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| 823db2838b |
4
.dockerignore
Normal file
4
.dockerignore
Normal file
@@ -0,0 +1,4 @@
|
||||
build
|
||||
jobs
|
||||
relog-web/node_modules
|
||||
relog-web/build
|
||||
27
.github/workflows/lint.yml
vendored
Normal file
27
.github/workflows/lint.yml
vendored
Normal file
@@ -0,0 +1,27 @@
|
||||
name: Lint
|
||||
on:
|
||||
push:
|
||||
pull_request:
|
||||
jobs:
|
||||
build:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: julia-actions/setup-julia@latest
|
||||
with:
|
||||
version: '1'
|
||||
- uses: actions/checkout@v1
|
||||
- name: Format check
|
||||
shell: julia --color=yes {0}
|
||||
run: |
|
||||
using Pkg
|
||||
Pkg.add(PackageSpec(name="JuliaFormatter", version="1"))
|
||||
using JuliaFormatter
|
||||
format("src", verbose=true)
|
||||
format("test/src", verbose=true)
|
||||
out = String(read(Cmd(`git diff`)))
|
||||
if isempty(out)
|
||||
exit(0)
|
||||
end
|
||||
@error "Some files have not been formatted !!!"
|
||||
write(stdout, out)
|
||||
exit(1)
|
||||
15
.github/workflows/tagbot.yml
vendored
Normal file
15
.github/workflows/tagbot.yml
vendored
Normal file
@@ -0,0 +1,15 @@
|
||||
name: TagBot
|
||||
on:
|
||||
issue_comment:
|
||||
types:
|
||||
- created
|
||||
workflow_dispatch:
|
||||
jobs:
|
||||
TagBot:
|
||||
if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- uses: JuliaRegistries/TagBot@v1
|
||||
with:
|
||||
token: ${{ secrets.GITHUB_TOKEN }}
|
||||
ssh: ${{ secrets.DOCUMENTER_KEY }}
|
||||
22
.github/workflows/test.yml
vendored
22
.github/workflows/test.yml
vendored
@@ -1,14 +1,16 @@
|
||||
name: Build & Test
|
||||
on:
|
||||
- push
|
||||
- pull_request
|
||||
push:
|
||||
pull_request:
|
||||
schedule:
|
||||
- cron: '45 10 * * *'
|
||||
jobs:
|
||||
test:
|
||||
name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
||||
version: ['1.3', '1.4', '1.5', 'nightly']
|
||||
version: ['1.6', '1.7', '1.8']
|
||||
os:
|
||||
- ubuntu-latest
|
||||
arch:
|
||||
@@ -19,5 +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
|
||||
|
||||
9
.gitignore
vendored
9
.gitignore
vendored
@@ -8,3 +8,12 @@ instances/*.py
|
||||
notebooks
|
||||
.idea
|
||||
*.lp
|
||||
Manifest.toml
|
||||
data
|
||||
build
|
||||
benchmark
|
||||
run.jl
|
||||
relog-web-legacy
|
||||
.vscode
|
||||
jobs
|
||||
**/tmp
|
||||
|
||||
28
.zenodo.json
Normal file
28
.zenodo.json
Normal file
@@ -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."
|
||||
}
|
||||
28
CHANGELOG.md
28
CHANGELOG.md
@@ -1,28 +0,0 @@
|
||||
# Version 0.5.0 (TBD)
|
||||
|
||||
- Allow plants to store input material for processing in later years
|
||||
|
||||
# Version 0.4.0 (Sep 18, 2020)
|
||||
|
||||
- Generate simplified solution reports (CSV)
|
||||
|
||||
# Version 0.3.3 (Aug 13, 2020)
|
||||
|
||||
- Add option to write solution to JSON file in RELOG.solve
|
||||
- Improve error message when instance is infeasible
|
||||
- Make output file more readable
|
||||
|
||||
# Version 0.3.2 (Aug 7, 2020)
|
||||
|
||||
- Add "building period" parameter
|
||||
|
||||
# Version 0.3.1 (July 17, 2020)
|
||||
|
||||
- Fix expansion cost breakdown
|
||||
|
||||
# Version 0.3.0 (June 25, 2020)
|
||||
|
||||
- Track emissions and energy (transportation and plants)
|
||||
- Minor changes to input file format:
|
||||
- Make all dictionary keys lowercase
|
||||
- Rename "outputs (tonne)" to "outputs (tonne/tonne)"
|
||||
25
COPYING.md
25
COPYING.md
@@ -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.
|
||||
|
||||
********************************************************************************
|
||||
25
Makefile
25
Makefile
@@ -1,25 +0,0 @@
|
||||
JULIA := julia --color=yes --project=@.
|
||||
SRC_FILES := $(wildcard src/*.jl test/*.jl)
|
||||
VERSION := 0.5
|
||||
|
||||
all: docs test
|
||||
|
||||
build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
|
||||
mkdir -p build
|
||||
$(JULIA) src/sysimage.jl
|
||||
|
||||
build/test.log: $(SRC_FILES) build/sysimage.so
|
||||
cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl
|
||||
|
||||
clean:
|
||||
rm -rf build/*
|
||||
|
||||
docs:
|
||||
mkdocs build -d ../docs/$(VERSION)/
|
||||
|
||||
test: build/test.log
|
||||
|
||||
test-watch:
|
||||
bash -c "while true; do make test --quiet; sleep 1; done"
|
||||
|
||||
.PHONY: docs test
|
||||
441
Manifest.toml
441
Manifest.toml
@@ -1,441 +0,0 @@
|
||||
# This file is machine-generated - editing it directly is not advised
|
||||
|
||||
[[Base64]]
|
||||
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
|
||||
|
||||
[[BenchmarkTools]]
|
||||
deps = ["JSON", "Logging", "Printf", "Statistics", "UUIDs"]
|
||||
git-tree-sha1 = "9e62e66db34540a0c919d72172cc2f642ac71260"
|
||||
uuid = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
|
||||
version = "0.5.0"
|
||||
|
||||
[[BinaryProvider]]
|
||||
deps = ["Libdl", "Logging", "SHA"]
|
||||
git-tree-sha1 = "ecdec412a9abc8db54c0efc5548c64dfce072058"
|
||||
uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
|
||||
version = "0.5.10"
|
||||
|
||||
[[Bzip2_jll]]
|
||||
deps = ["Libdl", "Pkg"]
|
||||
git-tree-sha1 = "03a44490020826950c68005cafb336e5ba08b7e8"
|
||||
uuid = "6e34b625-4abd-537c-b88f-471c36dfa7a0"
|
||||
version = "1.0.6+4"
|
||||
|
||||
[[CEnum]]
|
||||
git-tree-sha1 = "215a9aa4a1f23fbd05b92769fdd62559488d70e9"
|
||||
uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
|
||||
version = "0.4.1"
|
||||
|
||||
[[CSV]]
|
||||
deps = ["CategoricalArrays", "DataFrames", "Dates", "Mmap", "Parsers", "PooledArrays", "SentinelArrays", "Tables", "Unicode"]
|
||||
git-tree-sha1 = "a390152e6850405a48ca51bd7ca33d11a21d6230"
|
||||
uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
|
||||
version = "0.7.7"
|
||||
|
||||
[[Calculus]]
|
||||
deps = ["LinearAlgebra"]
|
||||
git-tree-sha1 = "f641eb0a4f00c343bbc32346e1217b86f3ce9dad"
|
||||
uuid = "49dc2e85-a5d0-5ad3-a950-438e2897f1b9"
|
||||
version = "0.5.1"
|
||||
|
||||
[[CategoricalArrays]]
|
||||
deps = ["DataAPI", "Future", "JSON", "Missings", "Printf", "Statistics", "StructTypes", "Unicode"]
|
||||
git-tree-sha1 = "2ac27f59196a68070e132b25713f9a5bbc5fa0d2"
|
||||
uuid = "324d7699-5711-5eae-9e2f-1d82baa6b597"
|
||||
version = "0.8.3"
|
||||
|
||||
[[Cbc]]
|
||||
deps = ["BinaryProvider", "Libdl", "MathOptInterface", "MathProgBase", "SparseArrays", "Test"]
|
||||
git-tree-sha1 = "62d80f448b5d77b3f0a59cecf6197aad2a3aa280"
|
||||
uuid = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
|
||||
version = "0.6.7"
|
||||
|
||||
[[Clp]]
|
||||
deps = ["BinaryProvider", "CEnum", "Clp_jll", "Libdl", "MathOptInterface", "SparseArrays"]
|
||||
git-tree-sha1 = "581763750759c1e38df2a35a0b3bdee496a062c7"
|
||||
uuid = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
|
||||
version = "0.8.1"
|
||||
|
||||
[[Clp_jll]]
|
||||
deps = ["CoinUtils_jll", "CompilerSupportLibraries_jll", "Libdl", "OpenBLAS32_jll", "Osi_jll", "Pkg"]
|
||||
git-tree-sha1 = "79263d9383ca89b35f31c33ab5b880536a8413a4"
|
||||
uuid = "06985876-5285-5a41-9fcb-8948a742cc53"
|
||||
version = "1.17.6+6"
|
||||
|
||||
[[CodecBzip2]]
|
||||
deps = ["Bzip2_jll", "Libdl", "TranscodingStreams"]
|
||||
git-tree-sha1 = "2e62a725210ce3c3c2e1a3080190e7ca491f18d7"
|
||||
uuid = "523fee87-0ab8-5b00-afb7-3ecf72e48cfd"
|
||||
version = "0.7.2"
|
||||
|
||||
[[CodecZlib]]
|
||||
deps = ["TranscodingStreams", "Zlib_jll"]
|
||||
git-tree-sha1 = "ded953804d019afa9a3f98981d99b33e3db7b6da"
|
||||
uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
|
||||
version = "0.7.0"
|
||||
|
||||
[[CoinUtils_jll]]
|
||||
deps = ["CompilerSupportLibraries_jll", "Libdl", "OpenBLAS32_jll", "Pkg"]
|
||||
git-tree-sha1 = "ee1f06ab89337b7f194c29377ab174e752cdf60d"
|
||||
uuid = "be027038-0da8-5614-b30d-e42594cb92df"
|
||||
version = "2.11.3+3"
|
||||
|
||||
[[CommonSubexpressions]]
|
||||
deps = ["MacroTools", "Test"]
|
||||
git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7"
|
||||
uuid = "bbf7d656-a473-5ed7-a52c-81e309532950"
|
||||
version = "0.3.0"
|
||||
|
||||
[[Compat]]
|
||||
deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "SHA", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"]
|
||||
git-tree-sha1 = "7c7f4cda0d58ec999189d70f5ee500348c4b4df1"
|
||||
uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
|
||||
version = "3.16.0"
|
||||
|
||||
[[CompilerSupportLibraries_jll]]
|
||||
deps = ["Libdl", "Pkg"]
|
||||
git-tree-sha1 = "7c4f882c41faa72118841185afc58a2eb00ef612"
|
||||
uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
|
||||
version = "0.3.3+0"
|
||||
|
||||
[[CoordinateTransformations]]
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34
Project.toml
34
Project.toml
@@ -1,39 +1,13 @@
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||||
Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
|
||||
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
|
||||
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
|
||||
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"
|
||||
PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
|
||||
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
|
||||
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
|
||||
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
|
||||
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||
|
||||
[compat]
|
||||
CSV = "0.7"
|
||||
Cbc = "0.6"
|
||||
Clp = "0.8"
|
||||
DataFrames = "0.21"
|
||||
DataStructures = "0.17"
|
||||
GZip = "0.5"
|
||||
Geodesy = "0.5"
|
||||
JSON = "0.21"
|
||||
JSONSchema = "0.2"
|
||||
JuMP = "0.21"
|
||||
MathOptInterface = "0.9"
|
||||
PackageCompiler = "1"
|
||||
ProgressBars = "0.6"
|
||||
julia = "1"
|
||||
|
||||
59
README.md
59
README.md
@@ -1,59 +0,0 @@
|
||||
<h1 align="center">RELOG: Reverse Logistics 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>
|
||||
<a href="https://github.com/ANL-CEEESA/RELOG/releases/">
|
||||
<img src="https://img.shields.io/github/v/release/ANL-CEEESA/RELOG?include_prereleases&label=pre-release">
|
||||
</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.
