198.95,
198.95
],
"Transportation energy (J)": [
51698.430779999995,
47390.228214999996
],
"Transportation cost ($)": [
6462.303847499999,
6462.303847499999
],
"Longitude (deg)": 19.0,
"Variable operating cost ($)": [
9947.5,
9947.5
],
"Latitude (deg)": 7.0,
"Emissions (tonne)": {
"CH4": [
0.59685,
0.3979
],
"CO2": [
10.3454,
9.9475
]
}
},
"C8": {
"Distance (km)": 5421.1,
"Amount (tonne)": [
706.17,
706.17
],
"Transportation energy (J)": [
459386.18243999995,
421104.00057
],
"Transportation cost ($)": [
57423.272805,
57423.272805
],
"Longitude (deg)": 40.0,
"Variable operating cost ($)": [
35308.5,
35308.5
],
"Latitude (deg)": 35.0,
"Emissions (tonne)": {
"CH4": [
2.11851,
1.41234
],
"CO2": [
36.720839999999995,
35.3085
]
}
},
"C6": {
"Distance (km)": 6595.52,
"Amount (tonne)": [
471.66,
471.66
],
"Transportation energy (J)": [
373301.15558400005,
342192.72595200007
],
"Transportation cost ($)": [
46662.64444800001,
46662.64444800001
],
"Longitude (deg)": 62.0,
"Variable operating cost ($)": [
23583.0,
23583.0
],
"Latitude (deg)": 14.0,
"Emissions (tonne)": {
"CH4": [
1.4149800000000001,
0.94332
],
"CO2": [
24.526320000000002,
23.583000000000002
]
}
},
"C10": {
"Distance (km)": 6014.13,
"Amount (tonne)": [
536.52,
536.52
],
"Transportation energy (J)": [
387204.123312,
354937.113036
],
"Transportation cost ($)": [
48400.515413999994,
48400.515413999994
],
"Longitude (deg)": 54.0,
"Variable operating cost ($)": [
26826.0,
26826.0
],
"Latitude (deg)": 22.0,
"Emissions (tonne)": {
"CH4": [
1.6095599999999999,
1.07304
],
"CO2": [
27.89904,
26.826
]
}
},
"C4": {
"Distance (km)": 2802.12,
"Amount (tonne)": [
352.19,
352.19
],
"Transportation energy (J)": [
118425.43713599998,
108556.65070799999
],
"Transportation cost ($)": [
14803.179642,
14803.179642
],
"Longitude (deg)": 16.0,
"Variable operating cost ($)": [
17609.5,
17609.5
],
"Latitude (deg)": 21.0,
"Emissions (tonne)": {
"CH4": [
1.05657,
0.70438
],
"CO2": [
18.313879999999997,
17.6095
]
}
},
"C7": {
"Distance (km)": 8469.86,
"Amount (tonne)": [
538.59,
538.59
],
"Transportation energy (J)": [
547413.827688,
501796.008714
],
"Transportation cost ($)": [
68426.728461,
68426.728461
],
"Longitude (deg)": 83.0,
"Variable operating cost ($)": [
26929.5,
26929.5
],
"Latitude (deg)": 30.0,
"Emissions (tonne)": {
"CH4": [
1.6157700000000002,
1.07718
],
"CO2": [
28.00668,
26.929500000000004
]
}
}
}
}
}
},
"F2": {
"L4": {
"Opening cost ($)": [
2999.9999999999995,
0.0
],
"Emissions (tonne)": {},
"Expansion cost ($)": [
0.0,
0.0
],
"Longitude (deg)": 0.2,
"Energy (GJ)": [
0.0,
0.0
],
"Total output": {
"P4": [
757.3824000000001,
757.3824000000001
],
"P3": [
47.336400000000005,
47.336400000000005
]
},
"Capacity (tonne)": [
10000.0,
10000.0
],
"Latitude (deg)": 0.75,
"Output": {
"Send": {
"P4": {
"F3": {
"L5": {
"Distance (km)": 8811.73,
"Amount (tonne)": [
757.3824000000001,
757.3824000000001
],
"Longitude (deg)": 100.0,
"Latitude (deg)": 100.0
}
}
},
"P3": {
"F4": {
"L6": {
"Distance (km)": 6824.63,
"Amount (tonne)": [
47.336400000000005,
47.336400000000005
],
"Longitude (deg)": 50.0,
"Latitude (deg)": 50.0
}
}
}
},
"Dispose": {}
},
"Total input (tonne)": [
946.728,
946.728
],
"Fixed operating cost ($)": [
50.0,
50.0
],
"Input": {
"F1": {
"L1": {
"Distance (km)": 85.87,
"Amount (tonne)": [
199.0,
199.0
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
341.7626,
341.7626
],
"Longitude (deg)": 0.0,
"Variable operating cost ($)": [
9950.0,
9950.0
],
"Latitude (deg)": 0.0,
"Emissions (tonne)": {}
},
"L2": {
"Distance (km)": 43.35,
"Amount (tonne)": [
747.728,
747.728
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
648.280176,
648.280176
],
"Longitude (deg)": 0.5,
"Variable operating cost ($)": [
37386.399999999994,
37386.399999999994
],
"Latitude (deg)": 0.5,
"Emissions (tonne)": {}
}
}
}
}
},
"F4": {
"L6": {
"Opening cost ($)": [
0.0,
0.0
],
"Emissions (tonne)": {},
"Expansion cost ($)": [
0.0,
0.0
],
"Longitude (deg)": 50.0,
"Energy (GJ)": [
0.0,
0.0
],
"Total output": {},
"Capacity (tonne)": [
10000.0,
10000.0
],
"Latitude (deg)": 50.0,
"Output": {
"Send": {},
"Dispose": {}
},
"Total input (tonne)": [
2415.6564,
2415.6564
],
"Fixed operating cost ($)": [
0.0,
0.0
],
"Input": {
"F1": {
"L1": {
"Distance (km)": 6893.41,
"Amount (tonne)": [
499.0,
499.0
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
42997.644875000005,
42997.644875000005
],
"Longitude (deg)": 0.0,
"Variable operating cost ($)": [
-7485.0,
-7485.0
],
"Latitude (deg)": 0.0,
"Emissions (tonne)": {}
},
"L2": {
"Distance (km)": 6828.89,
"Amount (tonne)": [
1869.32,
1869.32
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
159567.258185,
159567.258185
],
"Longitude (deg)": 0.5,
"Variable operating cost ($)": [
-28039.8,
-28039.8
],
"Latitude (deg)": 0.5,
"Emissions (tonne)": {}
}
},
"F2": {
"L4": {
"Distance (km)": 6824.63,
"Amount (tonne)": [
47.336400000000005,
47.336400000000005
],
"Transportation energy (J)": [
0.0,
0.0
],
"Transportation cost ($)": [
4038.1676941500004,
4038.1676941500004
],
"Longitude (deg)": 0.2,
"Variable operating cost ($)": [
-710.046,
-710.046
],
"Latitude (deg)": 0.75,
"Emissions (tonne)": {}
}
}
}
}
}
},
"Emissions": {
"Transportation (tonne)": {
"CH4": [
14.21592,
9.477279999999999
],
"CO2": [
246.40927999999994,
236.93200000000002
]
},
"Plants (tonne)": {
"CH4": [
14.21592,
9.47728
],
"CO2": [
246.40928,
236.93200000000002
]
}
},
"Products": {
"P1": {
"C1": {
"Marginal cost ($/tonne)": [
133.59,
134.49
]
},
"C2": {
"Marginal cost ($/tonne)": [
150.81,
151.71
]
},
"C3": {
"Marginal cost ($/tonne)": [
250.83,
251.73
]
},
"C8": {
"Marginal cost ($/tonne)": [
199.65,
200.55
]
},
"C6": {
"Marginal cost ($/tonne)": [
217.26,
218.16
]
},
"C10": {
"Marginal cost ($/tonne)": [
208.54,
209.44
]
},
"C4": {
"Marginal cost ($/tonne)": [
160.36,
161.26
]
},
"C5": {
"Marginal cost ($/tonne)": [
254.71,
255.61
]
},
"C7": {
"Marginal cost ($/tonne)": [
245.38,
246.28
]
},
"C9": {
"Marginal cost ($/tonne)": [
240.5,
241.4
]
}
}
}
}

