89 Commits

Author SHA1 Message Date
157cd500ef Implement transportation emissions 2025-09-17 12:11:52 -05:00
ca06db2870 Add more fields to CSV reports 2025-09-16 12:17:00 -05:00
5ac9ae2b62 Reports: use dicts instead of lists 2025-09-16 12:01:29 -05:00
e4d4ee1cc8 Implement global disposal limits 2025-09-16 11:53:32 -05:00
67b1e5fd40 Reformat source code 2025-09-16 11:14:15 -05:00
5ea3e10139 web: Update copyright 2025-06-10 09:58:39 -05:00
3f9d2f22f5 web: Add scaffold 2025-06-10 09:56:06 -05:00
3d36caa507 Circular: Implement driving distances 2025-03-28 13:46:45 -05:00
17a870967b Fix typos 2025-03-28 12:17:13 -05:00
29999d006e Bump version to 0.8.0 2025-03-28 12:06:49 -05:00
db7f1c8af5 Simplify title 2025-03-28 12:06:16 -05:00
f940489693 Add README.md 2025-03-28 12:04:16 -05:00
40947190ad Update docs 2025-03-28 11:39:16 -05:00
f82a1d121d Make boat example easier to run; add output 2023-12-08 10:06:21 -06:00
6dae3a825f Convert boat example to Julia 2023-12-08 10:01:53 -06:00
ada89b7878 Debug model; improve reports 2023-12-06 16:16:13 -06:00
319e5f1ed3 Implement basic reports; fix boat example 2023-12-06 14:39:28 -06:00
06642c631f Conclude model implementation 2023-12-06 13:06:42 -06:00
d41ff30326 Model: Objective function and plant constraints 2023-12-06 11:50:03 -06:00
0da66b571a Start implementation of circular model 2023-12-06 09:27:37 -06:00
4947ad1a8a Finish implementing parser 2023-11-30 12:05:32 -06:00
76b085e105 Parser: Allow scalars in timeseries fields 2023-11-30 11:06:31 -06:00
6055eafcd6 Add larger example 2023-11-30 10:49:22 -06:00
74759bd602 Start implementation of circular model 2023-11-28 10:26:07 -06:00
84bd25b04d Fix: Remove disposal when deleting product 2023-07-07 10:22:57 -05:00
c86dda12cd Make marginal costs optional in write_reports 2023-07-07 10:20:25 -05:00
f3a2d1d616 Fix bug in _compress when plants have fixed size
Capacity was being incorrectly multiplied by T twice. This
happened because there were two references to the same struct
in the plant sizes array. This fix replaces the second reference
by an actual copy of the struct.
2023-07-07 10:05:31 -05:00
029a47a64b compress: Update disposal/acquisition limits/costs 2023-05-16 15:29:08 -05:00
de27a6202d Bump version to 0.7.2 2023-03-10 16:27:32 -06:00
7d4a763910 Fix issue with collection disposal; increase precision in CSV reports 2023-03-10 14:20:21 -06:00
8432c49050 Add .zenodo.json 2023-03-08 10:17:18 -06:00
2d860326fe Bump version to 0.7.1 2023-03-08 10:01:45 -06:00
be37934b87 Web: Do not use heuristics 2023-03-08 09:44:44 -06:00
3c354ec3e4 Add write_reports function 2023-03-08 09:44:27 -06:00
f5a92358d7 Formulation: If plant is closed, storage cannot be used 2023-03-08 09:44:08 -06:00
69f205be77 Formulation: Prevent plants from sending products to themselves 2023-03-08 09:42:53 -06:00
3b3ecbde27 Web: Fix parsing of disposal limit 2023-03-08 09:42:02 -06:00
b3a6632d7e Fix badge 2023-02-23 11:20:12 -06:00
44008c349d Bump package version 2023-02-23 11:19:07 -06:00
9c5e652d82 Update CHANGELOG 2023-02-23 11:09:45 -06:00
716291ee0f Add initial capacities to web UI 2023-02-23 10:51:07 -06:00
256b863c34 Implement initial plant capacity 2023-02-23 10:34:34 -06:00
1f3a3c9317 web: Add driving metric, fix missing defaults 2023-02-22 15:41:39 -06:00
e4ed05fb98 Update JSON schema 2023-02-22 15:00:42 -06:00
b1d49e1313 Export/import in-memory data format 2023-02-22 14:48:34 -06:00
7c7ab47bb6 CSV vars: Modify export function, disable validation 2023-02-22 11:25:13 -06:00
a821efdce9 Extract constants from CSV 2023-02-22 10:22:43 -06:00
c89747e8d4 Parse and evaluate expressions 2023-02-22 10:08:29 -06:00
40506c13eb Format source code 2023-02-16 11:27:27 -06:00
78128bd79b Allow user to specify product acquisition costs 2023-02-16 11:25:05 -06:00
2f0228e9ca Merge branch 'relog-web' 2023-02-15 14:31:03 -06:00
811730b8ab Replace Cbc/Clp by HiGHS 2023-02-15 14:24:18 -06:00
7bce105428 Fix formatting 2023-02-15 13:47:00 -06:00
1aa01b7b2b Merge branch 'master' into relog-web 2023-02-15 13:27:47 -06:00
e86ae0f818 Add RELOG.version() 2023-02-15 13:22:47 -06:00
22d73c9ded Move tests to a separate package; update GitHub CI and docs 2023-02-15 12:32:29 -06:00
a8e4491ea3 Merge branch 'master' into feature/collection-disposal 2023-02-15 11:01:10 -06:00
50d53f628f Reformat source code 2023-01-24 10:31:40 -06:00
79748e3c13 Dist: Drop NaN in training dataset 2023-01-24 10:27:41 -06:00
d1f6796c96 Update CHANGELOG.md 2022-12-16 15:33:16 -06:00
51ff8eb130 Restrict NearestNeighbors version; remove debug statement 2022-12-15 11:03:44 -06:00
9191474df8 Bump version to 0.6 2022-12-15 10:36:44 -06:00
841fbf16fb Make distance metric configurable; fix building period bug 2022-12-15 10:26:10 -06:00
48bd3c403f Switch from Euclidean to approximate driving distance 2022-12-15 09:49:38 -06:00
23b3b33146 Update README.md 2022-10-28 14:05:53 -05:00
86dee7558b Replace Cbc/Clp by HiGHS 2022-09-08 12:14:49 -05:00
d84b74a8a7 relog-web: Make time limit configurable 2022-09-08 11:35:11 -05:00
bae39a4ff4 Merge tag 'v0.5.2' into feature/gui
[Diff since v0.5.1](https://github.com/ANL-CEEESA/RELOG/compare/v0.5.1...v0.5.2)
2022-08-26 14:34:17 -05:00
8bf2baf809 Enable marginal costs 2022-08-22 11:13:43 -05:00
027ffcd94c Update gitignore 2022-08-22 11:12:29 -05:00
84cf4ddcd9 Casebuilder: Add MapBlock; fix zip paths 2022-06-10 16:53:32 -05:00
8bce7c047b Case Builder: Disable buttons on submit 2022-06-09 16:49:48 -05:00
ee767b9ebd Create solver page; add Dockerfile 2022-06-06 13:46:22 -05:00
9112d9fde5 casebuilder: Validate JSON schema on load/save 2022-05-27 14:36:54 -05:00
3d03dfc722 Fix plant input 2022-04-01 16:42:06 -05:00
01a4c6626d Disable download button when no data is available 2022-04-01 13:53:28 -05:00
310f5c389e Fix import/export of default values 2022-04-01 13:41:53 -05:00
1273110419 Hide disposal for non-primary products 2022-03-18 16:47:28 -05:00
e797cb98e0 Minor changes to case builder 2022-03-18 10:33:16 -05:00
0beb30800e Enable controls 2022-03-18 10:05:08 -05:00
02a81e5fdd Switch to IndexedDB 2022-03-18 10:04:58 -05:00
096d95a1aa Implement inflation 2022-03-18 09:43:01 -05:00
01452441dc Reformat source code; implement import/export 2022-03-17 15:44:21 -05:00
56b673fb9e Initial amount CSV upload/download 2022-03-15 16:06:18 -05:00
af2a8b67be Implement form validation 2022-03-15 10:23:51 -05:00
524299a3c2 Make pipeline, parameters & product interactive 2022-03-15 09:29:53 -05:00
0e53a4334e Implement initial, static version of input GUI 2022-03-14 11:10:51 -05:00
a03b9169fd Allow product disposal at collection centers 2021-10-15 09:11:41 -05:00
ee58af73f0 Update sysimage and build scripts 2021-10-15 08:14:04 -05:00
117 changed files with 22766 additions and 4715 deletions