|
||||
|
||||
|
||||
|
||||
<img src="https://anl-ceeesa.github.io/RELOG/0.5/images/ex_transportation.png" width="1000px"/>
|
||||
|
||||
### Documentation
|
||||
|
||||
* [Usage](https://anl-ceeesa.github.io/RELOG/0.5/usage)
|
||||
* [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.5/format)
|
||||
* [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.5/reports)
|
||||
* [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.5/model)
|
||||
|
||||
### Authors
|
||||
|
||||
* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
|
||||
* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
|
||||
|
||||
### License
|
||||
|
||||
```text
|
||||
RELOG: Reverse Logistics Optimization
|
||||
Copyright © 2020, 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:
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
```
|
||||
306
docs/format.md
Normal file
306
docs/format.md
Normal file
@@ -0,0 +1,306 @@
|
||||
# RELOG: Composition Format
|
||||
|
||||
## Input Data Format (JSON)
|
||||
|
||||
## Glossary of types
|
||||
|
||||
| Type | Description | Example |
|
||||
| --------------------- | -------------------------------------------------------- | ------------------------ |
|
||||
| `int` | An integer number. | `1` |
|
||||
| `float` | A real number. | `3.1415` |
|
||||
| `str` | A string. | `"Euclidean"` |
|
||||
| `vec(int)` | A vector of integer numbers, with any length. | `[1, 2, 3]` |
|
||||
| `vec(int, 5)` | A vector of integer numbers, with 5 elements. | `[1, 2, 3, 4, 5]` |
|
||||
| `mat(float, 2, 3, 4)` | A matrix of floating point numbers with shape (2, 3, 5). | `rand(Float64, 2, 3, 4)` |
|
||||
| `dict(str, int)` | A dictionary mapping strings to integer numbers. | `Dict("A" => 1)` |
|
||||
|
||||
### Parameters
|
||||
|
||||
| Key | Type | Description |
|
||||
| :------------------------ | ---------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `time horizon (years)` | `int` | Number of years in the simulation. |
|
||||
| `building period (years)` | `vec(int)` | 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` | `str` | 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)": 4,
|
||||
"building period (years)": [1],
|
||||
"distance metric": "driving"
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Products
|
||||
|
||||
| Key | Type | Description |
|
||||
| :------------------------------------------ | :------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
|
||||
| `transportation cost ($/km/tonne)` | `vec(float, T)` | The cost (in dollars) to transport one tonne of the product over one kilometer at time $t$. |
|
||||
| `transportation energy (J/km/tonne)` | `vec(float, T)` | The energy (in J) required to transport this product at time $t$. |
|
||||
| `transportation emissions (tonne/km/tonne)` | `dict(str, vec(float, T))` | A dictionary mapping the name of each greenhouse gas (produced during the transportation of one tonne of this product along one kilometer at time $t$) to the amount of gas produced (in tonnes). |
|
||||
| `components` | `vec(str)` | List of components for the product. |
|
||||
|
||||
#### Example
|
||||
|
||||
```json
|
||||
{
|
||||
"products": {
|
||||
"P1": {
|
||||
"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]
|
||||
},
|
||||
"components": ["P1a", "P1b", "P1c"]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Centers
|
||||
|
||||
| Key | Type | Description |
|
||||
| :------------------------------ | ------------------------------- | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `latitude (deg)` | `float` | The latitude of the center. |
|
||||
| `longitude (deg)` | `float` | The longitude of the center. |
|
||||
| `input` | `str` | The name of the product this center takes as input. May be `null` if the center accept no input product. |
|
||||
| `outputs` | `vec(str)` | List of output products collected by the center. May be `[]` if none. |
|
||||
| `fixed output (tonne)` | `dict(str, mat(float, T, C))` | Dictionary mapping the name of each output product $p$ to a matrix $M$, where $M_{t,c}$ is the amount (in tonne) of output product component $c$ produced by the center at time $t$, regardless of how much input material the center received. |
|
||||
| `variable output (tonne/tonne)` | `dict(str,mat(float, T, M, N))` | Dictionary mapping the name of each output product $p$ to a $(T \times m \times n)$ matrix $M$ that describes the amount (in tonnes) of output product component produced by the center, depending on how much input material the center received in prior years, where $T$ is the number of years, $m$ is the number of components of $p$ and $n$ is the number of components of the input product. |
|
||||
| `revenue ($/tonne)` | `vec(float, T)` | Revenue generated by each tonne of input material sent to the center. If the center accepts no input, this should be `null` |
|
||||
| `collection cost ($/tonne)` | `dict(str,vec(float,T))` | Dictionary mapping the name of each output product to the cost of collecting one tonne of the product. |
|
||||
| `operating cost ($)` | `vec(float,T)` | Fixed cost to operate the center for one year, regardless of amount of product received or generated. |
|
||||
| `disposal limit (tonne)` | `dict(str,vec(float,T))` | Dictionary mapping the name of each output product to the maximum disposal amount allower per year of the product at the center. Entry may be `null` if unlimited. |
|
||||
| `disposal cost ($/tonne)` | `dict(str,vec(float,T))` | 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": [
|
||||
[50, 20, 10],
|
||||
[5, 2, 1],
|
||||
[0, 0, 0],
|
||||
[0, 0, 0]
|
||||
],
|
||||
"P3": [
|
||||
[20, 10],
|
||||
[10, 5],
|
||||
[0, 0],
|
||||
[0, 0]
|
||||
]
|
||||
},
|
||||
"variable output (tonne/tonne)": {
|
||||
"P2": [
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
]
|
||||
],
|
||||
"P3": [
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
],
|
||||
[
|
||||
[1, 0, 0],
|
||||
[0, 1, 1]
|
||||
]
|
||||
]
|
||||
},
|
||||
"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"],
|
||||
"fixed output (tonne)": {
|
||||
"P4": [
|
||||
[50, 5],
|
||||
[60, 6],
|
||||
[70, 7],
|
||||
[80, 8]
|
||||
]
|
||||
},
|
||||
"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)` | `float` | The latitude of the plant, in degrees. |
|
||||
| `longitude (deg)` | `float` | The longitude of the plant, in degrees. |
|
||||
| `input mix (%)` | `dict(str,float)` | Dictionary mapping the name of each input product to the amount required (as a percentage). Must sum to 100%. |
|
||||
| `output (tonne/tonne)` | `dict(str,dict(str,mat(float, T, M, N)))` | Dictionary of matrices describing the component outputs. |
|
||||
| `processing emissions (tonne)` | `dict(str,vec(float,T))` | 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 ($/tonne)` | `dict(str,vec(float,T))` | Dictionary mapping the name of each input product to the cost of storing the product for one year at the plant for later processing. |
|
||||
| `storage limit (tonne)` | | Dictionary mapping the name of each input product to the maximum amount allowed in storage at any time. May be `null` if unlimited. |
|
||||
| `disposal cost ($/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 ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series. |
|
||||
| `initial capacity (tonne)` | Capacity already available. If the plant has not been built yet, this should be `0`. |
|
||||
|
||||
```json
|
||||
{
|
||||
"plants": {
|
||||
"L1": {
|
||||
"latitude (deg)": 41.881,
|
||||
"longitude (deg)": -87.623,
|
||||
"input mix (%)": {
|
||||
"P1": 95.3,
|
||||
"P2": 4.7
|
||||
},
|
||||
"output (tonne/tonne)": {
|
||||
"P3": {
|
||||
"P1": [
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]]
|
||||
],
|
||||
"P2": [
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]]
|
||||
]
|
||||
},
|
||||
"P4": {
|
||||
"P1": [
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]],
|
||||
[[1, 0, 0], [0, 1, 1]]
|
||||
],
|
||||
"P2": [
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]],
|
||||
[[0, 1], [1, 0]]
|
||||
]
|
||||
},
|
||||
"P5": {
|
||||
"P1": [[1, 0, 0], [0, 1, 1]],
|
||||
"P2": [[0, 1], [1, 0]],
|
||||
}
|
||||
},
|
||||
"processing emissions (tonne)": {
|
||||
"CO2": 0.1
|
||||
},
|
||||
"storage cost ($/tonne)": {
|
||||
"P1": 0.1,
|
||||
"P2": 0.1
|
||||
},
|
||||
"storage limit (tonne)": {
|
||||
"P1": 100,
|
||||
"P2": null
|
||||
},
|
||||
"disposal cost ($/tonne)": {
|
||||
"P3": 0,
|
||||
"P4": 0.86,
|
||||
"P5": 0.25,
|
||||
},
|
||||
"disposal limit (tonne)": {
|
||||
"P3": null,
|
||||
"P4": 1000.0,
|
||||
"P5": 1000.0
|
||||
},
|
||||
"capacities": [
|
||||
{
|
||||
"size": 100,
|
||||
"opening cost ($)": 500,
|
||||
"fixed operating cost ($)": 300,
|
||||
"variable operating cost ($/tonne)": 5.0
|
||||
},
|
||||
{
|
||||
"size": 500,
|
||||
"opening cost ($)": 1000.0,
|
||||
"fixed operating cost ($)": 400.0,
|
||||
"variable operating cost ($/tonne)": 5.0.
|
||||
}
|
||||
],
|
||||
"initial capacity (tonne)": 0,
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
@@ -1,202 +0,0 @@
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 2
|
||||
},
|
||||
"products": {
|
||||
"P1": {
|
||||
"transportation cost ($/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]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,950 +0,0 @@
|
||||
{
|
||||
"Energy": {
|
||||
"Plants (GJ)": [
|
||||
568.6368,
|
||||
521.2504
|
||||
],
|
||||
"Transportation (GJ)": [
|
||||
3.120910400232,
|
||||
2.860834533546
|
||||
]
|
||||
},
|
||||
"Costs": {
|
||||
"Variable operating ($)": [
|
||||
216672.818,
|
||||
216672.818
|
||||
],
|
||||
"Transportation ($)": [
|
||||
714499.27483131,
|
||||
714499.27483131
|
||||
],
|
||||
"Disposal ($)": [
|
||||
-20.0,
|
||||
-20.0
|
||||
],
|
||||
"Total ($)": [
|
||||
939896.86883131,
|
||||
931282.09283131
|
||||
],
|
||||
"Fixed operating ($)": [
|
||||
130.0,
|
||||
130.0
|
||||
],
|
||||
"Opening ($)": [
|
||||
4500.0,
|
||||
0.0
|
||||
],
|
||||
"Expansion ($)": [
|
||||
4114.776,
|
||||
0.0
|
||||
]
|
||||
},
|
||||
"Plants": {
|
||||
"F3": {
|
||||
"L5": {
|
||||
"Opening cost ($)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Emissions (tonne)": {},
|
||||
"Expansion cost ($)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Longitude (deg)": 100.0,
|
||||
"Energy (GJ)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Total output": {},
|
||||
"Capacity (tonne)": [
|
||||
15000.0,
|
||||
15000.0
|
||||
],
|
||||
"Latitude (deg)": 100.0,
|
||||
"Output": {
|
||||
"Send": {},
|
||||
"Dispose": {}
|
||||
},
|
||||
"Total input (tonne)": [
|
||||
757.3824000000001,
|
||||
757.3824000000001
|
||||
],
|
||||
"Fixed operating cost ($)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Input": {
|
||||
"F2": {
|
||||
"L4": {
|
||||
"Distance (km)": 8811.73,
|
||||
"Amount (tonne)": [
|
||||
757.3824000000001,
|
||||
757.3824000000001
|
||||
],
|
||||
"Transportation energy (J)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"Transportation cost ($)": [
|
||||
116792.36127216002,
|
||||
116792.36127216002
|
||||
],
|
||||
"Longitude (deg)": 0.2,
|
||||
"Variable operating cost ($)": [
|
||||
-11360.736,
|
||||
-11360.736
|
||||
],
|
||||
"Latitude (deg)": 0.75,
|
||||
"Emissions (tonne)": {}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"F1": {
|
||||
"L1": {
|
||||
"Opening cost ($)": [
|
||||
500.0,
|
||||
0.0
|
||||
],
|
||||
"Emissions (tonne)": {
|
||||
"CH4": [
|
||||
3.0,
|
||||
2.0
|
||||
],
|
||||
"CO2": [
|
||||
52.0,
|
||||
50.0
|
||||
]
|
||||
},
|
||||
"Expansion cost ($)": [
|
||||
750.0,
|
||||
0.0
|
||||
],
|
||||
"Longitude (deg)": 0.0,
|
||||
"Energy (GJ)": [
|
||||
120.0,
|
||||
110.0
|
||||
],
|
||||
"Total output": {
|
||||
"P2": [
|
||||
200.0,
|
||||
200.0
|
||||
],
|
||||
"P3": [
|
||||
500.0,
|
||||
500.0
|
||||
]
|
||||
},
|
||||
"Capacity (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"Latitude (deg)": 0.0,
|
||||
"Output": {
|
||||
"Send": {
|
||||
"P2": {
|
||||
"F2": {
|
||||
"L4": {
|
||||
"Distance (km)": 85.87,
|
||||
"Amount (tonne)": [
|
||||
199.0,
|
||||
199.0
|
||||
],
|
||||
"Longitude (deg)": 0.2,
|
||||
"Latitude (deg)": 0.75
|
||||
}
|
||||
}
|
||||
},
|
||||
"P3": {
|
||||
"F4": {
|
||||
"L6": {
|
||||
"Distance (km)": 6893.41,
|
||||
"Amount (tonne)": [
|
||||
499.0,
|
||||
499.0
|
||||
],
|
||||
"Longitude (deg)": 50.0,
|
||||
"Latitude (deg)": 50.0
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"Dispose": {
|
||||
"P2": {
|
||||
"Amount (tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"Cost ($)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
},
|
||||
"P3": {
|
||||
"Amount (tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"Cost ($)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Total input (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"Fixed operating cost ($)": [
|
||||
30.0,
|
||||
30.0
|
||||
],
|
||||
"Input": {
|
||||
"Origin": {
|
||||
"C3": {
|
||||
"Distance (km)": 8889.75,
|
||||
"Amount (tonne)": [
|
||||
212.97000000000003,
|
||||
212.97000000000003
|
||||
],
|
||||
"Transportation energy (J)": [
|
||||
227190.00690000004,
|
||||
208257.50632500005
|
||||
],
|
||||
"Transportation cost ($)": [
|
||||
28398.750862500005,
|
||||
28398.750862500005
|
||||
],
|
||||
"Longitude (deg)": 76.0,
|
||||
"Variable operating cost ($)": [
|
||||
6389.1,
|
||||
6389.1
|
||||
],
|
||||
"Latitude (deg)": 84.0,
|
||||
"Emissions (tonne)": {
|
||||
"CH4": [
|
||||
0.6389100000000001,
|
||||
0.42594000000000004
|
||||
],
|
||||
"CO2": [
|
||||
11.074440000000001,
|
||||
10.648500000000002
|
||||
]
|
||||
}
|
||||
},
|
||||
"C7": {
|
||||
"Distance (km)": 8526.39,
|
||||
"Amount (tonne)": [
|
||||
246.62,
|
||||
246.62
|
||||
],
|
||||
"Transportation energy (J)": [
|
||||
252333.396216,
|
||||
231305.613198
|
||||
],
|
||||
"Transportation cost ($)": [
|
||||
31541.674527,
|
||||
31541.674527
|
||||
],
|
||||
"Longitude (deg)": 83.0,
|
||||
"Variable operating cost ($)": [
|
||||
7398.6,
|
||||
7398.6
|
||||
],
|
||||
"Latitude (deg)": 30.0,
|
||||
"Emissions (tonne)": {
|
||||
"CH4": [
|
||||
0.7398600000000001,
|
||||
0.49324
|
||||
],
|
||||
"CO2": [
|
||||
12.82424,
|
||||
12.331000000000001
|
||||
]
|
||||
}
|
||||
},
|
||||
"C5": {
|
||||
"Distance (km)": 9148.52,
|
||||
"Amount (tonne)": [
|
||||
510.3299999999999,
|
||||
510.3299999999999
|
||||
],
|
||||
"Transportation energy (J)": [
|
||||
560251.7053919999,
|
||||
513564.0632759999
|
||||
],
|
||||
"Transportation cost ($)": [
|
||||
70031.46317399999,
|
||||
70031.46317399999
|
||||
],
|
||||
"Longitude (deg)": 92.0,
|
||||
"Variable operating cost ($)": [
|
||||
15309.899999999998,
|
||||
15309.899999999998
|
||||
],
|
||||
"Latitude (deg)": 32.0,
|
||||
"Emissions (tonne)": {
|
||||
"CH4": [
|
||||
1.5309899999999999,
|
||||
1.02066
|
||||
],
|
||||
"CO2": [
|
||||
26.537159999999997,
|
||||
25.516499999999997
|
||||
]
|
||||
}
|
||||
},
|
||||
"C9": {
|
||||
"Distance (km)": 8201.21,
|
||||
"Amount (tonne)": [
|
||||
30.08,
|
||||
30.08
|
||||
],
|
||||
"Transportation energy (J)": [
|
||||
29603.087615999993,
|
||||
27136.163647999994
|
||||
],
|
||||
"Transportation cost ($)": [
|
||||
3700.385951999999,
|
||||
3700.385951999999
|
||||
],
|
||||
"Longitude (deg)": 52.0,
|
||||
"Variable operating cost ($)": [
|
||||
902.4,
|
||||
902.4
|
||||
],
|
||||
"Latitude (deg)": 74.0,
|
||||
"Emissions (tonne)": {
|
||||
"CH4": [
|
||||
0.09024,
|
||||
0.06016
|
||||
],
|
||||
"CO2": [
|
||||
1.5641599999999998,
|
||||
1.504
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
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|
||||
"C10": {
|
||||
"Marginal cost ($/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
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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...