202
test/fixtures/s1-wrong-length.json vendored Normal file
View File

@@ -0,0 +1,202 @@
{
"parameters": {
"time horizon (years)": 2
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": [0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.11],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052],
"CH4": [0.003, 0.002]
},
"initial amounts": {
"C1": {
"latitude (deg)": 7.0,
"longitude (deg)": 7.0,
"amount (tonne)": [934.56, 934.56]
},
"C2": {
"latitude (deg)": 7.0,
"longitude (deg)": 19.0,
"amount (tonne)": [198.95, 198.95]
},
"C3": {
"latitude (deg)": 84.0,
"longitude (deg)": 76.0,
"amount (tonne)": [212.97, 212.97]
},
"C4": {
"latitude (deg)": 21.0,
"longitude (deg)": 16.0,
"amount (tonne)": [352.19, 352.19]
},
"C5": {
"latitude (deg)": 32.0,
"longitude (deg)": 92.0,
"amount (tonne)": [510.33, 510.33]
},
"C6": {
"latitude (deg)": 14.0,
"longitude (deg)": 62.0,
"amount (tonne)": [471.66, 471.66]
},
"C7": {
"latitude (deg)": 30.0,
"longitude (deg)": 83.0,
"amount (tonne)": [785.21, 785.21]
},
"C8": {
"latitude (deg)": 35.0,
"longitude (deg)": 40.0,
"amount (tonne)": [706.17, 706.17]
},
"C9": {
"latitude (deg)": 74.0,
"longitude (deg)": 52.0,
"amount (tonne)": [30.08, 30.08]
},
"C10": {
"latitude (deg)": 22.0,
"longitude (deg)": 54.0,
"amount (tonne)": [536.52, 536.52]
}
}
},
"P2": {
"transportation cost ($/km/tonne)": [0.02, 0.02]
},
"P3": {
"transportation cost ($/km/tonne)": [0.0125, 0.0125]
},
"P4": {
"transportation cost ($/km/tonne)": [0.0175, 0.0175]
}
},
"plants": {
"F1": {
"input": "P1",
"outputs (tonne/tonne)": {
"P2": 0.2,
"P3": 0.5
},
"energy (GJ/tonne)": [0.12, 0.11],
"emissions (tonne/tonne)": {
"CO2": [0.052, 0.050],
"CH4": [0.003, 0.002]
},
"locations": {
"L1": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"disposal": {
"P2": {
"cost ($/tonne)": [-10.0, -10.0],
"limit (tonne)": [1.0, 1.0]
},
"P3": {
"cost ($/tonne)": [-10.0, -10.0],
"limit (tonne)": [1.0, 1.0]
}
},
"capacities (tonne)": {
"250.0": {
"opening cost ($)": [500.0, 500.0],
"fixed operating cost ($)": [30.0, 30.0],
"variable operating cost ($/tonne)": [30.0, 30.0]
},
"1000.0": {
"opening cost ($)": [1250.0, 1250.0],
"fixed operating cost ($)": [30.0, 30.0],
"variable operating cost ($/tonne)": [30.0, 30.0]
}
}
},
"L2": {
"latitude (deg)": 0.5,
"longitude (deg)": 0.5,
"capacities (tonne)": {
"0.0": {
"opening cost ($)": [1000, 1000],
"fixed operating cost ($)": [50.0, 50.0],
"variable operating cost ($/tonne)": [50.0, 50.0]
},
"10000.0": {
"opening cost ($)": [10000, 10000],
"fixed operating cost ($)": [50.0, 50.0],
"variable operating cost ($/tonne)": [50.0, 50.0]
}
}
}
}
},
"F2": {
"input": "P2",
"outputs (tonne/tonne)": {
"P3": 0.05,
"P4": 0.80
},
"locations": {
"L3": {
"latitude (deg)": 25.0,
"longitude (deg)": 65.0,
"disposal": {
"P3": {
"cost ($/tonne)": [100.0, 100.0]
}
},
"capacities (tonne)": {
"1000.0": {
"opening cost ($)": [3000, 3000],
"fixed operating cost ($)": [50.0, 50.0],
"variable operating cost ($/tonne)": [50.0, 50.0]
}
}
},
"L4": {
"latitude (deg)": 0.75,
"longitude (deg)": 0.20,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [3000, 3000],
"fixed operating cost ($)": [50.0, 50.0],
"variable operating cost ($/tonne)": [50.0, 50.0]
}
}
}
}
},
"F3": {
"input": "P4",
"locations": {
"L5": {
"latitude (deg)": 100.0,
"longitude (deg)": 100.0,
"capacities (tonne)": {
"15000": {
"opening cost ($)": [0.0, 0.0],
"fixed operating cost ($)": [0.0, 0.0],
"variable operating cost ($/tonne)": [-15.0, -15.0]
}
}
}
}
},
"F4": {
"input": "P3",
"locations": {
"L6": {
"latitude (deg)": 50.0,
"longitude (deg)": 50.0,
"capacities (tonne)": {
"10000": {
"opening cost ($)": [0.0, 0.0],
"fixed operating cost ($)": [0.0, 0.0],
"variable operating cost ($/tonne)": [-15.0, -15.0]
}
}
}
}
}
}
}

View File

@@ -1,169 +0,0 @@
{
"parameters": {
"time horizon (years)": 4,
"building period (years)": [1],
"distance metric": "euclidean"
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": 0.015,
"transportation energy (J/km/tonne)": 0.12,
"transportation emissions (tonne/km/tonne)": {
"CO2": 0.052,
"CH4": [0.003, 0.003, 0.003, 0.003]
},
"disposal limit (tonne)": 1.0
},
"P2": {
"transportation cost ($/km/tonne)": [0.015, 0.015, 0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.12, 0.12, 0.12],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.052, 0.052, 0.052],
"CH4": [0.003, 0.003, 0.003, 0.003]
},
"disposal limit (tonne)": 2.0
},
"P3": {
"transportation cost ($/km/tonne)": [0.015, 0.015, 0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.12, 0.12, 0.12],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.052, 0.052, 0.052],
"CH4": [0.003, 0.003, 0.003, 0.003]
},
"disposal limit (tonne)": 5.0
},
"P4": {
"transportation cost ($/km/tonne)": [0.015, 0.015, 0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.12, 0.12, 0.12],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.052, 0.052, 0.052],
"CH4": [0.003, 0.003, 0.003, 0.003]
},
"disposal limit (tonne)": null
}
},
"centers": {
"C1": {
"latitude (deg)": 41.881,
"longitude (deg)": -87.623,
"input": "P1",
"outputs": ["P2", "P3"],
"fixed output (tonne)": {
"P2": [100, 50, 0, 0],
"P3": [20, 10, 0, 0]
},
"variable output (tonne/tonne)": {
"P2": [0.20, 0.25, 0.12],
"P3": [0.25, 0.25, 0.25]
},
"revenue ($/tonne)": 12.0,
"collection cost ($/tonne)": {
"P2": [0.25, 0.25, 0.25, 0.25],
"P3": [0.37, 0.37, 0.37, 0.37]
},
"operating cost ($)": [150.0, 150.0, 150.0, 150.0],
"disposal limit (tonne)": {
"P2": [0, 0, 0, 0],
"P3": [null, null, null, null]
},
"disposal cost ($/tonne)": {
"P2": [0.23, 0.23, 0.23, 0.23],
"P3": [1.0, 1.0, 1.0, 1.0]
}
},
"C2": {
"latitude (deg)": 42.881,
"longitude (deg)": -87.623,
"input": null,
"outputs": ["P1"],
"variable output (tonne/tonne)": {
"P1": 0
},
"fixed output (tonne)": {
"P1": [50, 60, 70, 80]
},
"revenue ($/tonne)": null,
"collection cost ($/tonne)": {
"P1": 0.25
},
"operating cost ($)": [150.0, 150.0, 150.0, 150.0],
"disposal limit (tonne)": {
"P1": null
},
"disposal cost ($/tonne)": {
"P1": 0
}
},
"C3": {
"latitude (deg)": 43.881,
"longitude (deg)": -87.623,
"input": "P4",
"outputs": [],
"variable output (tonne/tonne)": {},
"constant output (tonne)": {},
"revenue ($/tonne)": [12.0, 12.0, 12.0, 12.0],
"collection cost ($/tonne)": {},
"operating cost ($)": 150.0,
"disposal limit (tonne)": {},
"disposal cost ($/tonne)": {}
}
},
"plants": {
"L1": {
"latitude (deg)": 44.881,
"longitude (deg)": -87.623,
"input mix (%)": {
"P1": 95.3,
"P2": 4.7
},
"output (tonne)": {
"P3": 0.25,
"P4": 0.12
},
"processing emissions (tonne)": {
"CO2": 0.1
},
"storage cost ($/tonne)": {
"P1": 0.1,
"P2": 0.1
},
"storage limit (tonne)": {
"P1": 100,
"P2": null
},
"disposal cost ($/tonne)": {
"P3": 0,
"P4": 0.86
},
"disposal limit (tonne)": {
"P3": null,
"P4": 1000.0
},
"capacities": [
{
"size (tonne)": 100,
"opening cost ($)": [300, 400, 450, 475],
"fixed operating cost ($)": 300,
"variable operating cost ($/tonne)": 5.0
},
{
"size (tonne)": 500,
"opening cost ($)": 1000.0,
"fixed operating cost ($)": 400.0,
"variable operating cost ($/tonne)": 5.0
}
],
"initial capacity (tonne)": 250
}
},
"emissions": {
"CO2": {
"limit (tonne)": [1000.0, 1100.0, 1200.0, 1300.0],
"penalty ($/tonne)": [50.0, 55.0, 60.0, 65.0]
},
"CH4": {
"limit (tonne)": null,
"penalty ($/tonne)": 1200.0
}
}
}