4
.dockerignore Normal file
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@@ -0,0 +1,4 @@
build
jobs
relog-web/node_modules
relog-web/build

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

View File

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

5
.gitignore vendored
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@@ -12,3 +12,8 @@ Manifest.toml
data
build
benchmark
run.jl
relog-web-legacy
.vscode
jobs
tmp

28
.zenodo.json Normal file
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@@ -0,0 +1,28 @@
{
"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."
}

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@@ -1,53 +0,0 @@
# 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
## [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)"

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@@ -1,25 +0,0 @@
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.
********************************************************************************

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@@ -1,19 +0,0 @@
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

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@@ -1,46 +1,18 @@
name = "RELOG"
uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
authors = ["Alinson S Xavier <axavier@anl.gov>"]
version = "0.5.2"
uuid = "7cafaa7a-b311-45f0-b313-80bf15b5e5e5"
authors = ["Alinson S. Xavier <git@axavier.org>"]
version = "0.8.0"
[deps]
CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
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"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JSONSchema = "7d188eb4-7ad8-530c-ae41-71a32a6d4692"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
[compat]
CRC = "4"
CSV = "0.10"
Cbc = "1"
Clp = "1"
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"

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@@ -1,8 +1,5 @@
<h1 align="center">RELOG: Reverse Logistics Optimization</h1>
<h1 align="center">RELOG: Supply Chain Analysis and Optimization</h1>
<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">
<img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4302341.svg">
</a>
@@ -11,31 +8,44 @@
</a>
</p>
**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.
**RELOG** is an open-source package designed to optimize supply chains for
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.
<img src="https://anl-ceeesa.github.io/RELOG/0.5/assets/ex_transportation.png" width="1000px"/>
## Screenshots
### Documentation
<img src="https://raw.githubusercontent.com/ANL-CEEESA/RELOG/refs/heads/circular/docs/src/assets/relog.png" width="1000px"/>
* [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)
## Documentation
### Authors
See official documentation at: https://anl-ceeesa.github.io/RELOG/
* **Alinson S. Xavier** <<axavier@anl.gov>>
* **Nwike Iloeje** <<ciloeje@anl.gov>>
* **John Atkins**
* **Kyle Sun**
## Authors
### License
- **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
- **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
RELOG: Reverse Logistics Optimization
Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Copyright © 2020-2025, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:

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[deps]
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
[compat]
JuliaFormatter = "0.14.4"

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

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

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

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# Input and Output Data Formats
# Input data format
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.
RELOG accepts as input a JSON file with four sections: `parameters`, `products`,
`centers` and `plants`. Below, we describe each section in more detail.
## Input Data Format (JSON)
RELOG accepts as input a JSON file with three sections: `parameters`, `products` and `plants`. Below, we describe each section in more detail.
### 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. |
## Parameters
| 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. |
| `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
```json
{
"parameters": {
"time horizon (years)": 2,
"building period (years)": [1]
}
"parameters": {
"time horizon (years)": 4,
"building period (years)": [1],
"distance metric": "driving"
}
}
```
### Products
## Products
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:
| Key | Description
|:--------------------------------------|:---------------|
|`transportation cost ($/km/tonne)` | The cost to transport this product. Must be a time series.
|`transportation energy (J/km/tonne)` | The energy required to transport this product. Must be a time series. Optional.
|`transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional.
|`initial amounts` | A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a time series.
Each product may have some amount available at the beginning of each time period. In this case, the key `initial amounts` maps to a dictionary with the following keys:
| Key | Description
|:------------------------|:---------------|
| `latitude (deg)` | The latitude of the location.
| `longitude (deg)` | The longitude of the location.
| `amount (tonne)` | The amount of the product initially available at the location. Must be a time series.
| Key | Description |
| :------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `transportation cost ($/km/tonne)` | The cost to transport this product. Must be a time series. |
| `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. |
| `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. |
#### Example
```json
{
"products": {
"P1": {
"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]
}
},
"transportation cost ($/km/tonne)": [0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.11],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.050],
"CH4": [0.003, 0.002]
}
"products": {
"P1": {
"transportation cost ($/km/tonne)": 0.015,
"transportation energy (J/km/tonne)": 0.12,
"transportation emissions (tonne/km/tonne)": {
"CO2": 0.052,
"CH4": 0.003
},
"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": {
"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": [0.12, 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": {
"latitude (deg)": 41.881,
"longitude (deg)": -87.623,
"input": null,
"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": {
"latitude (deg)": 41.881,
"longitude (deg)": -87.623,
"input": "P1",
"outputs": [],
"variable output (tonne/tonne)": {},
"constant output (tonne)": {},
"revenue ($/tonne)": [12.0, 12.0, 12.0, 12.0],
"collection cost ($/tonne)": {},
"operating cost ($)": [150.0, 150.0, 150.0, 150.0],
"disposal limit (tonne)": {},
"disposal cost ($/tonne)": {}
}
}
}
```
## Plants
| Key | Description |
| :----------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------- |
| `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. |
The entries in the `capacities` list should be dictionaries with the following
keys:
| Key | Description |
| :---------------------------------- | :-------------------------------------------------------------------------------------------------- |
| `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. Must be a time series. |
| `variable operating cost ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series. |
| `initial capacity (tonne)` | Capacity already available. If the plant has not been built yet, this should be `0`. |
```json
{
"plants": {
"L1": {
"latitude (deg)": 41.881,
"longitude (deg)": -87.623,
"input mix (%)": {
"P1": 95.3,
"P2": 4.7
},
"output (tonne)": {
"P3": 0.25,
"P4": 0.12,
"P5": 0.1
},
"processing emissions (tonne)": {
"CO2": 0.1
},
"storage cost ($/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
},
"P2": {
"transportation cost ($/km/tonne)": [0.022, 0.020]
},
"P3": {
"transportation cost ($/km/tonne)": [0.0125, 0.0125]
},
"P4": {
"transportation cost ($/km/tonne)": [0.0175, 0.0175]
{
"size": 500,
"opening cost ($)": 1000.0,
"fixed operating cost ($)": 400.0,
"variable operating cost ($/tonne)": 5.0.
}
],
"initial capacity (tonne)": 0,
}
}
}
```
### Processing plants
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:
| Key | Description
|:------------------------|:---------------|
| `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).
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:
| Key | Description
|:------------------------------|---------------|
| `latitude (deg)` | The latitude of the location, in degrees.
| `longitude (deg)` | The longitude of the location, in degrees.
| `disposal` | A dictionary describing what products can be disposed locally at the plant.
| `storage` | A dictionary describing the plant's storage.
| `capacities (tonne)` | A dictionary describing what plant sizes are allowed, and their characteristics.
The `storage` dictionary should contain the following keys:
| Key | Description
|:------------------------|:---------------|
| `cost ($/tonne)` | The cost to store a tonne of input product for one time period. Must be a time series.
| `limit (tonne)` | The maximum amount of input product this plant can have in storage at any given time.
The keys in the `disposal` dictionary should be the names of the products. The values are dictionaries with the following keys:
| Key | Description
|:------------------------|:---------------|
| `cost ($/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 ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series.
#### Example
```json
{
"plants": {
"F1": {
"input": "P1",
"outputs (tonne/tonne)": {
"P2": 0.2,
"P3": 0.5
},
"energy (GJ/tonne)": [0.12, 0.11],
"emissions (tonne/tonne)": {
"CO2": [0.052, 0.050],
"CH4": [0.003, 0.002]
},
"locations": {
"L1": {
"latitude (deg)": 0.0,
"longitude (deg)": 0.0,
"disposal": {
"P2": {
"cost ($/tonne)": [-10.0, -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

@@ -1,35 +1,42 @@
# RELOG: Reverse Logistics Optimization
# RELOG -- Supply Chain Analysis and Optimization
**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.
**RELOG** is an open-source package designed to optimize supply chains for
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
<center>
<img src="assets/ex_transportation.png" width="1000px"/>
<img src="assets/relog.png" width="1000px"/>
</center>
```
## Authors
### Table of Contents
- **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
- **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
```@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
## License
```text
RELOG: Reverse Logistics Optimization
Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
Copyright © 2020-2025, UChicago Argonne, LLC. All Rights Reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
@@ -52,4 +59,4 @@ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSE
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.
```
```