|
||||
23
mkdocs.yml
23
mkdocs.yml
@@ -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"
|
||||
23
src/RELOG.jl
23
src/RELOG.jl
@@ -1,11 +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
|
||||
include("dotdict.jl")
|
||||
include("instance.jl")
|
||||
include("graph.jl")
|
||||
include("model.jl")
|
||||
include("reports.jl")
|
||||
end
|
||||
|
||||
_round(x::Number) = round(x, digits = 5)
|
||||
|
||||
include("instance/structs.jl")
|
||||
include("instance/parse.jl")
|
||||
include("model/jumpext.jl")
|
||||
include("model/dist.jl")
|
||||
include("model/build.jl")
|
||||
include("reports/plants.jl")
|
||||
include("reports/transportation.jl")
|
||||
include("reports/centers.jl")
|
||||
|
||||
end # module RELOG
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
.navbar-default {
|
||||
border-bottom: 0px;
|
||||
background-color: #fff;
|
||||
box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
|
||||
}
|
||||
|
||||
a, .navbar-default a {
|
||||
color: #06a !important;
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.disabled > a {
|
||||
color: #999 !important;
|
||||
}
|
||||
|
||||
.navbar-default a:hover,
|
||||
.navbar-default .active,
|
||||
.active > a {
|
||||
background-color: #f0f0f0 !important;
|
||||
}
|
||||
|
||||
.icon-bar {
|
||||
background-color: #666 !important;
|
||||
}
|
||||
|
||||
.navbar-collapse {
|
||||
border-color: #fff !important;
|
||||
}
|
||||
@@ -1,195 +0,0 @@
|
||||
# Input and Output Data Formats
|
||||
|
||||
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.
|
||||
|
||||
## 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. |
|
||||
|
||||
|
||||
#### Example
|
||||
```json
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 2,
|
||||
"building period (years)": [1]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 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 ($/km/tonne)` | The cost to transport this product. Must be a time series.
|
||||
|`transportation energy (J/km/tonne)` | The energy required to transport this product. Must be a time series. Optional.
|
||||
|`transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional.
|
||||
|`initial amounts` | A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a time series.
|
||||
|
||||
Each product may have some amount available at the beginning of each time period. In this case, the key `initial amounts` maps to a dictionary with the following keys:
|
||||
|
||||
| Key | Description
|
||||
|:------------------------|---------------|
|
||||
| `latitude (deg)` | The latitude of the location.
|
||||
| `longitude (deg)` | The longitude of the location.
|
||||
| `amount (tonne)` | The amount of the product initially available at the location. Must be a time series.
|
||||
|
||||
#### Example
|
||||
|
||||
```json
|
||||
{
|
||||
"products": {
|
||||
"P1": {
|
||||
"initial amounts": {
|
||||
"C1": {
|
||||
"latitude (deg)": 7.0,
|
||||
"longitude (deg)": 7.0,
|
||||
"amount (tonne)": [934.56, 934.56]
|
||||
},
|
||||
"C2": {
|
||||
"latitude (deg)": 7.0,
|
||||
"longitude (deg)": 19.0,
|
||||
"amount (tonne)": [198.95, 198.95]
|
||||
},
|
||||
"C3": {
|
||||
"latitude (deg)": 84.0,
|
||||
"longitude (deg)": 76.0,
|
||||
"amount (tonne)": [212.97, 212.97]
|
||||
}
|
||||
},
|
||||
"transportation cost ($/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]
|
||||
}
|
||||
},
|
||||
"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]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 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]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### 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.
|
||||
|
||||
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|
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|
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@@ -1,51 +0,0 @@
|
||||
# RELOG: Reverse Logistics 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.
|
||||
|
||||
<img src="images/ex_transportation.png" width="1000px"/>
|
||||
|
||||
|
||||
### Table of Contents
|
||||
|
||||
* [Usage](usage.md)
|
||||
* [Input and Output Data Formats](format.md)
|
||||
* [Simplified Solution Reports](reports.md)
|
||||
* [Optimization Model](model.md)
|
||||
|
||||
### Source Code
|
||||
|
||||
* [https://github.com/ANL-CEEESA/RELOG](https://github.com/ANL-CEEESA/RELOG)
|
||||
|
||||
### Authors
|
||||
* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
|
||||
* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
|
||||
|
||||
### License
|
||||
|
||||
```text
|
||||
RELOG: Reverse Logistics Optimization
|
||||
Copyright © 2020, 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:
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
```
|
||||
@@ -1,8 +0,0 @@
|
||||
MathJax.Hub.Config({
|
||||
"tex2jax": { inlineMath: [ [ '$', '$' ] ] }
|
||||
});
|
||||
MathJax.Hub.Config({
|
||||
config: ["MMLorHTML.js"],
|
||||
jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"],
|
||||
extensions: ["MathMenu.js", "MathZoom.js"]
|
||||
});
|
||||
@@ -1,165 +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
|
||||
|
||||
* $L$ - 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:**
|
||||
|
||||
* $c^\text{disp}_{pmt}$ - 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.
|
||||
|
||||
**Products:**
|
||||
|
||||
* $\alpha_{pm}$ - 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:**
|
||||
|
||||
* $c^\text{tr}_{t}$ - Transportation cost during time $t$ (`$/tonne/km`)
|
||||
* $d_{lp}$ - Distance between plant $p$ and location $l$ (`km`)
|
||||
|
||||
|
||||
### Decision variables
|
||||
* $q_{mpt}$ - 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 $t$ (`bool`)
|
||||
* $w_{pt}$ - 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 $t$ (`bool`)
|
||||
* $y_{lpt}$ - 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:
|
||||
|
||||
\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:
|
||||
|
||||
\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.
|
||||
|
||||
\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:
|
||||
|
||||
\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:
|
||||
|
||||
\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:
|
||||
|
||||
\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:
|
||||
|
||||
\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.
|
||||
|
||||
\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.
|
||||
|
||||
\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:
|
||||
|
||||
\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}
|
||||
@@ -1,273 +0,0 @@
|
||||
# Simplified Solution Reports
|
||||
|
||||
In addition to the full output format described in [data formats](format.md), RELOG can also generate a number of simplified reports in tabular data format (CSV), which can be more easily processed by spreadsheet software (such as Microsoft Excel), by data analysis libraries (such as Pandas) or by relational databases (such as SQLite).
|
||||
|
||||
In this page, we also illustrate what types of charts and visualizations can be produced from these tabular data files. The sample charts have been produced using Python, matplotlib, seaborn and geopandas.
|
||||
|
||||
## Plants report
|
||||
|
||||
Report showing plant costs, capacities, energy expenditure and utilization factors.
|
||||
|
||||
Generated by `RELOG.write_plants_report(solution, filename)`. For a concrete example, see [nimh_plants.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plants.csv).
|
||||
|
||||
| Column | Description
|
||||
|:--------------------------------------|---------------|
|
||||
| `plant type` | Plant type.
|
||||
| `location name` | Location name.
|
||||
| `year` | What year this row corresponds to. This reports includes one row for each year in the simulation.
|
||||
| `latitude (deg)` | Latitude of the plant.
|
||||
| `longitude (deg)` | Longitude of the plant.
|
||||
| `capacity (tonne)` | Capacity of the plant at this point in time.
|
||||
| `amount received (tonne)` | Amount of input material received by the plant this year.
|
||||
| `amount processed (tonne)` | Amount of input material processed by the plant this year.
|
||||
| `amount in storage (tonne)` | Amount of input material in storage at the end of the year.
|
||||
| `utilization factor (%)` | Amount processed by the plant this year divided by current plant capacity.
|
||||
| `energy (GJ)` | Amount of energy expended by the plant this year.
|
||||
| `opening cost ($)` | Amount spent opening the plant. This value is only positive if the plant became operational this year.
|
||||
| `expansion cost ($)` | Amount spent this year expanding the plant capacity.
|
||||
| `fixed operating cost ($)` | Amount spent for keeping the plant operational this year.
|
||||
| `variable operating cost ($)` | Amount spent this year to process the input material.
|
||||
| `storage cost ($)` | Amount spent this year on storage.
|
||||
| `total cost ($)` | Sum of all previous plant costs.
|
||||
|
||||
|
||||
### Sample charts
|
||||
|
||||
* Bar plot with total plant costs per year, grouped by plant type (in Python):
|
||||
```python
|
||||
import pandas as pd
|
||||
import seaborn as sns; sns.set()
|
||||
|
||||
data = pd.read_csv("plants_report.csv")
|
||||
sns.barplot(x="year",
|
||||
y="total cost ($)",
|
||||
hue="plant type",
|
||||
data=data.groupby(["plant type", "year"])
|
||||
.sum()
|
||||
.reset_index());
|
||||
```
|
||||
|
||||
<img src="../images/ex_plant_cost_per_year.png" width="500px"/>
|
||||
|
||||
* Map showing plant locations (in Python):
|
||||
```python
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
|
||||
# Plot base map
|
||||
world = gp.read_file(gp.datasets.get_path('naturalearth_lowres'))
|
||||
ax = world.plot(color='white', edgecolor='50', figsize=(13,6))
|
||||
ax.set_ylim([23, 50])
|
||||
ax.set_xlim([-128, -65])
|
||||
|
||||
# Plot plant locations
|
||||
data = pd.read_csv("nimh_plants.csv")
|
||||
points = gp.points_from_xy(data["longitude (deg)"],
|
||||
data["latitude (deg)"])
|
||||
gp.GeoDataFrame(data, geometry=points).plot(ax=ax);
|
||||
```
|
||||
|
||||
<img src="../images/ex_plant_locations.png" width="1000px"/>
|
||||
|
||||
|
||||
## Plant outputs report
|
||||
|
||||
Report showing amount of products produced, sent and disposed of by each plant, as well as disposal costs.
|
||||
|
||||
Generated by `RELOG.write_plant_outputs_report(solution, filename)`. For a concrete example, see [nimh_plant_outputs.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plant_outputs.csv).
|
||||
|
||||
|
||||
| Column | Description
|
||||
|:--------------------------------------|---------------|
|
||||
| `plant type` | Plant type.
|
||||
| `location name` | Location name.
|
||||
| `year` | What year this row corresponds to. This reports includes one row for each year in the simulation.
|
||||
| `product name` | Product being produced.
|
||||
| `amount produced (tonne)` | Amount of product produced this year.
|
||||
| `amount sent (tonne)` | Amount of product produced by this plant and sent to another plant for further processing this year.
|
||||
| `amount disposed (tonne)` | Amount produced produced by this plant and immediately disposed of locally this year.
|
||||
| `disposal cost ($)` | Disposal cost for this year.