39
test/fixtures/storage.json vendored Normal file
View File

@@ -0,0 +1,39 @@
{
"parameters": {
"time horizon (years)": 3
},
"products": {
"battery": {
"initial amounts": {
"Chicago": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"amount (tonne)": [100.0, 0.0, 0.0]
}
},
"transportation cost ($/km/tonne)": [0.01, 0.01, 0.01]
}
},
"plants": {
"mega plant": {
"input": "battery",
"locations": {
"Chicago": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"storage": {
"cost ($/tonne)": [2.0, 1.5, 1.0],
"limit (tonne)": 50.0
},
"capacities (tonne)": {
"100": {
"opening cost ($)": [0.0, 0.0, 0],
"fixed operating cost ($)": [0.0, 0.0, 0.0],
"variable operating cost ($/tonne)": [10.0, 5.0, 2.0]
}
}
}
}
}
}
}

40
test/graph/build_test.jl Normal file
View File

@@ -0,0 +1,40 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
function graph_build_test()
@testset "build_graph" begin
instance = RELOG.parsefile(fixture("instances/s1.json"))
graph = RELOG.build_graph(instance)
process_node_by_location_name =
Dict(n.location.location_name => n for n in graph.process_nodes)
@test length(graph.plant_shipping_nodes) == 8
@test length(graph.collection_shipping_nodes) == 10
@test length(graph.process_nodes) == 6
node = graph.collection_shipping_nodes[1]
@test node.location.name == "C1"
@test length(node.incoming_arcs) == 0
@test length(node.outgoing_arcs) == 2
@test node.outgoing_arcs[1].source.location.name == "C1"
@test node.outgoing_arcs[1].dest.location.plant_name == "F1"
@test node.outgoing_arcs[1].dest.location.location_name == "L1"
@test node.outgoing_arcs[1].values["distance"] == 1095.62
node = process_node_by_location_name["L1"]
@test node.location.plant_name == "F1"
@test node.location.location_name == "L1"
@test length(node.incoming_arcs) == 10
@test length(node.outgoing_arcs) == 2
node = process_node_by_location_name["L3"]
@test node.location.plant_name == "F2"
@test node.location.location_name == "L3"
@test length(node.incoming_arcs) == 2
@test length(node.outgoing_arcs) == 2
@test length(graph.arcs) == 38
end
end

View File

@@ -0,0 +1,54 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
function compress_test()
@testset "compress" begin
instance = RELOG.parsefile(fixture("instances/s1.json"))
compressed = RELOG._compress(instance)
product_name_to_product = Dict(p.name => p for p in compressed.products)
location_name_to_facility = Dict()
for p in compressed.plants
location_name_to_facility[p.location_name] = p
end
for c in compressed.collection_centers
location_name_to_facility[c.name] = c
end
p1 = product_name_to_product["P1"]
p2 = product_name_to_product["P2"]
p3 = product_name_to_product["P3"]
c1 = location_name_to_facility["C1"]
l1 = location_name_to_facility["L1"]
@test compressed.time == 1
@test compressed.building_period == [1]
@test p1.name == "P1"
@test p1.transportation_cost [0.015]
@test p1.transportation_energy [0.115]
@test p1.transportation_emissions["CO2"] [0.051]
@test p1.transportation_emissions["CH4"] [0.0025]
@test c1.name == "C1"
@test c1.amount [1869.12]
@test l1.plant_name == "F1"
@test l1.location_name == "L1"
@test l1.energy [0.115]
@test l1.emissions["CO2"] [0.051]
@test l1.emissions["CH4"] [0.0025]
@test l1.sizes[1].opening_cost [500]
@test l1.sizes[2].opening_cost [1250]
@test l1.sizes[1].fixed_operating_cost [60]
@test l1.sizes[2].fixed_operating_cost [60]
@test l1.sizes[1].variable_operating_cost [30]
@test l1.sizes[2].variable_operating_cost [30]
@test l1.disposal_limit[p2] [2.0]
@test l1.disposal_limit[p3] [2.0]
@test l1.disposal_cost[p2] [-10.0]
@test l1.disposal_cost[p3] [-10.0]
end
end

View File

@@ -0,0 +1,27 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using RELOG
function geodb_test()
@testset "geodb_query (2018-us-county)" begin
region = RELOG.geodb_query("2018-us-county:17043")
@test region.centroid.lat == 41.83956
@test region.centroid.lon == -88.08857
@test region.population == 922_921
end
# @testset "geodb_query (2018-us-zcta)" begin
# region = RELOG.geodb_query("2018-us-zcta:60439")
# @test region.centroid.lat == 41.68241
# @test region.centroid.lon == -87.98954
# end
@testset "geodb_query (us-state)" begin
region = RELOG.geodb_query("us-state:IL")
@test region.centroid.lat == 39.73939
@test region.centroid.lon == -89.50414
@test region.population == 12_671_821
end
end