View File

@@ -1,196 +0,0 @@
# 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*}
```

279
docs/src/problem.md Normal file
View File

@@ -0,0 +1,279 @@
# 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 include 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 |
| $K^\text{cap}_{p}$ | Capacity of plant $p$, if the plant is open | 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 |
| $K^\text{mix}_{pmt}$ | 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 |
| $K^\text{output}_{pmt}$ | Amount of material $m$ produced by plant $p$ at time $t$ for each tonne of input material processed | tonne |
| $K^\text{tr-em}_{gmt}$ | Amount of greenhouse gas $g$ released by transporting 1 tonne of material $m$ over one km at time $t$ | tonne/km-tonne |
| $R^\text{tr}_{mt}$ | Cost to send material $m$ at time $t$ | \$/km-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 |
| $R^\text{fix}_{ut}$ | Fixed operating cost for plant/center $u$ at time $t$ | \$ |
| $R^\text{open}_{pt}$ | Cost to open plant $p$ at time $t$ | \$ |
| $R^\text{rev}_{ct}$ | Revenue for selling the input product of center $c$ at this center at time $t$ | \$/tonne |
| $R^\text{var}_{pt}$ | Cost to process one tonne of input material at plant $p$ at time $t$ | \$/tonne |
| $K^\text{out-fix}_{cmt}$ | Fixed amount of material $m$ collected at center $m$ at time $t$ | \$/tonne |
| $K^\text{out-var}_{c,m,i}$ | Factor used to calculate variable amount of material $m$ collected at center $m$. See `eq_z_collected` for more details. | -- |
| $K^\text{out-var-len}_{cm}$ | Length of the $K^\text{out-var}_{c,m,*}$ vector. | -- |
## 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 |
| $z^{\text{collected}}_{cmt}$ | `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 |
| $z^{\text{input}}_{ut}$ | `z_input[u.name, t]` | Total plant/center input 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 |
| $z^{\text{tr-em}}_{guvmt}$ | `z_tr_em[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 |
## Objective function
The goals 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:
```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:
```math
\sum_{p \in P} \sum_{t \in T} R^\text{fix}_{pt} x_{pt}
```
- 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}
```
## 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*}
```
- Plant input mix must have correct proportion
(`eq_input_mix[p.name, m.name, t]`):
```math
\begin{align*}
& \sum_{u : (u,m) \in E^-(p)} y_{upmt}
= K^\text{mix}_{pmt} z^{\text{input}}_{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{input}_{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*}
```
- Plants have a maximum capacity; furthermore, if the plant is not open, its
capacity is zero (`eq_capacity[p.name,t]`)
```math
\begin{align*}
& z^\text{input}_{pt} \leq K^\text{cap}_p x_{pt}
& \forall p \in 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_tr_em[g.name, u.name, v.name, m.name, t`)
```math
\begin{align*}
& z^{\text{tr-em}}_{guvmt} = K^{\text{dist}}_{uv} K^\text{tr-em}_{gmt} y_{uvmt}
& \forall g \in G, (u, v, m) \in E, t \in T
\end{align*}
```

View File

@@ -1,4 +1,4 @@
# Simplified Solution Reports
# 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).
@@ -154,6 +154,7 @@ Report showing primary product amounts, locations and marginal costs. Generated
| `longitude (deg)` | Longitude of the collection center.
| `year` | What year this row corresponds to. This reports includes one row for each year.
| `amount (tonne)` | Amount of product available at this collection center.
| `amount disposed (tonne)` | Amount of product disposed of at this collection center.
| `marginal cost ($/tonne)` | Cost to process one additional tonne of this product coming from this collection center.

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@@ -1,5 +1,4 @@
Usage
=====
# Usage
## 1. Installation
@@ -7,13 +6,7 @@ To use RELOG, the first step is to install the [Julia programming language](http
```julia
using Pkg
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")
Pkg.add(name="RELOG", version="0.7")
```
## 2. Modeling the problem
@@ -22,21 +15,21 @@ 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.
* 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).
@@ -96,12 +89,11 @@ To use the `resolve` method, the new input file should be very similar to the or
- **Plant's storage:** Cost.
- **Plant's capacity:** Opening cost, fixed operating cost and variable operating cost.
## 5. Advanced options
### 5.1 Changing the solver
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:
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:
```julia
using RELOG, Gurobi, JuMP

View File

@@ -1,202 +0,0 @@
{
"parameters": {
"time horizon (years)": 2
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": [0.015, 0.015],
"transportation energy (J/km/tonne)": [0.12, 0.11],
"transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.050],
"CH4": [0.003, 0.002]
},
"initial amounts": {
"C1": {
"latitude (deg)": 7.0,
"longitude (deg)": 7.0,
"amount (tonne)": [934.56, 934.56]
},
"C2": {
"latitude (deg)": 7.0,
"longitude (deg)": 19.0,
"amount (tonne)": [198.95, 198.95]
},
"C3": {
"latitude (deg)": 84.0,
"longitude (deg)": 76.0,
"amount (tonne)": [212.97, 212.97]
},
"C4": {
"latitude (deg)": 21.0,
"longitude (deg)": 16.0,
"amount (tonne)": [352.19, 352.19]
},
"C5": {
"latitude (deg)": 32.0,
"longitude (deg)": 92.0,
"amount (tonne)": [510.33, 510.33]
},
"C6": {
"latitude (deg)": 14.0,
"longitude (deg)": 62.0,
"amount (tonne)": [471.66, 471.66]
},
"C7": {
"latitude (deg)": 30.0,
"longitude (deg)": 83.0,
"amount (tonne)": [785.21, 785.21]
},
"C8": {
"latitude (deg)": 35.0,
"longitude (deg)": 40.0,
"amount (tonne)": [706.17, 706.17]
},
"C9": {
"latitude (deg)": 74.0,
"longitude (deg)": 52.0,
"amount (tonne)": [30.08, 30.08]
},
"C10": {
"latitude (deg)": 22.0,
"longitude (deg)": 54.0,
"amount (tonne)": [536.52, 536.52]
}
}
},
"P2": {
"transportation cost ($/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

@@ -1,347 +0,0 @@
{
"parameters": {
"time horizon (years)": 2
},
"products": {
"P1": {
"transportation cost ($/km/tonne)": [
0.015,
0.015
],
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View File

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],
"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
]
}
}
}
}

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@@ -1,11 +0,0 @@
[ Info: Reading s1.json...
[ Info: Building graph...
[ Info: 2 time periods
[ Info: 6 process nodes
[ Info: 8 shipping nodes (plant)
[ Info: 10 shipping nodes (collection)
[ Info: 38 arcs
[ Info: Building optimization model...
[ Info: Optimizing MILP...
[ Info: Re-optimizing with integer variables fixed...
[ Info: Extracting solution...

75
juliaw
View File

@@ -1,75 +0,0 @@
#!/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

View File

@@ -1,23 +0,0 @@
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

@@ -1,28 +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.
module RELOG
_round(x::Number) = round(x, digits = 5)
include("instance/structs.jl")
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/jumpext.jl")
include("model/dist.jl")
include("model/build.jl")
include("model/getsol.jl")
include("model/solve.jl")
include("model/resolve.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
include("reports/transportation.jl")
include("reports/centers.jl")
end # module RELOG

View File

@@ -1,100 +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 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}()
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
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(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(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,
)
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

View File

@@ -1,11 +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.
function to_csv(graph::Graph)
result = ""
for a in graph.arcs
result *= "$(a.source.index),$(a.dest.index)\n"
end
return result
end

View File

@@ -1,44 +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 Geodesy
abstract type Node end
mutable struct Arc
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}
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

View File

@@ -1,60 +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 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
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

View File

@@ -1,212 +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 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