|
||||
|
||||
### Sample charts
|
||||
|
||||
* Bar plot showing total amount produced for each product, grouped by year (in Python):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import seaborn as sns; sns.set()
|
||||
|
||||
data = pd.read_csv("plant_outputs_report.csv")
|
||||
sns.barplot(x="amount produced (tonne)",
|
||||
y="product name",
|
||||
hue="year",
|
||||
data=data.groupby(["product name", "year"])
|
||||
.sum()
|
||||
.reset_index());
|
||||
```
|
||||
|
||||
<img src="../images/ex_amount_produced.png" width="500px"/>
|
||||
|
||||
|
||||
## Plant emissions report
|
||||
|
||||
Report showing amount of emissions produced by each plant.
|
||||
|
||||
Generated by `RELOG.write_plant_emissions_report(solution, filename)`. For a concrete example, see [nimh_plant_emissions.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plant_emissions.csv).
|
||||
|
||||
| Column | Description
|
||||
|:--------------------------------------|---------------|
|
||||
| `plant type` | Plant type.
|
||||
| `location name` | Location name.
|
||||
| `year` | Year.
|
||||
| `emission type` | Type of emission.
|
||||
| `amount (tonne)` | Amount of emission produced by the plant this year.
|
||||
|
||||
### Sample charts
|
||||
|
||||
* Bar plot showing total emission by plant type, grouped type of emissions (in Python):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import seaborn as sns; sns.set()
|
||||
|
||||
data = pd.read_csv("plant_emissions_report.csv")
|
||||
sns.barplot(x="plant type",
|
||||
y="emission amount (tonne)",
|
||||
hue="emission type",
|
||||
data=data.groupby(["plant type", "emission type"])
|
||||
.sum()
|
||||
.reset_index());
|
||||
```
|
||||
|
||||
<img src="../images/ex_emissions.png" width="500px"/>
|
||||
|
||||
## Transportation report
|
||||
|
||||
Report showing amount of product sent from initial locations to plants, and from one plant to another. Includes the distance between each pair of locations, amount-distance shipped, transportation costs and energy expenditure.
|
||||
|
||||
Generated by `RELOG.write_transportation_report(solution, filename)`. For a concrete example, see [nimh_transportation.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_transportation.csv).
|
||||
|
||||
|
||||
| Column | Description
|
||||
|:--------------------------------------|---------------|
|
||||
| `source type` | If product is being shipped from an initial location, equals `Origin`. If product is being shipped from a plant, equals plant type.
|
||||
| `source location name` | Name of the location where the product is being shipped from.
|
||||
| `source latitude (deg)` | Latitude of the source location.
|
||||
| `source longitude (deg)` | Longitude of the source location.
|
||||
| `destination type`| Type of plant the product is being shipped to.
|
||||
| `destination location name`| Name of the location where the product is being shipped to.
|
||||
| `destination latitude (deg)` | Latitude of the destination location.
|
||||
| `destination longitude (deg)` | Longitude of the destination location.
|
||||
| `product`| Product being shipped.
|
||||
| `year`| Year.
|
||||
| `distance (km)`| Distance between source and destination.
|
||||
| `amount (tonne)`| Total amount of product being shipped between the two locations this year.
|
||||
| `amount-distance (tonne-km)`| Total amount being shipped this year times distance.
|
||||
| `transportation cost ($)`| Cost to transport this amount of product between the two locations for this year.
|
||||
| `transportation energy (GJ)`| Energy expended transporting this amount of product between the two locations.
|
||||
|
||||
### Sample charts
|
||||
|
||||
* Bar plot showing total amount-distance for each product type, grouped by year (in Python):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import seaborn as sns; sns.set()
|
||||
|
||||
data = pd.read_csv("transportation_report.csv")
|
||||
sns.barplot(x="product",
|
||||
y="amount-distance (tonne-km)",
|
||||
hue="year",
|
||||
data=data.groupby(["product", "year"])
|
||||
.sum()
|
||||
.reset_index());
|
||||
```
|
||||
|
||||
<img src="../images/ex_transportation_amount_distance.png" width="500px"/>
|
||||
|
||||
* Map of transportation lines (in Python):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import geopandas as gp
|
||||
from shapely.geometry import Point, LineString
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import collections
|
||||
|
||||
# Plot base map
|
||||
world = gp.read_file(gp.datasets.get_path('naturalearth_lowres'))
|
||||
ax = world.plot(color='white', edgecolor='50', figsize=(14,7))
|
||||
ax.set_ylim([23, 50])
|
||||
ax.set_xlim([-128, -65])
|
||||
|
||||
# Draw transportation lines
|
||||
data = pd.read_csv("transportation_report.csv")
|
||||
lines = [[(row["source longitude (deg)"], row["source latitude (deg)"]),
|
||||
(row["destination longitude (deg)"], row["destination latitude (deg)"])
|
||||
] for (index, row) in data.iterrows()]
|
||||
ax.add_collection(collections.LineCollection(lines,
|
||||
linewidths=0.25,
|
||||
zorder=1,
|
||||
alpha=0.5,
|
||||
color="50"))
|
||||
|
||||
# Draw source points
|
||||
points = gp.points_from_xy(data["source longitude (deg)"],
|
||||
data["source latitude (deg)"])
|
||||
gp.GeoDataFrame(data, geometry=points).plot(ax=ax,
|
||||
color="0.5",
|
||||
markersize=1);
|
||||
|
||||
# Draw destination points
|
||||
points = gp.points_from_xy(data["destination longitude (deg)"],
|
||||
data["destination latitude (deg)"])
|
||||
gp.GeoDataFrame(data, geometry=points).plot(ax=ax,
|
||||
color="red",
|
||||
markersize=50);
|
||||
```
|
||||
|
||||
<img src="../images/ex_transportation.png" width="1000px"/>
|
||||
|
||||
|
||||
## Transportation emissions report
|
||||
|
||||
Report showing emissions for each trip between initial locations and plants, and between pairs of plants.
|
||||
|
||||
Generated by `RELOG.write_transportation_emissions_report(solution, filename)`. For a concrete example, see [nimh_transportation_emissions.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_transportation_emissions.csv).
|
||||
|
||||
| Column | Description
|
||||
|:--------------------------------------|---------------|
|
||||
| `source type` | If product is being shipped from an initial location, equals `Origin`. If product is being shipped from a plant, equals plant type.
|
||||
| `source location name` | Name of the location where the product is being shipped from.
|
||||
| `source latitude (deg)` | Latitude of the source location.
|
||||
| `source longitude (deg)` | Longitude of the source location.
|
||||
| `destination type`| Type of plant the product is being shipped to.
|
||||
| `destination location name`| Name of the location where the product is being shipped to.
|
||||
| `destination latitude (deg)` | Latitude of the destination location.
|
||||
| `destination longitude (deg)` | Longitude of the destination location.
|
||||
| `product`| Product being shipped.
|
||||
| `year`| Year.
|
||||
| `distance (km)`| Distance between source and destination.
|
||||
| `shipped amount (tonne)`| Total amount of product being shipped between the two locations this year.
|
||||
| `shipped amount-distance (tonne-km)`| Total amount being shipped this year times distance.
|
||||
| `emission type` | Type of emission.
|
||||
| `emission amount (tonne)` | Amount of emission produced by transportation segment this year.
|
||||
|
||||
### Sample charts
|
||||
|
||||
* Bar plot showing total emission amount by emission type, grouped by type of product being transported (in Python):
|
||||
|
||||
```python
|
||||
import pandas as pd
|
||||
import seaborn as sns; sns.set()
|
||||
|
||||
data = pd.read_csv("transportation_emissions_report.csv")
|
||||
sns.barplot(x="emission type",
|
||||
y="emission amount (tonne)",
|
||||
hue="product",
|
||||
data=data.groupby(["product", "emission type"])
|
||||
.sum()
|
||||
.reset_index());
|
||||
```
|
||||
|
||||
<img src="../images/ex_transportation_emissions.png" width="500px"/>
|
||||
@@ -1,138 +0,0 @@
|
||||
Usage
|
||||
=====
|
||||
|
||||
## 1. Installation
|
||||
|
||||
To use RELOG, the first step is to install the [Julia programming language](https://julialang.org/) on your machine. Note that RELOG was developed and tested with Julia 1.5 and may not be compatible with newer versions. After Julia is installed, launch the Julia console, type `]` to switch to package manger mode, then run:
|
||||
|
||||
```text
|
||||
(@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git
|
||||
```
|
||||
|
||||
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:
|
||||
|
||||
```text
|
||||
(@v1.5) pkg> test RELOG
|
||||
```
|
||||
|
||||
To update the package to a newer version, type `]` to enter the package manager mode, then run:
|
||||
|
||||
```text
|
||||
(@v1.5) pkg> update RELOG
|
||||
```
|
||||
|
||||
## 2. Modeling the problem
|
||||
|
||||
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.
|
||||
|
||||
* 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.
|
||||
|
||||
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.
|
||||
|
||||
* 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.
|
||||
|
||||
* 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).
|
||||
|
||||
## 3. Running the optimization
|
||||
|
||||
After creating a JSON file describing the reverse manufacturing process and the input data, the following example illustrates how to use the package to find the optimal set of decisions:
|
||||
|
||||
```julia
|
||||
# Import package
|
||||
using RELOG
|
||||
|
||||
# Solve optimization problem
|
||||
solution = RELOG.solve("/home/user/instance.json")
|
||||
|
||||
# Write full solution in JSON format
|
||||
RELOG.write(solution, "solution.json")
|
||||
|
||||
# Write simplified reports in CSV format
|
||||
RELOG.write_plants_report(solution, "plants.csv")
|
||||
RELOG.write_transportation_report(solution, "transportation.csv")
|
||||
```
|
||||
|
||||
For a complete description of the file formats above, and for a complete list of available reports, see the [data format page](format.md).
|
||||
|
||||
## 4. What-If Analysis
|
||||
|
||||
Fundamentally, RELOG decides when and where to build plants based on a deterministic optimization problem that minimizes costs for a particular input file provided by the user. In practical situations, it may not be possible to perfectly estimate some (or most) entries in this input file in advance, such as costs, demands and emissions. In this situation, it may be interesting to evaluate how well does the facility location plan produced by RELOG work if costs, demands and emissions turn out to be different.
|
||||
|
||||
To simplify this what-if analysis, RELOG provides the `resolve` method, which updates a previous solution based on a new scenario. The method accepts a previous optimal solution, produced by RELOG, and a new input file, which describes the new scenario. The method reoptimizes the supply chain for this new input file, and produces a new solution which still builds the same set of plants as before, in exactly the same locations and with the same capacities, but that may now utilize the plants differently, based on the new data. For example, in the new solution, plants that were previously used at full capacity may now be utilized at half-capacity instead. As another example, regions that were previously served by a certain plant may now be served by a different one.
|
||||
|
||||
The following snippet shows how to use the method:
|
||||
|
||||
```julia
|
||||
# Import package
|
||||
using RELOG
|
||||
|
||||
# Optimize for the average scenario
|
||||
solution_avg = RELOG.solve("input_avg.json")
|
||||
|
||||
# Write reports for the average scenario
|
||||
RELOG.write_plants_report(solution_avg, "plants_avg.csv")
|
||||
RELOG.write_transportation_report(solution_avg, "transportation_avg.csv")
|
||||
|
||||
# Re-optimize for the high-demand scenario, keeping plants fixed
|
||||
solution_high = RELOG.resolve(solution_avg, "input_high.json")
|
||||
|
||||
# Write reports for the high-demand scenario
|
||||
RELOG.write_plants_report(solution_high, "plants_high.csv")
|
||||
RELOG.write_transportation_report(solution_high, "transportation_high.csv")
|
||||
```
|
||||
|
||||
To use the `resolve` method, the new input file should be very similar to the original one. Only the following entries are allowed to change:
|
||||
|
||||
- **Products:** Transportation costs, energy, emissions and initial amounts (latitude, longitude and amount).
|
||||
- **Plants:** Energy and emissions.
|
||||
- **Plant's location:** Latitude and longitude.
|
||||
- **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:
|
||||
|
||||
```julia
|
||||
using RELOG, Gurobi, JuMP
|
||||
|
||||
gurobi = optimizer_with_attributes(Gurobi.Optimizer,
|
||||
"TimeLimit" => 3600,
|
||||
"MIPGap" => 0.001)
|
||||
|
||||
RELOG.solve("instance.json",
|
||||
output="solution.json",
|
||||
optimizer=gurobi)
|
||||
```
|
||||
|
||||
### 5.2 Multi-period heuristics
|
||||
|
||||
For large-scale instances, it may be too time-consuming to find an exact optimal solution to the multi-period version of the problem. For these situations, RELOG includes a heuristic solution method, which proceeds as follows:
|
||||
|
||||
1. First, RELOG creates a single-period version of the problem, in which most values are replaced by their averages. This single-period problem is typically much easier to solve.
|
||||
|
||||
2. After solving the simplified problem, RELOG resolves the multi-period version of the problem, but considering only candidate plant locations that were selected by the optimal solution to the single-period version of the problem. All remaining candidate plant locations are removed.
|
||||
|
||||
To solve an instance using this heuristic, use the option `heuristic=true`, as shown below.
|
||||
|
||||
```julia
|
||||
using RELOG
|
||||
|
||||
solution = RELOG.solve("/home/user/instance.json",
|
||||
heuristic=true)
|
||||
```
|
||||
@@ -1,68 +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.