View File

@@ -0,0 +1,92 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
function parse_test()
@testset "parse" begin
instance = RELOG.parsefile(fixture("instances/s1.json"))
centers = instance.collection_centers
plants = instance.plants
products = instance.products
location_name_to_plant = Dict(p.location_name => p for p in plants)
product_name_to_product = Dict(p.name => p for p in products)
@test length(centers) == 10
@test centers[1].name == "C1"
@test centers[1].latitude == 7
@test centers[1].latitude == 7
@test centers[1].longitude == 7
@test centers[1].amount == [934.56, 934.56]
@test centers[1].product.name == "P1"
@test length(plants) == 6
plant = location_name_to_plant["L1"]
@test plant.plant_name == "F1"
@test plant.location_name == "L1"
@test plant.input.name == "P1"
@test plant.latitude == 0
@test plant.longitude == 0
@test length(plant.sizes) == 2
@test plant.sizes[1].capacity == 250
@test plant.sizes[1].opening_cost == [500, 500]
@test plant.sizes[1].fixed_operating_cost == [30, 30]
@test plant.sizes[1].variable_operating_cost == [30, 30]
@test plant.sizes[2].capacity == 1000
@test plant.sizes[2].opening_cost == [1250, 1250]
@test plant.sizes[2].fixed_operating_cost == [30, 30]
@test plant.sizes[2].variable_operating_cost == [30, 30]
p1 = product_name_to_product["P1"]
@test p1.disposal_limit == [1.0, 1.0]
@test p1.disposal_cost == [-1000.0, -1000.0]
p2 = product_name_to_product["P2"]
@test p2.disposal_limit == [0.0, 0.0]
@test p2.disposal_cost == [0.0, 0.0]
p3 = product_name_to_product["P3"]
@test length(plant.output) == 2
@test plant.output[p2] == 0.2
@test plant.output[p3] == 0.5
@test plant.disposal_limit[p2] == [1, 1]
@test plant.disposal_limit[p3] == [1, 1]
@test plant.disposal_cost[p2] == [-10, -10]
@test plant.disposal_cost[p3] == [-10, -10]
plant = location_name_to_plant["L3"]
@test plant.location_name == "L3"
@test plant.input.name == "P2"
@test plant.latitude == 25
@test plant.longitude == 65
@test length(plant.sizes) == 2
@test plant.sizes[1].capacity == 1000.0
@test plant.sizes[1].opening_cost == [3000, 3000]
@test plant.sizes[1].fixed_operating_cost == [50, 50]
@test plant.sizes[1].variable_operating_cost == [50, 50]
@test plant.sizes[1] == plant.sizes[2]
p4 = product_name_to_product["P4"]
@test plant.output[p3] == 0.05
@test plant.output[p4] == 0.8
@test plant.disposal_limit[p3] == [1e8, 1e8]
@test plant.disposal_limit[p4] == [0, 0]
end
@testset "parse (geodb)" begin
instance = RELOG.parsefile(fixture("instances/s2.json"))
centers = instance.collection_centers
@test centers[1].name == "C1"
@test centers[1].latitude == 41.83956
@test centers[1].longitude == -88.08857
end
# @testset "parse (invalid)" begin
# @test_throws ErrorException RELOG.parsefile(fixture("s1-wrong-length.json"))
# end
end

38
test/model/build_test.jl Normal file
View File

@@ -0,0 +1,38 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, HiGHS, JuMP, Printf, JSON, MathOptInterface.FileFormats
function model_build_test()
@testset "build" begin
instance = RELOG.parsefile(fixture("instances/s1.json"))
graph = RELOG.build_graph(instance)
model = RELOG.build_model(instance, graph, HiGHS.Optimizer)
process_node_by_location_name =
Dict(n.location.location_name => n for n in graph.process_nodes)
shipping_node_by_loc_and_prod_names = Dict(
(n.location.location_name, n.product.name) => n for n in graph.plant_shipping_nodes
)
@test length(model[1, :open_plant]) == 12
@test length(model[2, :flow]) == 76
@test length(model[2, :plant_dispose]) == 16
@test length(model[2, :capacity]) == 12
@test length(model[2, :expansion]) == 12
# l1 = process_node_by_location_name["L1"]
# v = model[2, :capacity][l1.index, 1]
# @test lower_bound(v) == 0.0
# @test upper_bound(v) == 1000.0
# v = model[2, :expansion][l1.index, 1]
# @test lower_bound(v) == 0.0
# @test upper_bound(v) == 750.0
# v = model[2, :plant_dispose][shipping_node_by_loc_and_prod_names["L1", "P2"].index, 1]
# @test lower_bound(v) == 0.0
# @test upper_bound(v) == 1.0
end
end

85
test/model/solve_test.jl Normal file
View File

@@ -0,0 +1,85 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, JuMP, Printf, JSON, MathOptInterface.FileFormats
basedir = dirname(@__FILE__)
function model_solve_test()
@testset "solve (exact)" begin
solution = RELOG.solve(fixture("instances/s1.json"))
solution_filename = tempname()
RELOG.write(solution, solution_filename)
@test isfile(solution_filename)
@test "Costs" in keys(solution)
@test "Fixed operating (\$)" in keys(solution["Costs"])
@test "Transportation (\$)" in keys(solution["Costs"])
@test "Variable operating (\$)" in keys(solution["Costs"])
@test "Total (\$)" in keys(solution["Costs"])
@test "Plants" in keys(solution)
@test "F1" in keys(solution["Plants"])
@test "F2" in keys(solution["Plants"])
@test "F3" in keys(solution["Plants"])
@test "F4" in keys(solution["Plants"])
@test "Products" in keys(solution)
@test "P1" in keys(solution["Products"])
@test "C1" in keys(solution["Products"]["P1"])
@test "Dispose (tonne)" in keys(solution["Products"]["P1"]["C1"])
total_disposal =
sum([loc["Dispose (tonne)"] for loc in values(solution["Products"]["P1"])])
@test total_disposal == [1.0, 1.0]
end
@testset "solve (heuristic)" begin
# Should not crash
solution = RELOG.solve(fixture("instances/s1.json"), heuristic = true)
end
# @testset "solve (infeasible)" begin
# json = JSON.parsefile(fixture("instances/s1.json"))
# for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
# location_dict["amount (tonne)"] *= 1000
# end
# @test_throws ErrorException("No solution available") RELOG.solve(RELOG.parse(json))
# end
@testset "solve (with storage)" begin
basedir = dirname(@__FILE__)
filename = "$basedir/../fixtures/storage.json"
instance = RELOG.parsefile(filename)
@test instance.plants[1].storage_limit == 50.0
@test instance.plants[1].storage_cost == [2.0, 1.5, 1.0]
solution = RELOG.solve(filename)
plant_dict = solution["Plants"]["mega plant"]["Chicago"]
@test plant_dict["Variable operating cost (\$)"] == [500.0, 0.0, 100.0]
@test plant_dict["Process (tonne)"] == [50.0, 0.0, 50.0]
@test plant_dict["Storage (tonne)"] == [50.0, 50.0, 0.0]
@test plant_dict["Storage cost (\$)"] == [100.0, 75.0, 0.0]
@test solution["Costs"]["Variable operating (\$)"] == [500.0, 0.0, 100.0]
@test solution["Costs"]["Storage (\$)"] == [100.0, 75.0, 0.0]
@test solution["Costs"]["Total (\$)"] == [600.0, 75.0, 100.0]
end
@testset "solve (stochastic)" begin
# Should not crash
solutions = RELOG.solve_stochastic(
scenarios=[
fixture("instances/case3_p010_s1.00.json"),
fixture("instances/case3_p010_s1.25.json"),
],
probs=[0.5, 0.5],
optimizer=optimizer_with_attributes(
HiGHS.Optimizer,
"log_to_console" => false,
),
method=:lshaped,
)
end
end

23
test/reports_test.jl Normal file
View File

@@ -0,0 +1,23 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using RELOG, JSON, GZip
basedir = @__DIR__
function reports_test()
@testset "Reports" begin
@testset "from solve" begin
solution = RELOG.solve(fixture("instances/s1.json"))
tmp_filename = tempname()
# The following should not crash
RELOG.write_plant_emissions_report(solution, tmp_filename)
RELOG.write_plant_outputs_report(solution, tmp_filename)
RELOG.write_plants_report(solution, tmp_filename)
RELOG.write_products_report(solution, tmp_filename)
RELOG.write_transportation_emissions_report(solution, tmp_filename)
RELOG.write_transportation_report(solution, tmp_filename)
end
end
end

47
test/runtests.jl Normal file
View File

@@ -0,0 +1,47 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using Test
using RELOG
using Revise
includet("instance/compress_test.jl")
includet("instance/geodb_test.jl")
includet("instance/parse_test.jl")
includet("graph/build_test.jl")
includet("model/build_test.jl")
includet("model/solve_test.jl")
includet("reports_test.jl")
function fixture(path)
for candidate in [
"fixtures/$path",
"test/fixtures/$path"
]
if isfile(candidate)
return candidate
end
end
error("Fixture not found: $path")
end
function runtests()
@testset "RELOG" begin
@testset "Instance" begin
compress_test()
geodb_test()
parse_test()
end
@testset "Graph" begin
graph_build_test()
end
@testset "Model" begin
model_build_test()
model_solve_test()
end
reports_test()
end
return
end
runtests()