@@ -1,180 +1,142 @@
# 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
using OrderedCollections
function parsefile(path::String)::Instance
return RELOG.parse(JSON.parsefile(path))
return RELOG.parse(JSON.parsefile(path, dicttype = () -> OrderedDict()))
end
function parse(json)::Instance
basedir = dirname(@__FILE__)
json_schema = JSON.parsefile("$basedir/../schemas/input.json")
validate(json, Schema(json_schema))
# Read parameters
time_horizon = json["parameters"]["time horizon (years)"]
building_period = json["parameters"]["building period (years)"]
T = json["parameters"]["time horizon (years)"]
json_schema["definitions"]["TimeSeries"]["minItems"] = T
json_schema["definitions"]["TimeSeries"]["maxItems"] = T
validate(json, Schema(json_schema))
# Read distance metric
distance_metric_str = lowercase(json["parameters"]["distance metric"])
if distance_metric_str == "driving"
distance_metric = KnnDrivingDistance()
elseif distance_metric_str == "euclidean"
distance_metric = EuclideanDistance()
else
error("Invalid distance metric: $distance_metric_str")
end
building_period = [1]
if "building period (years)" in keys(json)
building_period = json["building period (years)"]
timeseries(::Nothing; null_val = nothing) = repeat([null_val], time_horizon)
timeseries(x::Number; null_val = nothing) = repeat([x], time_horizon)
timeseries(x::Array; null_val = nothing) = [xi === nothing ? null_val : xi for xi in x]
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
plants = Plant[]
products = Product[]
collection_centers = CollectionCenter[]
prod_name_to_product = Dict{String,Product}()
plants_by_name = OrderedDict{String,Plant}()
for (name, pdict) in json["plants"]
prod_dict(key; scale = 1.0, null_val = Inf) = OrderedDict{Product,Vector{Float64}}(
products_by_name[p] => [
v === nothing ? null_val : v * scale for v in timeseries(pdict[key][p])
] for p in keys(pdict[key])
)
# Create products
for (product_name, product_dict) in json["products"]
cost = product_dict["transportation cost (\$/km/tonne)"]
energy = zeros(T)
emissions = Dict()
if "transportation energy (J/km/tonne)" in keys(product_dict)
energy = product_dict["transportation energy (J/km/tonne)"]
end
if "transportation emissions (tonne/km/tonne)" in keys(product_dict)
emissions = product_dict["transportation emissions (tonne/km/tonne)"]
end
product = Product(product_name, cost, energy, emissions)
push!(products, product)
prod_name_to_product[product_name] = product
# Create collection centers
if "initial amounts" in keys(product_dict)
for (center_name, center_dict) in product_dict["initial amounts"]
if "location" in keys(center_dict)
region = geodb_query(center_dict["location"])
center_dict["latitude (deg)"] = region.centroid.lat
center_dict["longitude (deg)"] = region.centroid.lon
end
center = CollectionCenter(
length(collection_centers) + 1,
center_name,
center_dict["latitude (deg)"],
center_dict["longitude (deg)"],
product,
center_dict["amount (tonne)"],
)
push!(collection_centers, center)
end
end
end
# Create plants
for (plant_name, plant_dict) in json["plants"]
input = prod_name_to_product[plant_dict["input"]]
output = Dict()
# Plant outputs
if "outputs (tonne/tonne)" in keys(plant_dict)
output = Dict(
prod_name_to_product[key] => value for
(key, value) in plant_dict["outputs (tonne/tonne)"] if value > 0
latitude = pdict["latitude (deg)"]
longitude = pdict["longitude (deg)"]
input_mix = prod_dict("input mix (%)", scale = 0.01)
output = prod_dict("output (tonne)")
emissions = timeseries(pdict["processing emissions (tonne)"])
storage_cost = prod_dict("storage cost (\$/tonne)")
storage_limit = prod_dict("storage limit (tonne)")
disposal_cost = prod_dict("disposal cost (\$/tonne)")
disposal_limit = prod_dict("disposal limit (tonne)")
initial_capacity = pdict["initial capacity (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
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,
storage_limit,
storage_cost,
)
push!(plants, plant)
end
plant = Plant(;
name,
latitude,
longitude,
input_mix,
output,
emissions,
storage_cost,
storage_limit,
disposal_cost,
disposal_limit,
capacities,
initial_capacity,
)
push!(plants, plant)
plants_by_name[name] = plant
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)
return Instance(;
time_horizon,
building_period,
distance_metric,
products,
products_by_name,
centers,
centers_by_name,
plants,
plants_by_name,
)
end

View File

@@ -1,57 +1,67 @@
# 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 OrderedCollections
using DataStructures
using JSON
using JSONSchema
using Printf
using Statistics
abstract type DistanceMetric end
mutable struct Product
name::String
transportation_cost::Vector{Float64}
transportation_energy::Vector{Float64}
transportation_emissions::Dict{String,Vector{Float64}}
Base.@kwdef mutable struct KnnDrivingDistance <: DistanceMetric
tree = nothing
ratios = nothing
end
mutable struct CollectionCenter
index::Int64
mutable struct EuclideanDistance <: DistanceMetric end
Base.@kwdef struct Product
name::String
tr_cost::Vector{Float64}
tr_energy::Vector{Float64}
tr_emissions::OrderedDict{String,Vector{Float64}}
disposal_limit::Vector{Float64}
end
Base.@kwdef struct Center
name::String
latitude::Float64
longitude::Float64
product::Product
amount::Vector{Float64}
input::Union{Product,Nothing}
outputs::Vector{Product}
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
mutable struct PlantSize
capacity::Float64
variable_operating_cost::Vector{Float64}
fixed_operating_cost::Vector{Float64}
Base.@kwdef struct PlantCapacity
size::Float64
opening_cost::Vector{Float64}
fix_operating_cost::Vector{Float64}
var_operating_cost::Vector{Float64}
end
mutable struct Plant
index::Int64
plant_name::String
location_name::String
input::Product
output::Dict{Product,Float64}
Base.@kwdef struct Plant
name::String
latitude::Float64
longitude::Float64
disposal_limit::Dict{Product,Vector{Float64}}
disposal_cost::Dict{Product,Vector{Float64}}
sizes::Vector{PlantSize}
energy::Vector{Float64}
emissions::Dict{String,Vector{Float64}}
storage_limit::Float64
storage_cost::Vector{Float64}
input_mix::OrderedDict{Product,Vector{Float64}}
output::OrderedDict{Product,Vector{Float64}}
emissions::OrderedDict{String,Vector{Float64}}
storage_cost::OrderedDict{Product,Vector{Float64}}
storage_limit::OrderedDict{Product,Vector{Float64}}
disposal_cost::OrderedDict{Product,Vector{Float64}}
disposal_limit::OrderedDict{Product,Vector{Float64}}
capacities::Vector{PlantCapacity}
initial_capacity::Float64
end
mutable struct Instance
time::Int64
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}
collection_centers::Vector{CollectionCenter}
time_horizon::Int
plants::Vector{Plant}
building_period::Vector{Int64}
plants_by_name::OrderedDict{String,Plant}
end

View File

@@ -1,21 +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 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