|
||||
|
||||
struct DotDict
|
||||
inner::Dict
|
||||
end
|
||||
|
||||
DotDict() = DotDict(Dict())
|
||||
|
||||
function Base.setproperty!(d::DotDict, key::Symbol, value)
|
||||
setindex!(getfield(d, :inner), value, key)
|
||||
end
|
||||
|
||||
function Base.getproperty(d::DotDict, key::Symbol)
|
||||
(key == :inner ? getfield(d, :inner) : d.inner[key])
|
||||
end
|
||||
|
||||
function Base.getindex(d::DotDict, key::Int64)
|
||||
d.inner[Symbol(key)]
|
||||
end
|
||||
|
||||
function Base.getindex(d::DotDict, key::Symbol)
|
||||
d.inner[key]
|
||||
end
|
||||
|
||||
function Base.keys(d::DotDict)
|
||||
keys(d.inner)
|
||||
end
|
||||
|
||||
function Base.values(d::DotDict)
|
||||
values(d.inner)
|
||||
end
|
||||
|
||||
function Base.iterate(d::DotDict)
|
||||
iterate(values(d.inner))
|
||||
end
|
||||
|
||||
function Base.iterate(d::DotDict, v::Int64)
|
||||
iterate(values(d.inner), v)
|
||||
end
|
||||
|
||||
function Base.length(d::DotDict)
|
||||
length(values(d.inner))
|
||||
end
|
||||
|
||||
function Base.show(io::IO, d::DotDict)
|
||||
print(io, "DotDict with $(length(keys(d.inner))) entries:\n")
|
||||
count = 0
|
||||
for k in keys(d.inner)
|
||||
count += 1
|
||||
if count > 10
|
||||
print(io, " ...\n")
|
||||
break
|
||||
end
|
||||
print(io, " :$(k) => $(d.inner[k])\n")
|
||||
end
|
||||
end
|
||||
|
||||
function recursive_to_dot_dict(el)
|
||||
if typeof(el) == Dict{String, Any}
|
||||
return DotDict(Dict(Symbol(k) => recursive_to_dot_dict(el[k]) for k in keys(el)))
|
||||
else
|
||||
return el
|
||||
end
|
||||
end
|
||||
|
||||
export recursive_to_dot_dict
|
||||
126
src/graph.jl
126
src/graph.jl
@@ -1,126 +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::Array{Arc}
|
||||
outgoing_arcs::Array{Arc}
|
||||
end
|
||||
|
||||
|
||||
mutable struct ShippingNode <: Node
|
||||
index::Int
|
||||
location::Union{Plant, CollectionCenter}
|
||||
product::Product
|
||||
incoming_arcs::Array{Arc}
|
||||
outgoing_arcs::Array{Arc}
|
||||
end
|
||||
|
||||
|
||||
mutable struct Graph
|
||||
process_nodes::Array{ProcessNode}
|
||||
plant_shipping_nodes::Array{ShippingNode}
|
||||
collection_shipping_nodes::Array{ShippingNode}
|
||||
arcs::Array{Arc}
|
||||
end
|
||||
|
||||
|
||||
function build_graph(instance::Instance)::Graph
|
||||
arcs = []
|
||||
next_index = 0
|
||||
process_nodes = ProcessNode[]
|
||||
plant_shipping_nodes = ShippingNode[]
|
||||
collection_shipping_nodes = ShippingNode[]
|
||||
|
||||
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)
|
||||
|
||||
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)
|
||||
end
|
||||
|
||||
|
||||
function to_csv(graph::Graph)
|
||||
result = ""
|
||||
for a in graph.arcs
|
||||
result *= "$(a.source.index),$(a.dest.index)\n"
|
||||
end
|
||||
return result
|
||||
end
|
||||
|
||||
|
||||
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(distance(x, y) / 1000.0, digits=2)
|
||||
end
|
||||
281
src/instance.jl
281
src/instance.jl
@@ -1,281 +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
|
||||
|
||||
|
||||
mutable struct Product
|
||||
name::String
|
||||
transportation_cost::Array{Float64}
|
||||
transportation_energy::Array{Float64}
|
||||
transportation_emissions::Dict{String, Array{Float64}}
|
||||
end
|
||||
|
||||
|
||||
mutable struct CollectionCenter
|
||||
index::Int64
|
||||
name::String
|
||||
latitude::Float64
|
||||
longitude::Float64
|
||||
product::Product
|
||||
amount::Array{Float64}
|
||||
end
|
||||
|
||||
|
||||
mutable struct PlantSize
|
||||
capacity::Float64
|
||||
variable_operating_cost::Array{Float64}
|
||||
fixed_operating_cost::Array{Float64}
|
||||
opening_cost::Array{Float64}
|
||||
end
|
||||
|
||||
|
||||
mutable struct Plant
|
||||
index::Int64
|
||||
plant_name::String
|
||||
location_name::String
|
||||
input::Product
|
||||
output::Dict{Product, Float64}
|
||||
latitude::Float64
|
||||
longitude::Float64
|
||||
disposal_limit::Dict{Product, Array{Float64}}
|
||||
disposal_cost::Dict{Product, Array{Float64}}
|
||||
sizes::Array{PlantSize}
|
||||
energy::Array{Float64}
|
||||
emissions::Dict{String, Array{Float64}}
|
||||
storage_limit::Float64
|
||||
storage_cost::Array{Float64}
|
||||
end
|
||||
|
||||
|
||||
mutable struct Instance
|
||||
time::Int64
|
||||
products::Array{Product, 1}
|
||||
collection_centers::Array{CollectionCenter, 1}
|
||||
plants::Array{Plant, 1}
|
||||
building_period::Array{Int64}
|
||||
end
|
||||
|
||||
|
||||
function validate(json, schema)
|
||||
result = JSONSchema.validate(json, schema)
|
||||
if result !== nothing
|
||||
if result isa JSONSchema.SingleIssue
|
||||
path = join(result.path, " → ")
|
||||
if length(path) == 0
|
||||
path = "root"
|
||||
end
|
||||
msg = "$(result.msg) in $(path)"
|
||||
else
|
||||
msg = convert(String, result)
|
||||
end
|
||||
throw(msg)
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
function parsefile(path::String)::Instance
|
||||
return RELOG.parse(JSON.parsefile(path))
|
||||
end
|
||||
|
||||
|
||||
function parse(json)::Instance
|
||||
basedir = dirname(@__FILE__)
|
||||
json_schema = JSON.parsefile("$basedir/schemas/input.json")
|
||||
validate(json, Schema(json_schema))
|
||||
|
||||
T = json["parameters"]["time horizon (years)"]
|
||||
json_schema["definitions"]["TimeSeries"]["minItems"] = T
|
||||
json_schema["definitions"]["TimeSeries"]["maxItems"] = T
|
||||
validate(json, Schema(json_schema))
|
||||
|
||||
building_period = [1]
|
||||
if "building period (years)" in keys(json)
|
||||
building_period = json["building period (years)"]
|
||||
end
|
||||
|
||||
plants = Plant[]
|
||||
products = Product[]
|
||||
collection_centers = CollectionCenter[]
|
||||
prod_name_to_product = Dict{String, Product}()
|
||||
|
||||
# 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"]
|
||||
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)
|
||||
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 in 1:T] for p in keys(output))
|
||||
disposal_cost = Dict(p => [0.0 for t in 1:T] for p in keys(output))
|
||||
|
||||
# Disposal
|
||||
if "disposal" in keys(location_dict)
|
||||
for (product_name, disposal_dict) in location_dict["disposal"]
|
||||
limit = [1e8 for t in 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
|
||||
end
|
||||
|
||||
@info @sprintf("%12d collection centers", length(collection_centers))
|
||||
@info @sprintf("%12d candidate plant locations", length(plants))
|
||||
|
||||
return Instance(T, products, collection_centers, plants, building_period)
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
_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
|
||||
145
src/instance/parse.jl
Normal file
145
src/instance/parse.jl
Normal file
@@ -0,0 +1,145 @@
|
||||
using JSON
|
||||
using OrderedCollections
|
||||
|
||||
function parsefile(path::String)::Instance
|
||||
return RELOG.parse(JSON.parsefile(path, dicttype = () -> OrderedDict()))
|
||||
end
|
||||
|
||||
function parse(json)::Instance
|
||||
# Read parameters
|
||||
time_horizon = json["parameters"]["time horizon (years)"]
|
||||
building_period = json["parameters"]["building period (years)"]
|
||||
distance_metric = json["parameters"]["distance metric"]
|
||||
|
||||
timeseries(x::Union{Nothing,Number}) = repeat([x], time_horizon)
|
||||
timeseries(x::Array) = x
|
||||
timeseries(d::OrderedDict) = OrderedDict(k => timeseries(v) 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)"])
|
||||
components = pdict["components"]
|
||||
prod = Product(; name, tr_cost, tr_energy, tr_emissions, components)
|
||||
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 => [v === nothing ? null_val : v for v in timeseries(cdict[key][p.name])]
|
||||
for p in outputs
|
||||
)
|
||||
to_array(x) = vcat(x'...)
|
||||
prepend_time_dimension(x) = to_array(repeat([x], time_horizon))
|
||||
|
||||
fixed_output = Dict()
|
||||
for p in outputs
|
||||
m = to_array(cdict["fixed output (tonne)"][p.name])
|
||||
if ndims(m) == 1
|
||||
m = prepend_time_dimension(m)
|
||||
end
|
||||
@assert size(m) == (time_horizon, length(p.components))
|
||||
fixed_output[p] = m
|
||||
end
|
||||
|
||||
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[]
|
||||
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])
|
||||
)
|
||||
|
||||
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
|
||||
|
||||
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
|
||||
|
||||
return Instance(;
|
||||
time_horizon,
|
||||
building_period,
|
||||
distance_metric,
|
||||
products,
|
||||
products_by_name,
|
||||
centers,
|
||||
centers_by_name,
|
||||
plants,
|
||||
plants_by_name,
|
||||
)
|
||||
end
|
||||
58
src/instance/structs.jl
Normal file
58
src/instance/structs.jl
Normal file
@@ -0,0 +1,58 @@
|
||||
using OrderedCollections
|
||||
|
||||
Base.@kwdef struct Product
|
||||
name::String
|
||||
tr_cost::Vector{Float64}
|
||||
tr_energy::Vector{Float64}
|
||||
tr_emissions::OrderedDict{String,Vector{Float64}}
|
||||
components::Vector{String}
|
||||
end
|
||||
|
||||
Base.@kwdef struct Center
|
||||
name::String
|
||||
latitude::Float64
|
||||
longitude::Float64
|
||||
input::Union{Product,Nothing}
|
||||
outputs::Vector{Product}
|
||||
fixed_output::OrderedDict{Product,Array{Float64,2}}
|
||||
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
|
||||
|
||||
Base.@kwdef struct PlantCapacity
|
||||
size::Float64
|
||||
opening_cost::Vector{Float64}
|
||||
fix_operating_cost::Vector{Float64}
|
||||
var_operating_cost::Vector{Float64}
|
||||
end
|
||||
|
||||
Base.@kwdef struct Plant
|
||||
name::String
|
||||
latitude::Float64
|
||||
longitude::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
|
||||
|
||||
Base.@kwdef struct Instance
|
||||
building_period::Vector{Int}
|
||||
centers_by_name::OrderedDict{String,Center}
|
||||
centers::Vector{Center}
|
||||
distance_metric::String
|
||||
products_by_name::OrderedDict{String,Product}
|
||||
products::Vector{Product}
|
||||
time_horizon::Int
|
||||
plants::Vector{Plant}
|
||||
plants_by_name::OrderedDict{String,Plant}
|
||||
end
|
||||
535
src/model.jl
535
src/model.jl
@@ -1,535 +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
|
||||
|
||||
|
||||
mutable struct ManufacturingModel
|
||||
mip::JuMP.Model
|
||||
vars::DotDict
|
||||
eqs::DotDict
|
||||
instance::Instance
|
||||
graph::Graph
|
||||
end
|
||||
|
||||
|
||||
function build_model(instance::Instance, graph::Graph, optimizer)::ManufacturingModel
|
||||
model = ManufacturingModel(Model(optimizer), DotDict(), DotDict(), instance, graph)
|
||||
create_vars!(model)
|
||||
create_objective_function!(model)
|
||||
create_shipping_node_constraints!(model)
|
||||
create_process_node_constraints!(model)
|
||||
return model
|
||||
end
|
||||
|
||||
|
||||
function create_vars!(model::ManufacturingModel)
|
||||
mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
|
||||
|
||||
vars.flow = Dict((a, t) => @variable(mip, lower_bound=0)
|
||||
for a in graph.arcs, t in 1:T)
|
||||
|
||||
vars.dispose = Dict((n, t) => @variable(mip,
|
||||
lower_bound=0,
|
||||
upper_bound=n.location.disposal_limit[n.product][t])
|
||||
for n in values(graph.plant_shipping_nodes), t in 1:T)
|
||||
|
||||
vars.store = Dict((n, t) => @variable(mip,
|
||||
lower_bound=0,
|
||||
upper_bound=n.location.storage_limit)
|
||||
for n in values(graph.process_nodes), t in 1:T)
|
||||
|
||||
vars.process = Dict((n, t) => @variable(mip,
|
||||
lower_bound = 0)
|
||||
for n in values(graph.process_nodes), t in 1:T)
|
||||
|
||||
vars.open_plant = Dict((n, t) => @variable(mip, binary=true)
|
||||
for n in values(graph.process_nodes), t in 1:T)
|
||||
|
||||
vars.is_open = Dict((n, t) => @variable(mip, binary=true)
|
||||
for n in values(graph.process_nodes), t in 1:T)
|
||||
|
||||
vars.capacity = Dict((n, t) => @variable(mip,
|
||||
lower_bound = 0,
|
||||
upper_bound = n.location.sizes[2].capacity)
|
||||
for n in values(graph.process_nodes), t in 1:T)
|
||||
|
||||
vars.expansion = Dict((n, t) => @variable(mip,
|
||||
lower_bound = 0,
|
||||
upper_bound = n.location.sizes[2].capacity -
|
||||
n.location.sizes[1].capacity)
|
||||
for n in values(graph.process_nodes), t in 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)
|
||||
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
|
||||
|
||||
function create_objective_function!(model::ManufacturingModel)
|
||||
mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
|
||||
obj = AffExpr(0.0)
|
||||
|
||||
# Process node costs
|
||||
for n in values(graph.process_nodes), t in 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, vars.flow[a, t])
|
||||
end
|
||||
|
||||
# Opening costs
|
||||
add_to_expression!(obj,
|
||||
n.location.sizes[1].opening_cost[t],
|
||||
vars.open_plant[n, t])
|
||||
|
||||
# Fixed operating costs (base)
|
||||
add_to_expression!(obj,
|
||||
n.location.sizes[1].fixed_operating_cost[t],
|
||||
vars.is_open[n, t])
|
||||
|
||||
# Fixed operating costs (expansion)
|
||||
add_to_expression!(obj,
|
||||
slope_fix_oper_cost(n.location, t),
|
||||
vars.expansion[n, t])
|
||||
|
||||
# Processing costs
|
||||
add_to_expression!(obj,
|
||||
n.location.sizes[1].variable_operating_cost[t],
|
||||
vars.process[n, t])
|
||||
|
||||
# Storage costs
|
||||
add_to_expression!(obj,
|
||||
n.location.storage_cost[t],
|
||||
vars.store[n, t])
|
||||
|
||||
# Expansion costs
|
||||
if t < T
|
||||
add_to_expression!(obj,
|
||||
slope_open(n.location, t) - slope_open(n.location, t + 1),
|
||||
vars.expansion[n, t])
|
||||
else
|
||||
add_to_expression!(obj,
|
||||
slope_open(n.location, t),
|
||||
vars.expansion[n, t])
|
||||
end
|
||||
end
|
||||
|
||||
# Shipping node costs
|
||||
for n in values(graph.plant_shipping_nodes), t in 1:T
|
||||
|
||||
# Disposal costs
|
||||
add_to_expression!(obj,
|
||||