View File

@@ -1,38 +0,0 @@
module RELOGT
using Test
using RELOG
using JuliaFormatter
include("instance/parse_test.jl")
include("model/build_test.jl")
include("model/dist_test.jl")
include("model/jumpext_test.jl")
include("reports_test.jl")
include("../fixtures/boat_example.jl")
basedir = dirname(@__FILE__)
function fixture(path::String)::String
return "$basedir/../fixtures/$path"
end
function runtests()
@testset "RELOG" begin
instance_parse_test_1()
instance_parse_test_2()
model_build_test()
model_dist_test()
report_tests()
jumpext_test()
end
return
end
function format()
JuliaFormatter.format(basedir, verbose = true)
JuliaFormatter.format("$basedir/../../src", verbose = true)
JuliaFormatter.format("$basedir/../fixtures", verbose = true)
return
end
end # module RELOGT

View File

@@ -1,90 +0,0 @@
using RELOG
using Test
using OrderedCollections
function instance_parse_test_1()
instance = RELOG.parsefile(fixture("simple.json"))
# Parameters
@test instance.time_horizon == 4
@test instance.building_period == [1]
@test instance.distance_metric isa RELOG.EuclideanDistance
# Products
@test length(instance.products) == 4
p1 = instance.products[1]
@test p1.name == "P1"
@test p1.tr_cost == [0.015, 0.015, 0.015, 0.015]
@test p1.tr_energy == [0.12, 0.12, 0.12, 0.12]
@test p1.tr_emissions ==
Dict("CO2" => [0.052, 0.052, 0.052, 0.052], "CH4" => [0.003, 0.003, 0.003, 0.003])
@test p1.disposal_limit == [1.0, 1.0, 1.0, 1.0]
@test instance.products_by_name["P1"] === p1
p2 = instance.products[2]
p3 = instance.products[3]
p4 = instance.products[4]
# Centers
@test length(instance.centers) == 3
c1 = instance.centers[1]
@test c1.latitude == 41.881
@test c1.longitude == -87.623
@test c1.input === p1
@test c1.outputs == [p2, p3]
@test c1.fixed_output == Dict(p2 => [100, 50, 0, 0], p3 => [20, 10, 0, 0])
@test c1.var_output == Dict(p2 => [0.2, 0.25, 0.12], p3 => [0.25, 0.25, 0.25])
@test c1.revenue == [12.0, 12.0, 12.0, 12.0]
@test c1.operating_cost == [150.0, 150.0, 150.0, 150.0]
@test c1.disposal_limit == Dict(p2 => [0, 0, 0, 0], p3 => [Inf, Inf, Inf, Inf])
@test c1.disposal_cost ==
Dict(p2 => [0.23, 0.23, 0.23, 0.23], p3 => [1.0, 1.0, 1.0, 1.0])
c2 = instance.centers[2]
@test c2.input === nothing
@test c2.revenue == [0, 0, 0, 0]
# Plants
@test length(instance.plants) == 1
l1 = instance.plants[1]
@test l1.latitude == 44.881
@test l1.longitude == -87.623
@test l1.input_mix ==
Dict(p1 => [0.953, 0.953, 0.953, 0.953], p2 => [0.047, 0.047, 0.047, 0.047])
@test l1.output == Dict(p3 => [0.25, 0.25, 0.25, 0.25], p4 => [0.12, 0.12, 0.12, 0.12])
@test l1.emissions == Dict("CO2" => [0.1, 0.1, 0.1, 0.1])
@test l1.storage_cost == Dict(p1 => [0.1, 0.1, 0.1, 0.1], p2 => [0.1, 0.1, 0.1, 0.1])
@test l1.storage_limit == Dict(p1 => [100, 100, 100, 100], p2 => [Inf, Inf, Inf, Inf])
@test l1.disposal_cost == Dict(p3 => [0, 0, 0, 0], p4 => [0.86, 0.86, 0.86, 0.86])
@test l1.disposal_limit ==
Dict(p3 => [Inf, Inf, Inf, Inf], p4 => [1000.0, 1000.0, 1000.0, 1000.0])
@test l1.initial_capacity == 250
@test length(l1.capacities) == 2
c1 = l1.capacities[1]
@test c1.size == 100
@test c1.opening_cost == [300, 400, 450, 475]
@test c1.fix_operating_cost == [300, 300, 300, 300]
@test c1.var_operating_cost == [5, 5, 5, 5]
c2 = l1.capacities[2]
@test c2.size == 500
@test c2.opening_cost == [1000, 1000, 1000, 1000]
@test c2.fix_operating_cost == [400, 400, 400, 400]
@test c2.var_operating_cost == [5, 5, 5, 5]
# Emissions
@test length(instance.emissions) == 2
co2 = instance.emissions[1]
@test co2.name == "CO2"
@test co2.limit == [1000.0, 1100.0, 1200.0, 1300.0]
@test co2.penalty == [50.0, 55.0, 60.0, 65.0]
@test instance.emissions_by_name["CO2"] === co2
ch4 = instance.emissions[2]
@test ch4.name == "CH4"
@test ch4.limit == [Inf, Inf, Inf, Inf]
@test ch4.penalty == [1200.0, 1200.0, 1200.0, 1200.0]
@test instance.emissions_by_name["CH4"] === ch4
end
function instance_parse_test_2()
# Should not crash
RELOG.parsefile(fixture("boat_example.json"))
end