@@ -1,249 +1,333 @@
# 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
using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
function build_model(instance::Instance; optimizer, variable_names::Bool = false)
model = JuMP.Model(optimizer)
centers = instance.centers
products = instance.products
plants = instance.plants
T = 1:instance.time_horizon
model.ext[:instance] = instance
function build_model(instance::Instance, graph::Graph, optimizer)::JuMP.Model
model = Model(optimizer)
model[:instance] = instance
model[:graph] = graph
create_vars!(model)
create_objective_function!(model)
create_shipping_node_constraints!(model)
create_process_node_constraints!(model)
return model
end
# 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 create_vars!(model::JuMP.Model)
graph, T = model[:graph], model[:instance].time
model[:flow] =
Dict((a, t) => @variable(model, lower_bound = 0) for a in graph.arcs, t = 1:T)
model[:dispose] = Dict(
(n, t) => @variable(
model,
lower_bound = 0,
upper_bound = n.location.disposal_limit[n.product][t]
) for n in values(graph.plant_shipping_nodes), t = 1:T
)
model[:store] = Dict(
(n, t) =>
@variable(model, lower_bound = 0, upper_bound = n.location.storage_limit)
for n in values(graph.process_nodes), t = 1:T
)
model[:process] = Dict(
(n, t) => @variable(model, lower_bound = 0) for
n in values(graph.process_nodes), t = 1:T
)
model[:open_plant] = Dict(
(n, t) => @variable(model, binary = true) for n in values(graph.process_nodes),
t = 1:T
)
model[:is_open] = Dict(
(n, t) => @variable(model, binary = true) for n in values(graph.process_nodes),
t = 1:T
)
model[:capacity] = Dict(
(n, t) => @variable(
model,
lower_bound = 0,
upper_bound = n.location.sizes[2].capacity
) for n in values(graph.process_nodes), t = 1:T
)
model[:expansion] = Dict(
(n, t) => @variable(
model,
lower_bound = 0,
upper_bound = n.location.sizes[2].capacity - n.location.sizes[1].capacity
) for n in values(graph.process_nodes), t = 1:T
)
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)
function push_edge!(src, dst, m)
push!(E, (src, dst, m))
push!(E_out[src], (dst, m))
push!(E_in[dst], (src, m))
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
for m in products
for p1 in plants
m keys(p1.output) || continue
function create_objective_function!(model::JuMP.Model)
graph, T = model[:graph], model[:instance].time
obj = AffExpr(0.0)
# Plant to plant
for p2 in plants
p1 != p2 || continue
m keys(p2.input_mix) || continue
push_edge!(p1, p2, m)
end
# Process node costs
for n in values(graph.process_nodes), t = 1:T
# Transportation and variable operating costs
for a in n.incoming_arcs
c = n.location.input.transportation_cost[t] * a.values["distance"]
add_to_expression!(obj, c, model[:flow][a, t])
# Plant to center
for c in centers
m == c.input || continue
push_edge!(p1, c, m)
end
end
# Opening costs
add_to_expression!(
obj,
n.location.sizes[1].opening_cost[t],
model[:open_plant][n, t],
)
for c1 in centers
m c1.outputs || continue
# Fixed operating costs (base)
add_to_expression!(
obj,
n.location.sizes[1].fixed_operating_cost[t],
model[:is_open][n, t],
)
# Center to plant
for p in plants
m keys(p.input_mix) || continue
push_edge!(c1, p, m)
end
# Fixed operating costs (expansion)
add_to_expression!(obj, slope_fix_oper_cost(n.location, t), model[:expansion][n, t])
# Processing costs
add_to_expression!(
obj,
n.location.sizes[1].variable_operating_cost[t],
model[:process][n, t],
)
# Storage costs
add_to_expression!(obj, n.location.storage_cost[t], model[:store][n, t])
# Expansion costs
if t < T
add_to_expression!(
obj,
slope_open(n.location, t) - slope_open(n.location, t + 1),
model[:expansion][n, t],
)
else
add_to_expression!(obj, slope_open(n.location, t), model[:expansion][n, t])
# Center to center
for c2 in centers
m == c2.input || continue
push_edge!(c1, c2, m)
end
end
end
# Shipping node costs
for n in values(graph.plant_shipping_nodes), t = 1:T
# 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
# Disposal costs
# Decision variables
# -------------------------------------------------------------------------
# Plant p is operational at time t
x = _init(model, :x)
for p in plants
x[p.name, 0] = p.initial_capacity > 0 ? 1 : 0
end
for p in plants, t in T
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
y = _init(model, :y)
for (p1, p2, m) in E, t in T
y[p1.name, p2.name, m.name, t] = @variable(model, lower_bound = 0)
end
# 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
# 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
# Transportation emissions by greenhouse gas
z_tr_em = _init(model, :z_tr_em)
for (p1, p2, m) in E, t in T, g in keys(m.tr_emissions)
z_tr_em[g, p1.name, p2.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Objective function
# -------------------------------------------------------------------------
obj = AffExpr()
# Transportation cost
for (p1, p2, m) in E, t in T
add_to_expression!(
obj,
n.location.disposal_cost[n.product][t],
model[:dispose][n, t],
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,
p.capacities[1].opening_cost[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, p.capacities[1].fix_operating_cost[t], x[p.name, t])
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
@objective(model, Min, obj)
end
# Constraints
# -------------------------------------------------------------------------
function create_shipping_node_constraints!(model::JuMP.Model)
graph, T = model[:graph], model[:instance].time
model[:eq_balance] = OrderedDict()
for t = 1:T
# Collection centers
for n in graph.collection_shipping_nodes
model[:eq_balance][n, t] = @constraint(
model,
sum(model[:flow][a, t] for a in n.outgoing_arcs) == n.location.amount[t]
)
end
# 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,
z_input[p.name, t] ==
sum(y[src.name, p.name, m.name, t] for (src, m) in E_in[p])
)
end
# Plants
for n in graph.plant_shipping_nodes
@constraint(
model,
sum(model[:flow][a, t] for a in n.incoming_arcs) ==
sum(model[:flow][a, t] for a in n.outgoing_arcs) + model[:dispose][n, t]
)
# Plants: Must meet input mix
eq_input_mix = _init(model, :eq_input_mix)
for p in plants, m in keys(p.input_mix), t in T
eq_input_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_input[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_input[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: Capacity limit
eq_capacity = _init(model, :eq_capacity)
for p in plants, t in T
eq_capacity[p.name, t] =
@constraint(model, z_input[p.name, t] <= p.capacities[1].size * x[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
eq_building_period[p.name, t] =
@constraint(model, x[p.name, t] - x[p.name, t-1] <= 0)
end
end
end
function create_process_node_constraints!(model::JuMP.Model)
graph, T = model[:graph], model[:instance].time
for t = 1:T, n in graph.process_nodes
input_sum = AffExpr(0.0)
for a in n.incoming_arcs
add_to_expression!(input_sum, 1.0, model[:flow][a, t])
end
# Output amount is implied by amount processed
for a in n.outgoing_arcs
@constraint(
model,
model[:flow][a, t] == a.values["weight"] * model[:process][n, t]
)
end
# If plant is closed, capacity is zero
@constraint(
# Centers: Definition of total center input
eq_z_input = _init(model, :eq_z_input)
for c in centers, t in T
eq_z_input[c.name, t] = @constraint(
model,
model[:capacity][n, t] <= n.location.sizes[2].capacity * model[:is_open][n, t]
z_input[c.name, t] ==
sum(y[src.name, c.name, m.name, t] for (src, m) in E_in[c])
)
# If plant is open, capacity is greater than base
@constraint(
model,
model[:capacity][n, t] >= n.location.sizes[1].capacity * model[:is_open][n, t]
)
# Capacity is linked to expansion
@constraint(
model,
model[:capacity][n, t] <=
n.location.sizes[1].capacity + model[:expansion][n, t]
)
# Can only process up to capacity
@constraint(model, model[:process][n, t] <= model[:capacity][n, t])
if t > 1
# Plant capacity can only increase over time
@constraint(model, model[:capacity][n, t] >= model[:capacity][n, t-1])
@constraint(model, model[:expansion][n, t] >= model[:expansion][n, t-1])
end
# Amount received equals amount processed plus stored
store_in = 0
if t > 1
store_in = model[:store][n, t-1]
end
if t == T
@constraint(model, model[:store][n, t] == 0)
end
@constraint(
model,
input_sum + store_in == model[:store][n, t] + model[:process][n, t]
)
# 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[:is_open][n, t] == model[:is_open][n, t-1] + model[:open_plant][n, t]
)
else
@constraint(model, model[:is_open][n, t] == model[:open_plant][n, t])
end
# Plant can only be opened during building period
if t model[:instance].building_period
@constraint(model, model[:open_plant][n, t] == 0)
end
end
# Centers: Calculate amount collected
eq_z_collected = _init(model, :eq_z_collected)
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,
z_collected[c.name, m.name, t] ==
sum(
z_input[c.name, t-offset] * c.var_output[m][offset+1] for
offset = 0:min(M - 1, t - 1)
) + c.fixed_output[m][t]
)
end
# Centers: Collected products must be disposed or sent
eq_balance = _init(model, :eq_balance)
for c in centers, m in c.outputs, t in 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_tr_em = _init(model, :eq_tr_em)
for (p1, p2, m) in E, t in T, g in keys(m.tr_emissions)
eq_tr_em[g, p1.name, p2.name, m.name, t] = @constraint(
model,
z_tr_em[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
if variable_names
_set_names!(model)
end
return model
end

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# 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