n.location.disposal_cost[n.product][t],
|
||||
vars.dispose[n, t])
|
||||
end
|
||||
|
||||
@objective(mip, Min, obj)
|
||||
end
|
||||
|
||||
|
||||
function create_shipping_node_constraints!(model::ManufacturingModel)
|
||||
mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
|
||||
eqs = model.eqs
|
||||
|
||||
eqs.balance = OrderedDict()
|
||||
|
||||
for t in 1:T
|
||||
# Collection centers
|
||||
for n in graph.collection_shipping_nodes
|
||||
eqs.balance[n, t] = @constraint(mip,
|
||||
sum(vars.flow[a, t] for a in n.outgoing_arcs)
|
||||
== n.location.amount[t])
|
||||
end
|
||||
|
||||
# Plants
|
||||
for n in graph.plant_shipping_nodes
|
||||
@constraint(mip,
|
||||
sum(vars.flow[a, t] for a in n.incoming_arcs) ==
|
||||
sum(vars.flow[a, t] for a in n.outgoing_arcs) + vars.dispose[n, t])
|
||||
end
|
||||
end
|
||||
|
||||
end
|
||||
|
||||
|
||||
function create_process_node_constraints!(model::ManufacturingModel)
|
||||
mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
|
||||
|
||||
for t in 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, vars.flow[a, t])
|
||||
end
|
||||
|
||||
# Output amount is implied by amount processed
|
||||
for a in n.outgoing_arcs
|
||||
@constraint(mip, vars.flow[a, t] == a.values["weight"] * vars.process[n, t])
|
||||
end
|
||||
|
||||
# If plant is closed, capacity is zero
|
||||
@constraint(mip, vars.capacity[n, t] <= n.location.sizes[2].capacity * vars.is_open[n, t])
|
||||
|
||||
# If plant is open, capacity is greater than base
|
||||
@constraint(mip, vars.capacity[n, t] >= n.location.sizes[1].capacity * vars.is_open[n, t])
|
||||
|
||||
# Capacity is linked to expansion
|
||||
@constraint(mip, vars.capacity[n, t] <= n.location.sizes[1].capacity + vars.expansion[n, t])
|
||||
|
||||
# Can only process up to capacity
|
||||
@constraint(mip, vars.process[n, t] <= vars.capacity[n, t])
|
||||
|
||||
if t > 1
|
||||
# Plant capacity can only increase over time
|
||||
@constraint(mip, vars.capacity[n, t] >= vars.capacity[n, t-1])
|
||||
@constraint(mip, vars.expansion[n, t] >= vars.expansion[n, t-1])
|
||||
end
|
||||
|
||||
# Amount received equals amount processed plus stored
|
||||
store_in = 0
|
||||
if t > 1
|
||||
store_in = vars.store[n, t-1]
|
||||
end
|
||||
if t == T
|
||||
@constraint(mip, vars.store[n, t] == 0)
|
||||
end
|
||||
@constraint(mip,
|
||||
input_sum + store_in == vars.store[n, t] + vars.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(mip, vars.is_open[n, t] == vars.is_open[n, t-1] + vars.open_plant[n, t])
|
||||
else
|
||||
@constraint(mip, vars.is_open[n, t] == vars.open_plant[n, t])
|
||||
end
|
||||
|
||||
# Plant can only be opened during building period
|
||||
if t ∉ model.instance.building_period
|
||||
@constraint(mip, vars.open_plant[n, t] == 0)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
default_milp_optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
|
||||
default_lp_optimizer = optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
|
||||
|
||||
function solve(instance::Instance;
|
||||
optimizer=nothing,
|
||||
output=nothing,
|
||||
marginal_costs=true,
|
||||
)
|
||||
|
||||
milp_optimizer = lp_optimizer = optimizer
|
||||
if optimizer == nothing
|
||||
milp_optimizer = default_milp_optimizer
|
||||
lp_optimizer = default_lp_optimizer
|
||||
end
|
||||
|
||||
@info "Building graph..."
|
||||
graph = RELOG.build_graph(instance)
|
||||
@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))
|
||||
|
||||
@info "Building optimization model..."
|
||||
model = RELOG.build_model(instance, graph, milp_optimizer)
|
||||
|
||||
@info "Optimizing MILP..."
|
||||
JuMP.optimize!(model.mip)
|
||||
|
||||
if !has_values(model.mip)
|
||||
@warn "No solution available"
|
||||
return OrderedDict()
|
||||
end
|
||||
|
||||
if marginal_costs
|
||||
@info "Re-optimizing with integer variables fixed..."
|
||||
all_vars = JuMP.all_variables(model.mip)
|
||||
vals = OrderedDict(var => JuMP.value(var) for var in all_vars)
|
||||
JuMP.set_optimizer(model.mip, 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.mip)
|
||||
end
|
||||
|
||||
@info "Extracting solution..."
|
||||
solution = get_solution(model, marginal_costs=marginal_costs)
|
||||
|
||||
if output != nothing
|
||||
write(solution, output)
|
||||
end
|
||||
|
||||
return solution
|
||||
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
|
||||
|
||||
|
||||
function get_solution(model::ManufacturingModel;
|
||||
marginal_costs=true,
|
||||
)
|
||||
mip, vars, eqs, graph, instance = model.mip, model.vars, model.eqs, 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(eqs.balance[n, t])), digits=2)
|
||||
for t in 1:T]
|
||||
)
|
||||
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 in 1:T],
|
||||
"Total output" => OrderedDict(),
|
||||
"Latitude (deg)" => plant.latitude,
|
||||
"Longitude (deg)" => plant.longitude,
|
||||
"Capacity (tonne)" => [JuMP.value(vars.capacity[process_node, t])
|
||||
for t in 1:T],
|
||||
"Opening cost (\$)" => [JuMP.value(vars.open_plant[process_node, t]) *
|
||||
plant.sizes[1].opening_cost[t]
|
||||
for t in 1:T],
|
||||
"Fixed operating cost (\$)" => [JuMP.value(vars.is_open[process_node, t]) *
|
||||
plant.sizes[1].fixed_operating_cost[t] +
|
||||
JuMP.value(vars.expansion[process_node, t]) *
|
||||
slope_fix_oper_cost(plant, t)
|
||||
for t in 1:T],
|
||||
"Expansion cost (\$)" => [(if t == 1
|
||||
slope_open(plant, t) * JuMP.value(vars.expansion[process_node, t])
|
||||
else
|
||||
slope_open(plant, t) * (
|
||||
JuMP.value(vars.expansion[process_node, t]) -
|
||||
JuMP.value(vars.expansion[process_node, t - 1])
|
||||
)
|
||||
end)
|
||||
for t in 1:T],
|
||||
"Process (tonne)" => [JuMP.value(vars.process[process_node, t])
|
||||
for t in 1:T],
|
||||
"Variable operating cost (\$)" => [JuMP.value(vars.process[process_node, t]) *
|
||||
plant.sizes[1].variable_operating_cost[t]
|
||||
for t in 1:T],
|
||||
"Storage (tonne)" => [JuMP.value(vars.store[process_node, t])
|
||||
for t in 1:T],
|
||||
"Storage cost (\$)" => [JuMP.value(vars.store[process_node, t]) *
|
||||
plant.storage_cost[t]
|
||||
for t in 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(vars.flow[a, t]) for t in 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(vars.dispose[shipping_node, t]) for t in 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.vars.dispose[shipping_node, t])
|
||||
for t in 1:T]
|
||||
disposal_dict["Cost (\$)"] = [disposal_dict["Amount (tonne)"][t] *
|
||||
plant.disposal_cost[shipping_node.product][t]
|
||||
for t in 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(vars.flow[a, t]) for t in 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
|
||||
302
src/model/build.jl
Normal file
302
src/model/build.jl
Normal file
@@ -0,0 +1,302 @@
|
||||
using JuMP
|
||||
|
||||
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
|
||||
|
||||
# Transportation edges
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
# Connectivity
|
||||
model.ext[:E] = E = []
|
||||
model.ext[:E_in] = E_in = Dict(src => [] for src in plants ∪ centers)
|
||||
model.ext[:E_out] = E_out = Dict(src => [] for src in plants ∪ centers)
|
||||
|
||||
function push_edge!(src, dst, m)
|
||||
push!(E, (src, dst, m))
|
||||
push!(E_out[src], (dst, m))
|
||||
push!(E_in[dst], (src, m))
|
||||
end
|
||||
|
||||
for m in products
|
||||
for p1 in plants
|
||||
m ∈ keys(p1.output) || continue
|
||||
|
||||
# Plant to plant
|
||||
for p2 in plants
|
||||
p1 != p2 || continue
|
||||
m ∈ keys(p2.input_mix) || continue
|
||||
push_edge!(p1, p2, m)
|
||||
end
|
||||
|
||||
# Plant to center
|
||||
for c in centers
|
||||
m == c.input || continue
|
||||
push_edge!(p1, c, m)
|
||||
end
|
||||
end
|
||||
|
||||
for c1 in centers
|
||||
m ∈ c1.outputs || continue
|
||||
|
||||
# Center to plant
|
||||
for p in plants
|
||||
m ∈ keys(p.input_mix) || continue
|
||||
push_edge!(c1, p, m)
|
||||
end
|
||||
|
||||
# Center to center
|
||||
for c2 in centers
|
||||
m == c2.input || continue
|
||||
push_edge!(c1, c2, m)
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
# Distances
|
||||
model.ext[:distances] = distances = Dict()
|
||||
for (p1, p2, m) in E
|
||||
d = _calculate_distance(p1.latitude, p1.longitude, p2.latitude, p2.longitude)
|
||||
distances[p1, p2, m] = d
|
||||
end
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
# Objective function
|
||||
# -------------------------------------------------------------------------
|
||||
obj = AffExpr()
|
||||
|
||||
# Transportation cost
|
||||
for (p1, p2, m) in E, t in T
|
||||
add_to_expression!(
|
||||
obj,
|
||||
distances[p1, p2, m] * m.tr_cost[t],
|
||||
y[p1.name, p2.name, m.name, t],
|
||||
)
|
||||
end
|
||||
|
||||
# Center: Revenue
|
||||
for c in centers, (p, m) in E_in[c], t in T
|
||||
add_to_expression!(obj, -c.revenue[t], y[p.name, c.name, m.name, t])
|
||||
end
|
||||
|
||||
# Center: Collection cost
|
||||
for c in centers, (p, m) in E_out[c], t in T
|
||||
add_to_expression!(obj, c.collection_cost[m][t], y[c.name, p.name, m.name, t])
|
||||
end
|
||||
|
||||
# Center: Disposal cost
|
||||
for c in centers, m in c.outputs, t in T
|
||||
add_to_expression!(obj, c.disposal_cost[m][t], z_disp[c.name, m.name, t])
|
||||
end
|
||||
|
||||
# Center: Operating cost
|
||||
for c in centers, t in T
|
||||
add_to_expression!(obj, c.operating_cost[t])
|
||||
end
|
||||
|
||||
# Plants: Disposal cost
|
||||
for p in plants, m in keys(p.output), t in T
|
||||
add_to_expression!(obj, p.disposal_cost[m][t], z_disp[p.name, m.name, t])
|
||||
end
|
||||
|
||||
# Plants: Opening cost
|
||||
for p in plants, t in T
|
||||
add_to_expression!(
|
||||
obj,
|
||||
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)
|
||||
|
||||
# Constraints
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
# Plants: Definition of total plant input
|
||||
eq_z_input = _init(model, :eq_z_input)
|
||||
for p in plants, t in T
|
||||
eq_z_input[p.name, t] = @constraint(
|
||||
model,
|
||||
z_input[p.name, t] ==
|
||||
sum(y[src.name, p.name, m.name, t] for (src, m) in E_in[p])
|
||||
)
|
||||
end
|
||||
|
||||
# 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
|
||||
|
||||
# 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,
|
||||
z_input[c.name, t] ==
|
||||
sum(y[src.name, c.name, m.name, t] for (src, m) in E_in[c])
|
||||
)
|
||||
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)
|
||||
) + sum(
|
||||
c.fixed_output[m][t,mi]
|
||||
for mi in 1:length(m.components)
|
||||
)
|
||||
)
|
||||
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
|
||||
|
||||
if variable_names
|
||||
_set_names!(model)
|
||||
end
|
||||
return model
|
||||
end
|
||||
11
src/model/dist.jl
Normal file
11
src/model/dist.jl
Normal file
@@ -0,0 +1,11 @@
|
||||
# RELOG: Reverse Logistics Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
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 = 3)
|
||||
end
|
||||
47
src/model/jumpext.jl
Normal file
47
src/model/jumpext.jl
Normal file
@@ -0,0 +1,47 @@
|
||||
# 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
|
||||
278
src/reports.jl
278
src/reports.jl
@@ -1,278 +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 plants_report(solution)::DataFrame
|
||||
df = DataFrame()
|
||||
df."plant type" = String[]
|
||||
df."location name" = String[]
|
||||
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."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 in 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
|
||||
end
|
||||
end
|
||||
return df
|
||||
end
|
||||
|
||||
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 in 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
|
||||
|
||||
|
||||
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 in 1:T
|
||||
push!(df, [
|
||||
plant_name,
|
||||
location_name,
|
||||
year,
|
||||
emission_name,
|
||||
round(emission_amount[year], digits=2),
|
||||
])
|
||||
end
|
||||
end
|
||||
end
|
||||
end
|
||||
return df
|
||||
end
|
||||
|
||||
|
||||
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 in 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
|
||||
|
||||
|
||||
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 in 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
|
||||
|
||||
function write(solution::AbstractDict, filename::AbstractString)
|
||||
@info "Writing solution: $filename"
|
||||
open(filename, "w") do file
|
||||
JSON.print(file, solution, 2)
|
||||
end
|
||||
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))
|
||||
|
||||
write_plant_emissions_report(solution, filename) =
|
||||
CSV.write(filename, plant_emissions_report(solution))
|
||||
|
||||
write_transportation_report(solution, filename) =
|
||||
CSV.write(filename, transportation_report(solution))
|
||||
|
||||
write_transportation_emissions_report(solution, filename) =
|
||||
CSV.write(filename, transportation_emissions_report(solution))
|
||||
91
src/reports/centers.jl
Normal file
91
src/reports/centers.jl
Normal file
@@ -0,0 +1,91 @@
|
||||
# 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."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,
|
||||
[
|
||||
c.name,
|
||||
t,
|
||||
input_name,
|
||||
_round(input),
|
||||
_round(revenue),
|
||||
_round(c.operating_cost[t]),
|
||||
],
|
||||
)
|
||||
end
|
||||
return df
|
||||
end
|
||||
|
||||
function center_outputs_report(model)::DataFrame
|
||||
df = DataFrame()
|
||||
df."center" = String[]
|
||||
df."output product" = String[]
|
||||
df."year" = Int[]
|
||||
df."amount collected (tonne)" = Float64[]
|
||||
df."amount disposed (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,
|
||||
[
|
||||
c.name,
|
||||
m.name,
|
||||
t,
|
||||
_round(collected),
|
||||
_round(disposed),
|
||||
_round(collection_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))