View File

@@ -1,216 +0,0 @@
using RELOG
using Test
using HiGHS
using JuMP
function model_build_test()
instance = RELOG.parsefile(fixture("simple.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
y = model[:y]
z_disp = model[:z_disp]
z_input = model[:z_input]
z_process = model[:z_process]
z_storage = model[:z_storage]
z_em_tr = model[:z_em_tr]
z_em_plant = model[:z_em_plant]
z_exp = model[:z_exp]
x = model[:x]
obj = objective_function(model)
# print(model)
@test obj.terms[y["L1", "C3", "P4", 1]] == (
111.118 * 0.015 # transportation
- 12.0 # revenue
)
@test obj.terms[y["C1", "L1", "P2", 4]] == (
333.262 * 0.015 + # transportation
0.25 + # center collection cost
5.0 # plant operating cost
)
@test obj.terms[z_disp["C1", "P2", 1]] == 0.23
@test obj.constant == (
150 * 4 * 3 # center operating cost
- 300 # initial opening cost
- 150 * 1.75 # initial expansion
)
@test obj.terms[z_disp["L1", "P4", 2]] == 0.86
@test obj.terms[x["L1", 1]] == (
-100.0 + # opening cost
300 # fixed operating cost
)
@test obj.terms[x["L1", 2]] == (
-50.0 + # opening cost
300 # fixed operating cost
)
@test obj.terms[x["L1", 3]] == (
-25.0 + # opening cost
300 # fixed operating cost
)
@test obj.terms[x["L1", 4]] == (
475.0 + # opening cost
300 # fixed operating cost
)
# Test expansion variables exist and have correct initial values
@test z_exp["L1", 0] == 150.0 # initial_capacity (250) - min_capacity (100)
@test haskey(z_exp, ("L1", 1))
@test haskey(z_exp, ("L1", 2))
@test haskey(z_exp, ("L1", 3))
@test haskey(z_exp, ("L1", 4))
# Test expansion costs in objective function
# R_expand[1] = (1000 - 300) / (500 - 100) = 1.75
# R_expand[2] = (1000 - 400) / (500 - 100) = 1.5
# R_fix_exp[1] = (400 - 300) / (500 - 100) = 0.25
@test obj.terms[z_exp["L1", 1]] == (
+1.75 # expansion cost[1]
- 1.5 # expansion cost[2]
+ 0.25 # fixed operating cost[1]
)
# Test storage cost in objective function
@test obj.terms[z_storage["L1", "P1", 1]] == 0.1 # P1 storage cost
@test obj.terms[z_storage["L1", "P2", 1]] == 0.1 # P2 storage cost
# Variables: Transportation emissions
@test haskey(z_em_tr, ("CO2", "L1", "C3", "P4", 1))
@test haskey(z_em_tr, ("CH4", "L1", "C3", "P4", 1))
@test haskey(z_em_tr, ("CO2", "C2", "L1", "P1", 1))
@test haskey(z_em_tr, ("CH4", "C2", "L1", "P1", 1))
# Variables: Plant emissions
@test haskey(z_em_plant, ("CO2", "L1", 1))
@test haskey(z_em_plant, ("CO2", "L1", 2))
@test haskey(z_em_plant, ("CO2", "L1", 3))
@test haskey(z_em_plant, ("CO2", "L1", 4))
# Plants: Definition of total plant input
@test repr(model[:eq_z_input]["L1", 1]) ==
"eq_z_input[L1,1] : -y[C2,L1,P1,1] - y[C1,L1,P2,1] + z_input[L1,1] = 0"
# Plants: Definition of total processing amount
@test repr(model[:eq_z_process]["L1", 1]) ==
"eq_z_process[L1,1] : -z_input[L1,1] + z_storage[L1,P1,1] + z_storage[L1,P2,1] + z_process[L1,1] = 0"
# Plants: Processing mix must have correct proportion
@test repr(model[:eq_process_mix]["L1", "P1", 1]) ==
"eq_process_mix[L1,P1,1] : y[C2,L1,P1,1] - z_storage[L1,P1,1] - 0.953 z_process[L1,1] = 0"
@test repr(model[:eq_process_mix]["L1", "P2", 1]) ==
"eq_process_mix[L1,P2,1] : y[C1,L1,P2,1] - z_storage[L1,P2,1] - 0.047 z_process[L1,1] = 0"
# Plants: Calculate amount produced
@test repr(model[:eq_z_prod]["L1", "P3", 1]) ==
"eq_z_prod[L1,P3,1] : z_prod[L1,P3,1] - 0.25 z_process[L1,1] = 0"
@test repr(model[:eq_z_prod]["L1", "P4", 1]) ==
"eq_z_prod[L1,P4,1] : z_prod[L1,P4,1] - 0.12 z_process[L1,1] = 0"
# Plants: Produced material must be sent or disposed
@test repr(model[:eq_balance]["L1", "P3", 1]) ==
"eq_balance[L1,P3,1] : z_prod[L1,P3,1] - z_disp[L1,P3,1] = 0"
@test repr(model[:eq_balance]["L1", "P4", 1]) ==
"eq_balance[L1,P4,1] : -y[L1,C3,P4,1] + z_prod[L1,P4,1] - z_disp[L1,P4,1] = 0"
# Plants: Processing limit (capacity constraint)
@test repr(model[:eq_process_limit]["L1", 1]) ==
"eq_process_limit[L1,1] : -100 x[L1,1] - z_exp[L1,1] + z_process[L1,1] ≤ 0"
# Plants: Expansion upper bound
@test repr(model[:eq_exp_ub]["L1", 1]) ==
"eq_exp_ub[L1,1] : -400 x[L1,1] + z_exp[L1,1] ≤ 0"
# Plants: Disposal limit
@test repr(model[:eq_disposal_limit]["L1", "P4", 1]) ==
"eq_disposal_limit[L1,P4,1] : z_disp[L1,P4,1] ≤ 1000"
@test ("L1", "P3", 1) keys(model[:eq_disposal_limit])
# Plants: Plant remains open
@test repr(model[:eq_keep_open]["L1", 4]) ==
"eq_keep_open[L1,4] : -x[L1,3] + x[L1,4] ≥ 0"
@test repr(model[:eq_keep_open]["L1", 1]) == "eq_keep_open[L1,1] : x[L1,1] ≥ 1"
# Plants: Building period
@test ("L1", 1) keys(model[:eq_building_period])
@test repr(model[:eq_building_period]["L1", 2]) ==
"eq_building_period[L1,2] : -x[L1,1] + x[L1,2] ≤ 0"
# Centers: Definition of total center input
@test repr(model[:eq_z_input]["C1", 1]) ==
"eq_z_input[C1,1] : -y[C2,C1,P1,1] + z_input[C1,1] = 0"
# Centers: Calculate amount collected
@test repr(model[:eq_z_collected]["C1", "P2", 1]) ==
"eq_z_collected[C1,P2,1] : -0.2 z_input[C1,1] + z_collected[C1,P2,1] = 100"
@test repr(model[:eq_z_collected]["C1", "P2", 2]) ==
"eq_z_collected[C1,P2,2] : -0.25 z_input[C1,1] - 0.2 z_input[C1,2] + z_collected[C1,P2,2] = 50"
@test repr(model[:eq_z_collected]["C1", "P2", 3]) ==
"eq_z_collected[C1,P2,3] : -0.12 z_input[C1,1] - 0.25 z_input[C1,2] - 0.2 z_input[C1,3] + z_collected[C1,P2,3] = 0"
@test repr(model[:eq_z_collected]["C1", "P2", 4]) ==
"eq_z_collected[C1,P2,4] : -0.12 z_input[C1,2] - 0.25 z_input[C1,3] - 0.2 z_input[C1,4] + z_collected[C1,P2,4] = 0"
# Centers: Collected products must be disposed or sent
@test repr(model[:eq_balance]["C1", "P2", 1]) ==
"eq_balance[C1,P2,1] : -y[C1,L1,P2,1] - z_disp[C1,P2,1] + z_collected[C1,P2,1] = 0"
@test repr(model[:eq_balance]["C1", "P3", 1]) ==
"eq_balance[C1,P3,1] : -z_disp[C1,P3,1] + z_collected[C1,P3,1] = 0"
# Centers: Disposal limit
@test repr(model[:eq_disposal_limit]["C1", "P2", 1]) ==
"eq_disposal_limit[C1,P2,1] : z_disp[C1,P2,1] ≤ 0"
@test ("C1", "P3", 1) keys(model[:eq_disposal_limit])
# Global disposal limit
@test repr(model[:eq_disposal_limit]["P1", 1]) ==
"eq_disposal_limit[P1,1] : z_disp[C2,P1,1] ≤ 1"
@test repr(model[:eq_disposal_limit]["P2", 1]) ==
"eq_disposal_limit[P2,1] : z_disp[C1,P2,1] ≤ 2"
@test repr(model[:eq_disposal_limit]["P3", 1]) ==
"eq_disposal_limit[P3,1] : z_disp[L1,P3,1] + z_disp[C1,P3,1] ≤ 5"
@test ("P4", 1) keys(model[:eq_disposal_limit])
# Products: Transportation emissions
@test repr(model[:eq_emission_tr]["CH4", "L1", "C3", "P4", 1]) ==
"eq_emission_tr[CH4,L1,C3,P4,1] : -0.333354 y[L1,C3,P4,1] + z_em_tr[CH4,L1,C3,P4,1] = 0"
# Plants: Plant emissions (updated to use z_process)
@test repr(model[:eq_emission_plant]["CO2", "L1", 1]) ==
"eq_emission_plant[CO2,L1,1] : -0.1 z_process[L1,1] + z_em_plant[CO2,L1,1] = 0"
# Objective function: Emissions penalty costs
@test obj.terms[z_em_plant["CO2", "L1", 1]] == 50.0 # CO2 penalty at time 1
@test obj.terms[z_em_plant["CO2", "L1", 2]] == 55.0 # CO2 penalty at time 2
@test obj.terms[z_em_plant["CO2", "L1", 3]] == 60.0 # CO2 penalty at time 3
@test obj.terms[z_em_plant["CO2", "L1", 4]] == 65.0 # CO2 penalty at time 4
@test obj.terms[z_em_tr["CO2", "L1", "C3", "P4", 1]] == 50.0 # CO2 transportation penalty at time 1
@test obj.terms[z_em_tr["CH4", "L1", "C3", "P4", 1]] == 1200.0 # CH4 transportation penalty at time 1
# Global emissions limit constraints
@test repr(model[:eq_emission_limit]["CO2", 1]) ==
"eq_emission_limit[CO2,1] : z_em_tr[CO2,C2,L1,P1,1] + z_em_tr[CO2,C2,C1,P1,1] + z_em_tr[CO2,C1,L1,P2,1] + z_em_tr[CO2,L1,C3,P4,1] + z_em_plant[CO2,L1,1] ≤ 1000"
@test ("CH4", 1) keys(model[:eq_emission_limit])
# Test storage variables exist
@test haskey(z_storage, ("L1", "P1", 1))
@test haskey(z_storage, ("L1", "P2", 1))
@test haskey(z_process, ("L1", 1))
@test haskey(z_process, ("L1", 2))
@test haskey(z_process, ("L1", 3))
@test haskey(z_process, ("L1", 4))
# Test initial storage values
@test z_storage["L1", "P1", 0] == 0
@test z_storage["L1", "P2", 0] == 0
# Test storage limit constraints (P1 has limit of 100, P2 has no limit)
@test haskey(model[:eq_storage_limit], ("L1", "P1", 1))
@test repr(model[:eq_storage_limit]["L1", "P1", 1]) ==
"eq_storage_limit[L1,P1,1] : z_storage[L1,P1,1] ≤ 100"
@test ("L1", "P2", 1) keys(model[:eq_storage_limit]) # P2 has no storage limit
# Test final storage constraints exist
@test haskey(model[:eq_storage_final], ("L1", "P1"))
@test haskey(model[:eq_storage_final], ("L1", "P2"))
@test repr(model[:eq_storage_final]["L1", "P1"]) ==
"eq_storage_final[L1,P1] : z_storage[L1,P1,4] = 0"
@test repr(model[:eq_storage_final]["L1", "P2"]) ==
"eq_storage_final[L1,P2] : z_storage[L1,P2,4] = 0"
end