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@@ -1,227 +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 JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
function get_solution(model::JuMP.Model; marginal_costs = true)
graph, instance = model[:graph], model[:instance]
T = instance.time
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(),
),
)
plant_to_process_node = OrderedDict(n.location => n for n in graph.process_nodes)
plant_to_shipping_nodes = OrderedDict()
for p in instance.plants
plant_to_shipping_nodes[p] = []
for a in plant_to_process_node[p].outgoing_arcs
push!(plant_to_shipping_nodes[p], a.dest)
end
end
# Products
if marginal_costs
for n in graph.collection_shipping_nodes
location_dict = OrderedDict{Any,Any}(
"Marginal cost (\$/tonne)" => [
round(abs(JuMP.shadow_price(model[:eq_balance][n, t])), digits = 2) for t = 1:T
],
"Latitude (deg)" => n.location.latitude,
"Longitude (deg)" => n.location.longitude,
"Amount (tonne)" => n.location.amount,
)
if n.product.name keys(output["Products"])
output["Products"][n.product.name] = OrderedDict()
end
output["Products"][n.product.name][n.location.name] = location_dict
end
end
# Plants
for plant in instance.plants
skip_plant = true
process_node = plant_to_process_node[plant]
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)" =>
[JuMP.value(model[:capacity][process_node, t]) for t = 1:T],
"Opening cost (\$)" => [
JuMP.value(model[:open_plant][process_node, t]) *
plant.sizes[1].opening_cost[t] for t = 1:T
],
"Fixed operating cost (\$)" => [
JuMP.value(model[:is_open][process_node, t]) *
plant.sizes[1].fixed_operating_cost[t] +
JuMP.value(model[:expansion][process_node, t]) *
slope_fix_oper_cost(plant, t) for t = 1:T
],
"Expansion cost (\$)" => [
(
if t == 1
slope_open(plant, t) * JuMP.value(model[:expansion][process_node, t])
else
slope_open(plant, t) * (
JuMP.value(model[:expansion][process_node, t]) -
JuMP.value(model[:expansion][process_node, t-1])
)
end
) for t = 1:T
],
"Process (tonne)" =>
[JuMP.value(model[:process][process_node, t]) for t = 1:T],
"Variable operating cost (\$)" => [
JuMP.value(model[:process][process_node, t]) *
plant.sizes[1].variable_operating_cost[t] for t = 1:T
],
"Storage (tonne)" =>
[JuMP.value(model[:store][process_node, t]) for t = 1:T],
"Storage cost (\$)" => [
JuMP.value(model[:store][process_node, 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 = [JuMP.value(model[:flow][a, 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 shipping_node in plant_to_shipping_nodes[plant]
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 = [JuMP.value(model[:dispose][shipping_node, 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)"] =
[JuMP.value(model[:dispose][shipping_node, 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 = [JuMP.value(model[:flow][a, 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

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# 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 != $v")
end
function set_name(x::Number, n::String)
# nop
end
function _init(model::JuMP.Model, key::Symbol)::OrderedDict
if !(key in keys(object_dictionary(model)))
model[key] = OrderedDict()
end
return model[key]
end
function _set_names!(model::JuMP.Model)
@info "Setting variable and constraint names..."
time_varnames = @elapsed begin
_set_names!(object_dictionary(model))
end
@info @sprintf("Set names in %.2f seconds", time_varnames)
end
function _set_names!(dict::Dict)
for name in keys(dict)
dict[name] isa AbstractDict || continue
for idx in keys(dict[name])
if dict[name][idx] isa AffExpr
continue
end
idx_str = join(map(string, idx), ",")
set_name(dict[name][idx], "$name[$idx_str]")
end
end
end

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# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using JuMP
function resolve(model_old, filename::AbstractString; kwargs...)::OrderedDict
@info "Reading $filename..."
instance = RELOG.parsefile(filename)
return resolve(model_old, instance; kwargs...)
end
function resolve(model_old, instance::Instance; optimizer = nothing)::OrderedDict
milp_optimizer = lp_optimizer = optimizer
if optimizer === nothing
milp_optimizer = _get_default_milp_optimizer()
lp_optimizer = _get_default_lp_optimizer()
end
@info "Building new graph..."
graph = build_graph(instance)
_print_graph_stats(instance, graph)
@info "Building new optimization model..."
model_new = RELOG.build_model(instance, graph, milp_optimizer)
@info "Fixing decision variables..."
_fix_plants!(model_old, model_new)
JuMP.set_optimizer(model_new, lp_optimizer)
@info "Optimizing MILP..."
JuMP.optimize!(model_new)
if !has_values(model_new)
@warn("No solution available")
return OrderedDict()
end
@info "Extracting solution..."
solution = get_solution(model_new, marginal_costs = true)
return solution
end
function _fix_plants!(model_old, model_new)::Nothing
T = model_new[:instance].time
# Fix open_plant variables
for ((node_old, t), var_old) in model_old[:open_plant]
value_old = JuMP.value(var_old)
node_new = model_new[:graph].name_to_process_node_map[(
node_old.location.plant_name,
node_old.location.location_name,
)]
var_new = model_new[:open_plant][node_new, t]
JuMP.unset_binary(var_new)
JuMP.fix(var_new, value_old)
end
# Fix is_open variables
for ((node_old, t), var_old) in model_old[:is_open]
value_old = JuMP.value(var_old)
node_new = model_new[:graph].name_to_process_node_map[(
node_old.location.plant_name,
node_old.location.location_name,
)]
var_new = model_new[:is_open][node_new, t]
JuMP.unset_binary(var_new)
JuMP.fix(var_new, value_old)
end
# Fix plant capacities
for ((node_old, t), var_old) in model_old[:capacity]
value_old = JuMP.value(var_old)
node_new = model_new[:graph].name_to_process_node_map[(
node_old.location.plant_name,
node_old.location.location_name,
)]
var_new = model_new[:capacity][node_new, t]
JuMP.delete_lower_bound(var_new)
JuMP.delete_upper_bound(var_new)
JuMP.fix(var_new, value_old)
end
# Fix plant expansion
for ((node_old, t), var_old) in model_old[:expansion]
value_old = JuMP.value(var_old)
node_new = model_new[:graph].name_to_process_node_map[(
node_old.location.plant_name,
node_old.location.location_name,
)]
var_new = model_new[:expansion][node_new, t]
JuMP.delete_lower_bound(var_new)
JuMP.delete_upper_bound(var_new)
JuMP.fix(var_new, value_old)
end
end

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@@ -1,109 +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 JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
function _get_default_milp_optimizer()
return optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
end
function _get_default_lp_optimizer()
return optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
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(
instance::Instance;
optimizer = nothing,
output = nothing,
marginal_costs = true,
return_model = false,
)
milp_optimizer = lp_optimizer = optimizer
if optimizer == nothing
milp_optimizer = _get_default_milp_optimizer()
lp_optimizer = _get_default_lp_optimizer()
end
@info "Building graph..."
graph = RELOG.build_graph(instance)
_print_graph_stats(instance, graph)
@info "Building optimization model..."
model = RELOG.build_model(instance, graph, milp_optimizer)
@info "Optimizing MILP..."
JuMP.optimize!(model)
if !has_values(model)
error("No solution available")
end
if marginal_costs
@info "Re-optimizing with integer variables fixed..."
all_vars = JuMP.all_variables(model)
vals = OrderedDict(var => JuMP.value(var) for var in all_vars)
JuMP.set_optimizer(model, lp_optimizer)
for var in all_vars
if JuMP.is_binary(var)
JuMP.unset_binary(var)
JuMP.fix(var, vals[var])
end
end
JuMP.optimize!(model)
end
@info "Extracting solution..."
solution = get_solution(model, marginal_costs = marginal_costs)
if output != nothing
write(solution, output)
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 = solve(compressed; output = nothing, marginal_costs = false, 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

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# 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

@@ -1,38 +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 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

@@ -1,66 +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 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

@@ -5,75 +5,98 @@
using DataFrames
using CSV
function plants_report(solution)::DataFrame
function plants_report(model)::DataFrame
df = DataFrame()
df."plant type" = String[]
df."location name" = String[]
df."plant" = String[]
df."latitude" = Float64[]
df."longitude" = Float64[]
df."initial capacity" = Float64[]
df."current capacity" = Float64[]
df."year" = Int[]
df."latitude (deg)" = Float64[]
df."longitude (deg)" = Float64[]
df."capacity (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."operational?" = Bool[]
df."input amount (tonne)" = Float64[]
df."opening cost (\$)" = Float64[]
df."expansion cost (\$)" = Float64[]
df."fixed operating cost (\$)" = Float64[]
df."variable operating cost (\$)" = Float64[]
df."storage cost (\$)" = Float64[]
df."total 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 year = 1:T
capacity = round(location_dict["Capacity (tonne)"][year], digits = 2)
received = round(location_dict["Total input (tonne)"][year], digits = 2)
processed = round(location_dict["Process (tonne)"][year], digits = 2)
in_storage = round(location_dict["Storage (tonne)"][year], digits = 2)
utilization_factor = round(processed / capacity * 100.0, digits = 2)
energy = round(location_dict["Energy (GJ)"][year], digits = 2)
latitude = round(location_dict["Latitude (deg)"], digits = 6)
longitude = round(location_dict["Longitude (deg)"], digits = 6)
opening_cost = round(location_dict["Opening cost (\$)"][year], digits = 2)
expansion_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,
)
push!(
df,
[
plant_name,
location_name,
year,
latitude,
longitude,
capacity,
processed,
received,
in_storage,
utilization_factor,
energy,
opening_cost,
expansion_cost,
fixed_cost,
var_cost,
storage_cost,
total_cost,
],
)
end
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, t in T
operational = JuMP.value(model[:x][p.name, t]) > 0.5
input = value(model[:z_input][p.name, t])
# Opening cost
opening_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
# Plant size
curr_capacity = 0
if operational
curr_capacity = p.capacities[1].size
end
fix_operating_cost = (operational ? p.capacities[1].fix_operating_cost[t] : 0)
var_operating_cost = input * p.capacities[1].var_operating_cost[t]
push!(
df,
Dict(
"plant" => p.name,
"latitude" => p.latitude,
"longitude" => p.longitude,
"initial capacity" => p.initial_capacity,
"current capacity" => curr_capacity,
"year" => t,
"operational?" => operational,
"input amount (tonne)" => _round(input),
"opening cost (\$)" => _round(opening_cost),
"fixed operating cost (\$)" => _round(fix_operating_cost),
"variable operating cost (\$)" => _round(var_operating_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
write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution))
write_plant_outputs_report(solution, filename) =
CSV.write(filename, plant_outputs_report(solution))