|
||||
72
src/reports/plants.jl
Normal file
72
src/reports/plants.jl
Normal file
@@ -0,0 +1,72 @@
|
||||
# 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 plants_report(model)::DataFrame
|
||||
df = DataFrame()
|
||||
df."plant" = String[]
|
||||
df."year" = Int[]
|
||||
df."operational?" = Bool[]
|
||||
df."input amount (tonne)" = Float64[]
|
||||
df."opening cost (\$)" = Float64[]
|
||||
df."fixed operating cost (\$)" = Float64[]
|
||||
df."variable operating cost (\$)" = Float64[]
|
||||
|
||||
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 = 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
|
||||
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,
|
||||
[
|
||||
p.name,
|
||||
t,
|
||||
operational,
|
||||
_round(input),
|
||||
_round(opening_cost),
|
||||
_round(fix_operating_cost),
|
||||
_round(var_operating_cost),
|
||||
],
|
||||
)
|
||||
end
|
||||
return df
|
||||
end
|
||||
|
||||
function plant_outputs_report(model)::DataFrame
|
||||
df = DataFrame()
|
||||
df."plant" = String[]
|
||||
df."output product" = String[]
|
||||
df."year" = Int[]
|
||||
df."amount produced (tonne)" = Float64[]
|
||||
df."amount disposed (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,
|
||||
[p.name, m.name, t, _round(produced), _round(disposed), _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))
|
||||
56
src/reports/transportation.jl
Normal file
56
src/reports/transportation.jl
Normal file
@@ -0,0 +1,56 @@
|
||||
# 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,
|
||||
[
|
||||
p1.name,
|
||||
p2.name,
|
||||
m.name,
|
||||
t,
|
||||
_round(amount),
|
||||
_round(distance),
|
||||
_round(tr_cost),
|
||||
_round(revenue),
|
||||
_round(collection_cost),
|
||||
],
|
||||
)
|
||||
end
|
||||
return df
|
||||
end
|
||||
|
||||
write_transportation_report(solution, filename) =
|
||||
CSV.write(filename, transportation_report(solution))
|
||||
@@ -1,145 +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": {
|
||||
"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": [
|
||||
"latitude (deg)",
|
||||
"longitude (deg)",
|
||||
"capacities (tonne)"
|
||||
]
|
||||
}
|
||||
},
|
||||
"InitialAmount": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"latitude (deg)": { "type": "number" },
|
||||
"longitude (deg)": { "type": "number" },
|
||||
"amount (tonne)": { "$ref": "#/definitions/TimeSeries" }
|
||||
},
|
||||
"required": [
|
||||
"latitude (deg)",
|
||||
"longitude (deg)",
|
||||
"amount (tonne)"
|
||||
]
|
||||
}
|
||||
},
|
||||
"Product": {
|
||||
"type": "object",
|
||||
"additionalProperties": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"transportation cost ($/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"
|
||||
]
|
||||
}
|
||||
@@ -1,22 +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
14
test/Project.toml
Normal 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"
|
||||
181
test/fixtures/boat_example.jl
vendored
Normal file
181
test/fixtures/boat_example.jl
vendored
Normal file
@@ -0,0 +1,181 @@
|
||||
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], [100], [100], [100], [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], [0], [0], [0], [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),
|
||||
"components" => ["1"],
|
||||
)
|
||||
|
||||
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 ($city_name)" =>
|
||||
merge(recycling_plant, lat_lon_dict(city_location)) for
|
||||
(city_name, city_location) in cities_a
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
# Generate instance file
|
||||
open(fixture("boat_example.json"), "w") do file
|
||||
JSON.print(file, data, 2)
|
||||
end
|
||||
|
||||
# Load and solve example
|
||||
instance = RELOG.parsefile(fixture("boat_example.json"))
|
||||
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
|
||||
optimize!(model)
|
||||
|
||||
# Write reports
|
||||
mkpath(fixture("boat_example"))
|
||||
write_to_file(model, fixture("boat_example/model.lp"))
|
||||
RELOG.write_plants_report(model, fixture("boat_example/plants.csv"))
|
||||
RELOG.write_plant_outputs_report(model, fixture("boat_example/plant_outputs.csv"))
|
||||
RELOG.write_centers_report(model, fixture("boat_example/centers.csv"))
|
||||
RELOG.write_center_outputs_report(model, fixture("boat_example/center_outputs.csv"))
|
||||
RELOG.write_transportation_report(model, fixture("boat_example/transportation.csv"))
|
||||
|
||||
return
|
||||
end
|
||||
1569
test/fixtures/boat_example.json
vendored
Normal file
1569
test/fixtures/boat_example.json
vendored
Normal file
File diff suppressed because it is too large
Load Diff
81
test/fixtures/boat_example/center_outputs.csv
vendored
Normal file
81
test/fixtures/boat_example/center_outputs.csv
vendored
Normal file
@@ -0,0 +1,81 @@
|
||||
center,output product,year,amount collected (tonne),amount disposed (tonne),collection cost ($),disposal cost ($)
|
||||
NailFactory (Chicago),Nail,1,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Chicago),Nail,2,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Chicago),Nail,3,1.0,-0.0,1000.0,-0.0
|
||||
NailFactory (Chicago),Nail,4,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Chicago),Nail,5,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Phoenix),Nail,1,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Phoenix),Nail,2,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Phoenix),Nail,3,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Phoenix),Nail,4,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Phoenix),Nail,5,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Dallas),Nail,1,1.0,-0.0,1000.0,-0.0
|
||||
NailFactory (Dallas),Nail,2,1.0,-0.0,1000.0,-0.0
|
||||
NailFactory (Dallas),Nail,3,1.0,-0.0,1000.0,-0.0
|
||||
NailFactory (Dallas),Nail,4,1.0,0.0,1000.0,0.0
|
||||
NailFactory (Dallas),Nail,5,1.0,0.0,1000.0,0.0
|
||||
Forest (Chicago),Wood,1,100.0,100.0,0.0,0.0
|
||||
Forest (Chicago),Wood,2,100.0,100.0,0.0,0.0
|
||||
Forest (Chicago),Wood,3,100.0,100.0,0.0,0.0
|
||||
Forest (Chicago),Wood,4,100.0,100.0,0.0,0.0
|
||||
Forest (Chicago),Wood,5,100.0,100.0,0.0,0.0
|
||||
Forest (Phoenix),Wood,1,100.0,100.0,0.0,0.0
|
||||
Forest (Phoenix),Wood,2,100.0,100.0,0.0,0.0
|
||||
Forest (Phoenix),Wood,3,100.0,100.0,0.0,0.0
|
||||
Forest (Phoenix),Wood,4,100.0,100.0,0.0,0.0
|
||||
Forest (Phoenix),Wood,5,100.0,100.0,0.0,0.0
|
||||
Forest (Dallas),Wood,1,100.0,43.0,14250.0,0.0
|
||||
Forest (Dallas),Wood,2,100.0,43.0,14250.0,0.0
|
||||
Forest (Dallas),Wood,3,100.0,43.0,14250.0,0.0
|
||||
Forest (Dallas),Wood,4,100.0,43.0,14250.0,0.0
|
||||
Forest (Dallas),Wood,5,100.0,43.0,14250.0,0.0
|
||||
Retail (Chicago),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (Chicago),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (Chicago),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (Chicago),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (Chicago),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (New York City),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (New York City),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (New York City),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (New York City),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (New York City),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (Los Angeles),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (Los Angeles),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (Los Angeles),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (Los Angeles),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (Los Angeles),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (Houston),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (Houston),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (Houston),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (Houston),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (Houston),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (Phoenix),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (Phoenix),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (Phoenix),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (Phoenix),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (Phoenix),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (Philadelphia),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (Philadelphia),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (Philadelphia),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (Philadelphia),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (Philadelphia),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (San Antonio),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (San Antonio),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (San Antonio),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (San Antonio),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (San Antonio),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (San Diego),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (San Diego),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (San Diego),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (San Diego),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (San Diego),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
Retail (Dallas),UsedBoat,1,6.31579,0.0,631.57895,0.0
|
||||
Retail (Dallas),UsedBoat,2,22.93629,0.0,2293.62881,0.0
|
||||
Retail (Dallas),UsedBoat,3,31.7714,0.0,3177.13952,0.0
|
||||
Retail (Dallas),UsedBoat,4,33.80867,0.0,3380.86724,0.0
|
||||
Retail (Dallas),UsedBoat,5,34.54174,0.0,3454.17409,0.0
|
||||
Retail (San Jose),UsedBoat,1,0.0,0.0,0.0,0.0
|
||||
Retail (San Jose),UsedBoat,2,0.0,0.0,0.0,0.0
|
||||
Retail (San Jose),UsedBoat,3,0.0,0.0,0.0,0.0
|
||||
Retail (San Jose),UsedBoat,4,0.0,0.0,0.0,0.0
|
||||
Retail (San Jose),UsedBoat,5,0.0,0.0,0.0,0.0
|
||||
|
81
test/fixtures/boat_example/centers.csv
vendored
Normal file
81
test/fixtures/boat_example/centers.csv
vendored
Normal file
@@ -0,0 +1,81 @@
|
||||
center,year,input product,input amount (tonne),revenue ($),operating cost ($)
|
||||
NailFactory (Chicago),1,,0.0,0.0,0.0
|
||||
NailFactory (Chicago),2,,0.0,0.0,0.0
|
||||
NailFactory (Chicago),3,,0.0,0.0,0.0
|
||||
NailFactory (Chicago),4,,0.0,0.0,0.0
|
||||
NailFactory (Chicago),5,,0.0,0.0,0.0
|
||||
NailFactory (Phoenix),1,,0.0,0.0,0.0
|
||||
NailFactory (Phoenix),2,,0.0,0.0,0.0
|
||||
NailFactory (Phoenix),3,,0.0,0.0,0.0
|
||||
NailFactory (Phoenix),4,,0.0,0.0,0.0
|
||||
NailFactory (Phoenix),5,,0.0,0.0,0.0
|
||||
NailFactory (Dallas),1,,0.0,0.0,0.0
|
||||
NailFactory (Dallas),2,,0.0,0.0,0.0
|
||||
NailFactory (Dallas),3,,0.0,0.0,0.0
|
||||
NailFactory (Dallas),4,,0.0,0.0,0.0
|
||||
NailFactory (Dallas),5,,0.0,0.0,0.0
|
||||
Forest (Chicago),1,,0.0,0.0,0.0
|
||||
Forest (Chicago),2,,0.0,0.0,0.0
|
||||
Forest (Chicago),3,,0.0,0.0,0.0
|
||||
Forest (Chicago),4,,0.0,0.0,0.0
|
||||
Forest (Chicago),5,,0.0,0.0,0.0
|
||||
Forest (Phoenix),1,,0.0,0.0,0.0
|
||||
Forest (Phoenix),2,,0.0,0.0,0.0
|
||||
Forest (Phoenix),3,,0.0,0.0,0.0
|
||||
Forest (Phoenix),4,,0.0,0.0,0.0
|
||||
Forest (Phoenix),5,,0.0,0.0,0.0
|
||||
Forest (Dallas),1,,0.0,0.0,0.0
|
||||
Forest (Dallas),2,,0.0,0.0,0.0
|
||||
Forest (Dallas),3,,0.0,0.0,0.0
|
||||
Forest (Dallas),4,,0.0,0.0,0.0
|
||||
Forest (Dallas),5,,0.0,0.0,0.0
|
||||
Retail (Chicago),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Chicago),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Chicago),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Chicago),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Chicago),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (New York City),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (New York City),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (New York City),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (New York City),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (New York City),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Los Angeles),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Los Angeles),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Los Angeles),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Los Angeles),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Los Angeles),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Houston),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Houston),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Houston),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Houston),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Houston),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Phoenix),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Phoenix),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Phoenix),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Phoenix),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Phoenix),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Philadelphia),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Philadelphia),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Philadelphia),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Philadelphia),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Philadelphia),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Antonio),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Antonio),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Antonio),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Antonio),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Antonio),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Diego),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Diego),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Diego),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Diego),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Diego),5,NewBoat,0.0,0.0,125000.0