View File

@@ -1,25 +0,0 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using RELOG
function model_dist_test()
# Euclidean distance between Chicago and Indianapolis
@test RELOG._calculate_distance(
41.866,
-87.656,
39.764,
-86.148,
RELOG.EuclideanDistance(),
) == 265.818
# Driving distance between Chicago and Indianapolis
@test RELOG._calculate_distance(
41.866,
-87.656,
39.764,
-86.148,
RELOG.KnnDrivingDistance(),
) == 316.43
end

View File

@@ -1,144 +0,0 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using RELOG
using JuMP
using HiGHS
using Test
function jumpext_test()
jumpext_pwl_single_point()
jumpext_pwl_two_points()
jumpext_pwl_multiple_points()
jumpext_pwl_input_validation()
return
end
function jumpext_pwl_single_point()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [5.0]
ypts = [10.0 20.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(x) 5.0 atol = 1e-6
@test value(y1) 10.0 atol = 1e-6
@test value(y2) 20.0 atol = 1e-6
return
end
function jumpext_pwl_two_points()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [0.0, 2.0]
ypts = [0.0 10.0; 4.0 6.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
# Test at x = 1
JuMP.fix(x, 1.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 2.0 atol = 1e-6
@test value(y2) 8.0 atol = 1e-6
# Test at x = 2
JuMP.fix(x, 2.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 4.0 atol = 1e-6
@test value(y2) 6.0 atol = 1e-6
return
end
function jumpext_pwl_multiple_points()
model = Model(HiGHS.Optimizer)
set_silent(model)
@variable(model, x)
@variable(model, y1)
@variable(model, y2)
xpts = [0.0, 1.0, 2.0]
ypts = [0.0 5.0; 2.0 3.0; 1.0 4.0]
RELOG._add_pwl_constraints(model, x, [y1, y2], xpts, ypts)
# Test at x = 0.5
JuMP.fix(x, 0.5)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 1.0 atol = 1e-6
@test value(y2) 4.0 atol = 1e-6
# Test at x = 1
JuMP.fix(x, 1.0)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 2.0 atol = 1e-6
@test value(y2) 3.0 atol = 1e-6
# Test at x = 1.5
JuMP.fix(x, 1.5)
optimize!(model)
@test is_solved_and_feasible(model)
@test value(y1) 1.5 atol = 1e-6
@test value(y2) 3.5 atol = 1e-6
return
end
function jumpext_pwl_input_validation()
model = Model(HiGHS.Optimizer)
@variable(model, x)
@variable(model, y)
# Test non-matrix ypts
@test_throws ArgumentError RELOG._add_pwl_constraints(model, x, [y], [1.0], [1.0])
# Test mismatched dimensions
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0, 2.0],
[1.0 2.0],
)
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0],
[1.0 2.0; 3.0 4.0],
)
# Test empty breakpoints
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
Float64[],
Matrix{Float64}(undef, 0, 1),
)
# Test non-increasing x points
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[2.0, 1.0],
[1.0; 2.0],
)
@test_throws ArgumentError RELOG._add_pwl_constraints(
model,
x,
[y],
[1.0, 1.0],
[1.0; 2.0],
)
return
end

View File

@@ -1,14 +0,0 @@
function report_tests()
# Load and solve the boat example
instance = RELOG.parsefile(fixture("boat_example.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
optimize!(model)
mkpath("tmp")
write_to_file(model, "tmp/model.lp")
RELOG.write_plants_report(model, "tmp/plants.csv")
RELOG.write_plant_inputs_report(model, "tmp/plant_inputs.csv")
RELOG.write_plant_outputs_report(model, "tmp/plant_outputs.csv")
RELOG.write_centers_report(model, "tmp/centers.csv")
RELOG.write_center_outputs_report(model, "tmp/center_outputs.csv")
RELOG.write_transportation_report(model, "tmp/transportation.csv")
end

View File

@@ -1 +0,0 @@
FAST_REFRESH=false

25
web/.gitignore vendored
View File

@@ -1,25 +0,0 @@
# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
# dependencies
/node_modules
/.pnp
.pnp.js
# testing
/coverage
# production
/build
# misc
.DS_Store
.env.local
.env.development.local
.env.test.local
.env.production.local
npm-debug.log*
yarn-debug.log*
yarn-error.log*
assets

View File

@@ -1 +0,0 @@
{}

17746
web/package-lock.json generated

File diff suppressed because it is too large Load Diff

View File

@@ -1,65 +0,0 @@
{
"name": "web",
"version": "0.1.0",
"private": true,
"dependencies": {
"@fortawesome/fontawesome-svg-core": "^6.7.2",
"@fortawesome/free-regular-svg-icons": "^6.7.2",
"@fortawesome/free-solid-svg-icons": "^6.7.2",
"@fortawesome/react-fontawesome": "^0.2.2",
"@testing-library/dom": "^10.4.0",
"@testing-library/jest-dom": "^6.6.3",
"@testing-library/react": "^16.3.0",
"@testing-library/user-event": "^13.5.0",
"@types/jest": "^27.5.2",
"@types/node": "^16.18.126",
"@types/pako": "^2.0.3",
"@types/papaparse": "^5.3.16",
"@types/react": "^19.1.3",
"@types/react-dom": "^19.1.3",
"ajv": "^8.17.1",
"eslint": "^8.57.1",
"pako": "^2.1.0",
"papaparse": "^5.5.2",
"react": "^19.1.0",
"react-dom": "^19.1.0",
"react-scripts": "^5.0.1",
"tabulator-tables": "^6.3.1",
"typescript": "^4.9.5",
"web-vitals": "^2.1.4"
},
"scripts": {
"start": "react-scripts start",
"build": "react-scripts build",
"test": "react-scripts test",
"eject": "react-scripts eject"
},
"eslintConfig": {
"extends": [
"react-app",
"react-app/jest"
],
"rules": {
"semi": [
"error",
"always"
]
}
},
"browserslist": {
"production": [
">0.2%",
"not dead",
"not op_mini all"
],
"development": [
"last 1 chrome version",
"last 1 firefox version",
"last 1 safari version"
]
},
"devDependencies": {
"@types/tabulator-tables": "^6.2.6",
"prettier": "3.5.3"
}
}

Binary file not shown.