View File

@@ -1,43 +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 products_report(solution; marginal_costs = true)::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[]
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 = location_dict["Marginal cost (\$/tonne)"][year]
latitude = round(location_dict["Latitude (deg)"], digits = 6)
longitude = round(location_dict["Longitude (deg)"], digits = 6)
amount = location_dict["Amount (tonne)"][year]
push!(
df,
[
prod_name,
location_name,
latitude,
longitude,
year,
amount,
marginal_cost,
],
)
end
end
end
return df
end
write_products_report(solution, filename) = CSV.write(filename, products_report(solution))

View File

@@ -1,75 +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(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

@@ -1,71 +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_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

@@ -0,0 +1,99 @@
# 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_tr_em][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))

View File

@@ -1,14 +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
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

View File

@@ -1,197 +0,0 @@
{
"$schema": "http://json-schema.org/draft-07/schema#",
"$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": {
"$ref": "#/definitions/InitialAmount"
}
},
"required": [
"transportation cost ($/km/tonne)"
]
}
}
},
"type": "object",
"properties": {
"parameters": {
"$ref": "#/definitions/Parameters"
},
"plants": {
"$ref": "#/definitions/Plant"
},
"products": {
"$ref": "#/definitions/Product"
}
},
"required": [
"parameters",
"plants",
"products"
]
}

View File

@@ -1,15 +0,0 @@
using PackageCompiler
using Cbc
using Clp
using Geodesy
using JSON
using JSONSchema
using JuMP
using MathOptInterface
using ProgressBars
pkg = [:Cbc, :Clp, :Geodesy, :JSON, :JSONSchema, :JuMP, :MathOptInterface, :ProgressBars]
@info "Building system image..."
create_sysimage(pkg, sysimage_path = "build/sysimage.so")

14
test/Project.toml Normal file
View File

@@ -0,0 +1,14 @@
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"

186
test/fixtures/boat_example.jl vendored Normal file
View File

@@ -0,0 +1,186 @@
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_outputs_report(model, fixture("boat_example/plant_outputs.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

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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

81
test/fixtures/boat_example/centers.csv vendored Normal file
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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

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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

101
test/fixtures/boat_example/plants.csv vendored Normal file
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plant,latitude,longitude,initial capacity,current capacity,year,operational?,input amount (tonne),opening cost ($),fixed operating cost ($),variable operating cost ($)
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,1,false,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
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,3,false,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
BoatFactory (Chicago),41.881832,-87.623177,0.0,0.0,5,false,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
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,2,false,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
BoatFactory (New York City),40.712776,-74.005974,0.0,0.0,4,false,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
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,1,false,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
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,3,false,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
BoatFactory (Los Angeles),34.052235,-118.243683,0.0,0.0,5,false,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
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,2,false,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
BoatFactory (Houston),29.760427,-95.369804,0.0,0.0,4,false,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
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,1,false,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
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,3,false,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
BoatFactory (Phoenix),33.448376,-112.074036,0.0,0.0,5,false,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
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,2,false,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
BoatFactory (Philadelphia),39.952583,-75.165222,0.0,0.0,4,false,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
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,1,false,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
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,3,false,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
BoatFactory (San Antonio),29.424122,-98.493629,0.0,0.0,5,false,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
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,2,false,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
BoatFactory (San Diego),32.715736,-117.161087,0.0,0.0,4,false,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
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,1,true,63.15789,100000.0,250000.0,315.78947
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,2,true,71.46814,0.0,250000.0,357.34072
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,3,true,75.8857,0.0,250000.0,379.42849
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,4,true,76.90434,0.0,250000.0,384.52168
BoatFactory (Dallas),32.776664,-96.796988,0.0,500.0,5,true,77.27087,0.0,250000.0,386.35435
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,1,false,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
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,3,false,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
BoatFactory (San Jose),37.338208,-121.886329,0.0,0.0,5,false,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
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,2,false,-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
RecyclingPlant (Chicago),41.881832,-87.623177,0.0,0.0,4,false,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
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,1,false,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
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,3,false,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
RecyclingPlant (New York City),40.712776,-74.005974,0.0,0.0,5,false,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
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,2,false,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
RecyclingPlant (Los Angeles),34.052235,-118.243683,0.0,0.0,4,false,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
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,1,false,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
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,3,false,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
RecyclingPlant (Houston),29.760427,-95.369804,0.0,0.0,5,false,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
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,2,false,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
RecyclingPlant (Phoenix),33.448376,-112.074036,0.0,0.0,4,false,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
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,1,false,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
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,3,false,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
RecyclingPlant (Philadelphia),39.952583,-75.165222,0.0,0.0,5,false,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
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,2,false,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
RecyclingPlant (San Antonio),29.424122,-98.493629,0.0,0.0,4,false,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
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,1,false,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
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,3,false,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
RecyclingPlant (San Diego),32.715736,-117.161087,0.0,0.0,5,false,0.0,0.0,0.0,0.0
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,1,true,6.31579,500000.0,125000.0,15.78947
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,2,true,22.93629,0.0,125000.0,57.34072
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,3,true,31.7714,0.0,125000.0,79.42849
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,4,true,33.80867,0.0,125000.0,84.52168
RecyclingPlant (Dallas),32.776664,-96.796988,0.0,500.0,5,true,34.54174,0.0,125000.0,86.35435
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,1,false,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
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,3,false,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
RecyclingPlant (San Jose),37.338208,-121.886329,0.0,0.0,5,false,0.0,0.0,0.0,0.0
1 plant latitude longitude initial capacity current capacity year operational? input amount (tonne) opening cost ($) fixed operating cost ($) variable operating cost ($)
2 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 1 false 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
4 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 3 false 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
6 BoatFactory (Chicago) 41.881832 -87.623177 0.0 0.0 5 false 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
8 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 2 false 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
10 BoatFactory (New York City) 40.712776 -74.005974 0.0 0.0 4 false 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
12 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 1 false 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
14 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 3 false 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
16 BoatFactory (Los Angeles) 34.052235 -118.243683 0.0 0.0 5 false 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
18 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 2 false 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
20 BoatFactory (Houston) 29.760427 -95.369804 0.0 0.0 4 false 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
22 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 1 false 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
24 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 3 false 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
26 BoatFactory (Phoenix) 33.448376 -112.074036 0.0 0.0 5 false 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
28 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 2 false 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
30 BoatFactory (Philadelphia) 39.952583 -75.165222 0.0 0.0 4 false 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
32 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 1 false 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
34 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 3 false 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
36 BoatFactory (San Antonio) 29.424122 -98.493629 0.0 0.0 5 false 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
38 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 2 false 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
40 BoatFactory (San Diego) 32.715736 -117.161087 0.0 0.0 4 false 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
42 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 1 true 63.15789 100000.0 250000.0 315.78947
43 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 2 true 71.46814 0.0 250000.0 357.34072
44 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 3 true 75.8857 0.0 250000.0 379.42849
45 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 4 true 76.90434 0.0 250000.0 384.52168
46 BoatFactory (Dallas) 32.776664 -96.796988 0.0 500.0 5 true 77.27087 0.0 250000.0 386.35435
47 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 1 false 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
49 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 3 false 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
51 BoatFactory (San Jose) 37.338208 -121.886329 0.0 0.0 5 false 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
53 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 2 false -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
55 RecyclingPlant (Chicago) 41.881832 -87.623177 0.0 0.0 4 false 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
57 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 1 false 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
59 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 3 false 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
61 RecyclingPlant (New York City) 40.712776 -74.005974 0.0 0.0 5 false 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
63 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 2 false 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
65 RecyclingPlant (Los Angeles) 34.052235 -118.243683 0.0 0.0 4 false 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
67 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 1 false 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
69 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 3 false 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
71 RecyclingPlant (Houston) 29.760427 -95.369804 0.0 0.0 5 false 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
73 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 2 false 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
75 RecyclingPlant (Phoenix) 33.448376 -112.074036 0.0 0.0 4 false 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
77 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 1 false 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
79 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 3 false 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
81 RecyclingPlant (Philadelphia) 39.952583 -75.165222 0.0 0.0 5 false 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
83 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 2 false 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
85 RecyclingPlant (San Antonio) 29.424122 -98.493629 0.0 0.0 4 false 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
87 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 1 false 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
89 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 3 false 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
91 RecyclingPlant (San Diego) 32.715736 -117.161087 0.0 0.0 5 false 0.0 0.0 0.0 0.0
92 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 1 true 6.31579 500000.0 125000.0 15.78947
93 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 2 true 22.93629 0.0 125000.0 57.34072
94 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 3 true 31.7714 0.0 125000.0 79.42849
95 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 4 true 33.80867 0.0 125000.0 84.52168
96 RecyclingPlant (Dallas) 32.776664 -96.796988 0.0 500.0 5 true 34.54174 0.0 125000.0 86.35435
97 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 1 false 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
99 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 3 false 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
101 RecyclingPlant (San Jose) 37.338208 -121.886329 0.0 0.0 5 false 0.0 0.0 0.0 0.0