Before

Width:  |  Height:  |  Size: 3.8 KiB

View File

@@ -1,49 +0,0 @@
<!--
~ RELOG: Supply Chain Analysis and Optimization
~ Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
~ Released under the modified BSD license. See COPYING.md for more details.
-->
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" href="%PUBLIC_URL%/favicon.ico" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="theme-color" content="#000000" />
<meta name="description" content="RELOG Case Builder" />
<link rel="apple-touch-icon" href="%PUBLIC_URL%/logo192.png" />
<link rel="manifest" href="%PUBLIC_URL%/manifest.json" />
<title>Case Builder - RELOG</title>
<style>
:root {
--site-max-width: 1500px;
--site-min-width: 900px;
--box-border: 1px solid rgba(0, 0, 0, 0.2);
--box-shadow: 0px 2px 4px -3px rgba(0, 0, 0, 0.2);
--border-radius: 4px;
--primary: #0d6efd;
--contrast-100: #202020;
--contrast-80: #606060;
--contrast-60: #909090;
--contrast-20: #d6d6d6;
--contrast-10: #f6f6f6;
--contrast-0: #fefefe;
}
body {
margin: 0;
padding: 0;
font-family: Arial, sans-serif;
background-color: #333;
}
.content {
background-color: var(--contrast-10);
padding-bottom: 36px;
}
</style>
</head>
<body>
<noscript>You need to enable JavaScript to run this app.</noscript>
<div id="root"></div>
</body>
</html>

Binary file not shown.

Before

Width:  |  Height:  |  Size: 5.2 KiB

Binary file not shown.

Before

Width:  |  Height:  |  Size: 9.4 KiB

View File

@@ -1,25 +0,0 @@
{
"short_name": "React App",
"name": "Create React App Sample",
"icons": [
{
"src": "favicon.ico",
"sizes": "64x64 32x32 24x24 16x16",
"type": "image/x-icon"
},
{
"src": "logo192.png",
"type": "image/png",
"sizes": "192x192"
},
{
"src": "logo512.png",
"type": "image/png",
"sizes": "512x512"
}
],
"start_url": ".",
"display": "standalone",
"theme_color": "#000000",
"background_color": "#ffffff"
}

View File

@@ -1,3 +0,0 @@
# https://www.robotstxt.org/robotstxt.html
User-agent: *
Disallow:

View File

@@ -1,27 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
import Header from "./Header";
import "tabulator-tables/dist/css/tabulator.min.css";
import "../Common/Forms/Tables.css";
import Footer from "./Footer";
const CaseBuilder = () => {
const onClear = () => {};
const onSave = () => {};
const onLoad = () => {};
return (
<div>
<Header onClear={onClear} onSave={onSave} onLoad={onLoad} />
<div className="content"></div>
<Footer />
</div>
);
};
export default CaseBuilder;

View File

@@ -1,14 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
.Footer {
background-color: #333;
text-align: center;
color: #aaa;
font-size: 14px;
padding: 16px;
line-height: 24px;
}

View File

@@ -1,18 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
import styles from "./Footer.module.css";
function Footer() {
return (
<div className={styles.Footer}>
RELOG: Supply Chain Analysis and Optimization <br />
Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
</div>
);
}
export default Footer;

View File

@@ -1,41 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
.HeaderBox {
background-color: var(--contrast-0);
border-bottom: var(--box-border);
box-shadow: var(--box-shadow);
padding: 0;
margin: 0;
}
.HeaderContent {
margin: 0 auto;
max-width: var(--site-max-width);
min-width: var(--site-min-width);
}
.HeaderContent h1,
h2 {
color: var(--contrast-100);
display: inline-block;
line-height: 48px;
font-size: 28px;
margin: 0;
padding: 12px;
}
.HeaderContent h2 {
display: inline-block;
font-size: 22px;
color: var(--contrast-80);
font-weight: normal;
}
.buttonContainer {
float: right;
padding: 16px 12px;
}

View File

@@ -1,39 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
import styles from "./Header.module.css";
import SiteHeaderButton from "../Common/Buttons/SiteHeaderButton";
import { useRef } from "react";
import FileUploadElement from "../Common/Buttons/FileUploadElement";
interface HeaderProps {
onClear: () => void;
onSave: () => void;
onLoad: () => void;
}
function Header(props: HeaderProps) {
const fileElem = useRef<FileUploadElement>(null);
function onLoad() {}
return (
<div className={styles.HeaderBox}>
<div className={styles.HeaderContent}>
<h1>RELOG</h1>
<h2>Case Builder</h2>
<div className={styles.buttonContainer}>
<SiteHeaderButton title="Clear" onClick={props.onClear} />
<SiteHeaderButton title="Load" onClick={onLoad} />
<SiteHeaderButton title="Save" onClick={props.onSave} />
</div>
<FileUploadElement ref={fileElem} accept=".json,.json.gz" />
</div>
</div>
);
}
export default Header;

View File

@@ -1,58 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
import pako from "pako";
import React, { Component } from "react";
class FileUploadElement extends Component<any> {
private inputRef = React.createRef<HTMLInputElement>();
private callback: (data: any) => void = () => {};
showFilePicker = (callback: (data: any) => void) => {
this.callback = callback;
this.inputRef.current?.click();
};
onFileSelected = (event: React.ChangeEvent<HTMLInputElement>) => {
const file = event.target.files![0]!;
let isCompressed = file.name.endsWith(".gz");
if (file) {
const reader = new FileReader();
reader.onload = async (e) => {
let content = e.target?.result;
if (isCompressed) {
const compressed = new Uint8Array(content as ArrayBuffer);
const decompressed = pako.inflate(compressed);
content = new TextDecoder().decode(decompressed);
}
this.callback(content as string);
this.callback = () => {};
};
if (isCompressed) {
reader.readAsArrayBuffer(file);
} else {
reader.readAsText(file);
}
}
event.target.value = "";
};
override render() {
return (
<input
ref={this.inputRef}
type="file"
accept={this.props.accept}
style={{ display: "none" }}
onChange={this.onFileSelected}
/>
);
}
}
export default FileUploadElement;

View File

@@ -1,43 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
.tooltip {
visibility: hidden;
background-color: var(--contrast-80);
color: var(--contrast-10);
opacity: 0;
width: 250px;
margin-top: 36px;
margin-left: -250px;
position: absolute;
z-index: 100;
font-size: 14px;
border-radius: var(--border-radius);
box-shadow: var(--box-shadow);
line-height: 20px;
transition: opacity 0.5s;
font-weight: normal;
text-align: left;
padding: 6px 12px;
}
.icon {
color: var(--contrast-60);
font-size: 16px;
padding: 8px 8px 8px 0;
}
.HelpButton {
border: 0;
background-color: transparent;
cursor: pointer;
}
.HelpButton:hover .tooltip {
visibility: visible;
opacity: 100%;
transition: opacity 0.5s;
}

View File

@@ -1,22 +0,0 @@
/*
* RELOG: Supply Chain Analysis and Optimization
* Copyright (C) 2020-2025, UChicago Argonne, LLC. All rights reserved.
* Released under the modified BSD license. See COPYING.md for more details.
*/
import styles from "./HelpButton.module.css";
import { FontAwesomeIcon } from "@fortawesome/react-fontawesome";
import { faCircleQuestion } from "@fortawesome/free-regular-svg-icons";
function HelpButton({ text }: { text: String }) {
return (
<button className={styles.HelpButton}>
<span className={styles.tooltip}>{text}</span>
<span className={styles.icon}>
<FontAwesomeIcon icon={faCircleQuestion} />
</span>
</button>
);
}
export default HelpButton;

Some files were not shown because too many files have changed in this diff Show More