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@@ -0,0 +1,81 @@
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

@@ -0,0 +1,41 @@
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

View File

@@ -1,202 +0,0 @@
{
"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]
}
}
}
}
}
}
}

159
test/fixtures/simple.json vendored Normal file
View File

@@ -0,0 +1,159 @@
{
"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)": 0
}
}
}

View File

@@ -1,39 +0,0 @@
{
"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]
}
}
}
}
}
}
}

View File

@@ -1,39 +0,0 @@
# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
@testset "build_graph" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../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

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
@testset "compress" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../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

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# 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
@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

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
@testset "parse" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../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]
p2 = product_name_to_product["P2"]
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
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../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
# basedir = dirname(@__FILE__)
# @test_throws ErrorException RELOG.parsefile("$basedir/../fixtures/s1-wrong-length.json")
# end

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
@testset "build" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../instances/s1.json")
graph = RELOG.build_graph(instance)
model = RELOG.build_model(instance, graph, Cbc.Optimizer)
set_optimizer_attribute(model, "logLevel", 0)
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[:flow]) == 76
@test length(model[:dispose]) == 16
@test length(model[:open_plant]) == 12
@test length(model[:capacity]) == 12
@test length(model[:expansion]) == 12
l1 = process_node_by_location_name["L1"]
v = model[:capacity][l1, 1]
@test lower_bound(v) == 0.0
@test upper_bound(v) == 1000.0
v = model[:expansion][l1, 1]
@test lower_bound(v) == 0.0
@test upper_bound(v) == 750.0
v = model[:dispose][shipping_node_by_loc_and_prod_names["L1", "P2"], 1]
@test lower_bound(v) == 0.0
@test upper_bound(v) == 1.0
end

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG
@testset "Resolve" begin
# Shoud not crash
filename = "$(pwd())/../instances/s1.json"
solution_old, model_old = RELOG.solve(filename, return_model = true)
solution_new = RELOG.resolve(model_old, filename)
end

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
basedir = dirname(@__FILE__)
@testset "solve (exact)" begin
solution_filename_a = tempname()
solution_filename_b = tempname()
solution = RELOG.solve("$basedir/../../instances/s1.json", output = solution_filename_a)
@test isfile(solution_filename_a)
RELOG.write(solution, solution_filename_b)
@test isfile(solution_filename_b)
@test "Costs" in keys(solution)
@test "Fixed operating (\$)" in keys(solution["Costs"])
@test "Transportation (\$)" in keys(solution["Costs"])
@test "Variable operating (\$)" in keys(solution["Costs"])
@test "Total (\$)" in keys(solution["Costs"])
@test "Plants" in keys(solution)
@test "F1" in keys(solution["Plants"])
@test "F2" in keys(solution["Plants"])
@test "F3" in keys(solution["Plants"])
@test "F4" in keys(solution["Plants"])
end
@testset "solve (heuristic)" begin
# Should not crash
solution = RELOG.solve("$basedir/../../instances/s1.json", heuristic = true)
end
@testset "solve (infeasible)" begin
json = JSON.parsefile("$basedir/../../instances/s1.json")
for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
location_dict["amount (tonne)"] *= 1000
end
@test_throws ErrorException("No solution available") RELOG.solve(RELOG.parse(json))
end
@testset "solve (with storage)" begin
basedir = dirname(@__FILE__)
filename = "$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

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# 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
@testset "Reports" begin
@testset "from solve" begin
solution = RELOG.solve("$(pwd())/../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

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# Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov>
using Test
@testset "RELOG" begin
@testset "Instance" begin
include("instance/compress_test.jl")
include("instance/geodb_test.jl")
include("instance/parse_test.jl")
end
@testset "Graph" begin
include("graph/build_test.jl")
end
@testset "Model" begin
include("model/build_test.jl")
include("model/solve_test.jl")
include("model/resolve_test.jl")
end
include("reports_test.jl")
end

36
test/src/RELOGT.jl Normal file
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module RELOGT
using Test
using RELOG
using JuliaFormatter
include("instance/parse_test.jl")
include("model/build_test.jl")
include("model/dist_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()
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

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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 == 0
@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]
end
function instance_parse_test_2()
# Should not crash
RELOG.parsefile(fixture("boat_example.json"))
end

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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_tr_em = model[:z_tr_em]
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
)
@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
)
# Variables: Transportation emissions
@test haskey(z_tr_em, ("CO2", "L1", "C3", "P4", 1))
@test haskey(z_tr_em, ("CH4", "L1", "C3", "P4", 1))
@test haskey(z_tr_em, ("CO2", "C2", "L1", "P1", 1))
@test haskey(z_tr_em, ("CH4", "C2", "L1", "P1", 1))
# 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: Must meet input mix
@test repr(model[:eq_input_mix]["L1", "P1", 1]) ==
"eq_input_mix[L1,P1,1] : y[C2,L1,P1,1] - 0.953 z_input[L1,1] = 0"
@test repr(model[:eq_input_mix]["L1", "P2", 1]) ==
"eq_input_mix[L1,P2,1] : y[C1,L1,P2,1] - 0.047 z_input[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_input[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_input[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: Capacity limit
@test repr(model[:eq_capacity]["L1", 1]) ==
"eq_capacity[L1,1] : -100 x[L1,1] + z_input[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] ≥ 0"
# 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_tr_em]["CH4", "L1", "C3", "P4", 1]) ==
"eq_tr_em[CH4,L1,C3,P4,1] : -0.333354 y[L1,C3,P4,1] + z_tr_em[CH4,L1,C3,P4,1] = 0"
end

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# 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

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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_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

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FAST_REFRESH=false

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web/.gitignore vendored Normal file
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# 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

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{}

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web/package.json Normal file
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{
"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"
}
}

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web/public/index.html Normal file
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<!--
~ 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>

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{
"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"
}

3
web/public/robots.txt Normal file
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# https://www.robotstxt.org/robotstxt.html
User-agent: *
Disallow:

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/*
* 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;

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@@ -0,0 +1,14 @@
/*
* 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;
}

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@@ -0,0 +1,18 @@
/*
* 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;

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/*
* 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;
}

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/*
* 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;

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/*
* 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;

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/*
* 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;
}

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/*
* 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;

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/*
* 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.
*/
.SectionButton {
height: 48px;
width: 48px;
font-size: 16px;
border: 0;
background-color: transparent;
margin: 8px 0 8px 0px;
cursor: pointer;
color: var(--contrast-60);
}
.SectionButton:hover {
color: var(--contrast-100);
background-color: var(--contrast-20);
border-radius: var(--border-radius);
}
.SectionButton:active {
background-color: var(--contrast-60);
}

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/*
* 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 { IconDefinition } from "@fortawesome/fontawesome-svg-core";
import { FontAwesomeIcon } from "@fortawesome/react-fontawesome";
import styles from "./SectionButton.module.css";
interface SectionButtonProps {
icon: IconDefinition;
tooltip: string;
onClick?: () => void;
}
function SectionButton(props: SectionButtonProps) {
return (
<button
className={styles.SectionButton}
title={props.tooltip}
onClick={props.onClick}
>
<FontAwesomeIcon icon={props.icon} />
</button>
);
}
export default SectionButton;

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