|
||||
Retail (Dallas),1,NewBoat,63.15789,757894.73684,125000.0
|
||||
Retail (Dallas),2,NewBoat,71.46814,857617.72853,125000.0
|
||||
Retail (Dallas),3,NewBoat,75.8857,910628.37148,125000.0
|
||||
Retail (Dallas),4,NewBoat,76.90434,922852.03459,125000.0
|
||||
Retail (Dallas),5,NewBoat,77.27087,927250.44516,125000.0
|
||||
Retail (San Jose),1,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Jose),2,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Jose),3,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Jose),4,NewBoat,0.0,0.0,125000.0
|
||||
Retail (San Jose),5,NewBoat,0.0,0.0,125000.0
|
||||
|
151
test/fixtures/boat_example/plant_outputs.csv
vendored
Normal file
151
test/fixtures/boat_example/plant_outputs.csv
vendored
Normal file
@@ -0,0 +1,151 @@
|
||||
plant,output product,year,amount produced (tonne),amount disposed (tonne),disposal cost ($)
|
||||
BoatFactory (Chicago),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (Chicago),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (Chicago),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (Chicago),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (Chicago),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (New York City),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (New York City),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (New York City),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (New York City),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (New York City),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (Houston),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (Houston),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (Houston),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (Houston),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (Houston),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),NewBoat,5,0.0,0.0,0.0
|
||||
BoatFactory (Dallas),NewBoat,1,63.15789,0.0,0.0
|
||||
BoatFactory (Dallas),NewBoat,2,71.46814,0.0,0.0
|
||||
BoatFactory (Dallas),NewBoat,3,75.8857,0.0,0.0
|
||||
BoatFactory (Dallas),NewBoat,4,76.90434,0.0,0.0
|
||||
BoatFactory (Dallas),NewBoat,5,77.27087,0.0,0.0
|
||||
BoatFactory (San Jose),NewBoat,1,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),NewBoat,2,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),NewBoat,3,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),NewBoat,4,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),NewBoat,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),Wood,5,0.0,0.0,0.0
|
||||
RecyclingPlant (Dallas),Nail,1,0.15789,0.0,0.0
|
||||
RecyclingPlant (Dallas),Nail,2,0.57341,0.0,0.0
|
||||
RecyclingPlant (Dallas),Nail,3,0.79428,0.0,0.0
|
||||
RecyclingPlant (Dallas),Nail,4,0.84522,0.0,0.0
|
||||
RecyclingPlant (Dallas),Nail,5,0.86354,0.0,0.0
|
||||
RecyclingPlant (Dallas),Wood,1,3.0,0.0,0.0
|
||||
RecyclingPlant (Dallas),Wood,2,10.89474,0.0,0.0
|
||||
RecyclingPlant (Dallas),Wood,3,15.09141,0.0,0.0
|
||||
RecyclingPlant (Dallas),Wood,4,16.05912,0.0,0.0
|
||||
RecyclingPlant (Dallas),Wood,5,16.40733,0.0,0.0
|
||||
RecyclingPlant (San Jose),Nail,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Nail,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Nail,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Nail,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Nail,5,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Wood,1,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Wood,2,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Wood,3,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Wood,4,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),Wood,5,0.0,0.0,0.0
|
||||
|
101
test/fixtures/boat_example/plants.csv
vendored
Normal file
101
test/fixtures/boat_example/plants.csv
vendored
Normal file
@@ -0,0 +1,101 @@
|
||||
plant,year,operational?,input amount (tonne),opening cost ($),fixed operating cost ($),variable operating cost ($)
|
||||
BoatFactory (Chicago),1,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Chicago),2,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Chicago),3,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Chicago),4,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Chicago),5,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (New York City),1,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (New York City),2,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (New York City),3,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (New York City),4,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (New York City),5,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Los Angeles),1,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Los Angeles),2,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Los Angeles),3,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Los Angeles),4,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Los Angeles),5,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Houston),1,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Houston),2,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Houston),3,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Houston),4,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Houston),5,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Phoenix),1,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Phoenix),2,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Phoenix),3,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Phoenix),4,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (Phoenix),5,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),1,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),2,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),3,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),4,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Philadelphia),5,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Antonio),1,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (San Antonio),2,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (San Antonio),3,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (San Antonio),4,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (San Antonio),5,false,-0.0,0.0,0.0,-0.0
|
||||
BoatFactory (San Diego),1,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),2,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),3,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),4,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Diego),5,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (Dallas),1,true,63.15789,100000.0,250000.0,315.78947
|
||||
BoatFactory (Dallas),2,true,71.46814,0.0,250000.0,357.34072
|
||||
BoatFactory (Dallas),3,true,75.8857,0.0,250000.0,379.42849
|
||||
BoatFactory (Dallas),4,true,76.90434,0.0,250000.0,384.52168
|
||||
BoatFactory (Dallas),5,true,77.27087,0.0,250000.0,386.35435
|
||||
BoatFactory (San Jose),1,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),2,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),3,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),4,false,0.0,0.0,0.0,0.0
|
||||
BoatFactory (San Jose),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Chicago),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (New York City),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Los Angeles),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Houston),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),3,false,-0.0,0.0,0.0,-0.0
|
||||
RecyclingPlant (Phoenix),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Phoenix),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Philadelphia),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Antonio),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Diego),5,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (Dallas),1,true,6.31579,500000.0,125000.0,15.78947
|
||||
RecyclingPlant (Dallas),2,true,22.93629,0.0,125000.0,57.34072
|
||||
RecyclingPlant (Dallas),3,true,31.7714,0.0,125000.0,79.42849
|
||||
RecyclingPlant (Dallas),4,true,33.80867,0.0,125000.0,84.52168
|
||||
RecyclingPlant (Dallas),5,true,34.54174,0.0,125000.0,86.35435
|
||||
RecyclingPlant (San Jose),1,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),2,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),3,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),4,false,0.0,0.0,0.0,0.0
|
||||
RecyclingPlant (San Jose),5,false,0.0,0.0,0.0,0.0
|
||||
|
41
test/fixtures/boat_example/transportation.csv
vendored
Normal file
41
test/fixtures/boat_example/transportation.csv
vendored
Normal 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
|
||||
|
7
test/fixtures/nimh_plant_emissions.csv
vendored
7
test/fixtures/nimh_plant_emissions.csv
vendored
@@ -1,7 +0,0 @@
|
||||
plant type,location name,year,emission type,emission amount (tonne)
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,CO2,40711.3
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,CO2,23336.47
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,CO2,52927.44
|
||||
Mega Plant,"Sebastian, Arkansas",1,CO2,110818.84
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,CO2,63523.43
|
||||
Mega Plant,"Maricopa, Arizona",1,CO2,144072.0
|
||||
|
37
test/fixtures/nimh_plant_outputs.csv
vendored
37
test/fixtures/nimh_plant_outputs.csv
vendored
@@ -1,37 +0,0 @@
|
||||
plant type,location name,year,product name,amount produced (tonne),amount sent (tonne),amount disposed (tonne),disposal cost ($)
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,rare earth cela,18045.12,0.0,18045.12,0.0
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,rare earth diddy,7624.02,0.0,7624.02,0.0
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,salt,6434.8,0.0,6434.8,324314.03
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,rare earth misch,2188.78,0.0,2188.78,0.0
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,rare earth cela,10343.8,0.0,10343.8,0.0
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,rare earth diddy,4370.23,0.0,4370.23,0.0
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,salt,3688.55,0.0,3688.55,185902.85
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,rare earth misch,1254.65,0.0,1254.65,0.0
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,rare earth cela,23459.88,0.0,23459.88,0.0
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,rare earth diddy,9911.74,0.0,9911.74,0.0
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,salt,8365.68,0.0,8365.68,421630.2
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,rare earth misch,2845.56,0.0,2845.56,0.0
|
||||
Mega Plant,"Sebastian, Arkansas",1,iron-nickel scrap,35656.28,0.0,35656.28,0.0
|
||||
Mega Plant,"Sebastian, Arkansas",1,mixed-hydroxides,73141.86,0.0,73141.86,0.0
|
||||
Mega Plant,"Sebastian, Arkansas",1,leach residue,31470.35,0.0,31470.35,6.38848022e6
|
||||
Mega Plant,"Sebastian, Arkansas",1,plastic pack,96503.37,0.0,96503.37,4.86376966e6
|
||||
Mega Plant,"Sebastian, Arkansas",1,salt,285304.6,0.0,285304.6,1.437935192e7
|
||||
Mega Plant,"Sebastian, Arkansas",1,rare earth mix,44145.84,44145.84,0.0,0.0
|
||||
Mega Plant,"Sebastian, Arkansas",1,nickel-iron scrap,178655.26,0.0,178655.26,0.0
|
||||
Mega Plant,"Sebastian, Arkansas",1,nickel,37931.36,0.0,37931.36,0.0
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,iron-nickel scrap,20438.84,0.0,20438.84,0.0
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,mixed-hydroxides,41926.28,0.0,41926.28,0.0
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,leach residue,18039.39,0.0,18039.39,3.66199595e6
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,plastic pack,55317.53,0.0,55317.53,2.78800343e6
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,salt,163541.92,0.0,163541.92,8.24251257e6
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,rare earth mix,25305.22,25305.22,0.0,0.0
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,nickel-iron scrap,102408.52,0.0,102408.52,0.0
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,nickel,21742.96,0.0,21742.96,0.0
|
||||
Mega Plant,"Maricopa, Arizona",1,iron-nickel scrap,46355.57,0.0,46355.57,0.0
|
||||
Mega Plant,"Maricopa, Arizona",1,mixed-hydroxides,95089.37,0.0,95089.37,0.0
|
||||
Mega Plant,"Maricopa, Arizona",1,leach residue,40913.58,0.0,40913.58,8.30545698e6
|
||||
Mega Plant,"Maricopa, Arizona",1,plastic pack,125460.91,0.0,125460.91,6.32322998e6
|
||||
Mega Plant,"Maricopa, Arizona",1,salt,370915.31,0.0,370915.31,1.869413141e7
|
||||
Mega Plant,"Maricopa, Arizona",1,rare earth mix,57392.58,57392.58,0.0,0.0
|
||||
Mega Plant,"Maricopa, Arizona",1,nickel-iron scrap,232263.93,0.0,232263.93,0.0
|
||||
Mega Plant,"Maricopa, Arizona",1,nickel,49313.34,0.0,49313.34,0.0
|
||||
|
7
test/fixtures/nimh_plants.csv
vendored
7
test/fixtures/nimh_plants.csv
vendored
@@ -1,7 +0,0 @@
|
||||
plant type,location name,year,latitude (deg),longitude (deg),capacity (tonne),amount processed (tonne),utilization factor (%),energy (GJ),opening cost ($),expansion cost ($),fixed operating cost ($),variable operating cost ($),total cost ($)
|
||||
Rare Earth Recycling Plant,"Sebastian, Arkansas",1,35.23416,-94.212943,44145.84,44145.84,100.0,1.13360359e6,6.9926855e6,1.793555439e7,1.677420707e7,1.0080261442e8,1.4250506138e8
|
||||
Rare Earth Recycling Plant,"Stanly, North Carolina",1,35.334445,-80.223231,25305.22,25305.22,100.0,649802.71,7.1653444e6,9.98954108e6,1.126536154e7,5.778193764e7,8.620218466e7
|
||||
Rare Earth Recycling Plant,"Lynn, Texas",1,33.166444,-101.793455,57392.58,57392.58,100.0,1.47376145e6,7.4243328e6,2.5154042e7,2.06474474e7,1.310502261e8,1.842760483e8
|
||||
Mega Plant,"Sebastian, Arkansas",1,35.23416,-94.212943,553817.3,553817.3,100.0,3.08574408e6,1.6858178e7,4.012879371e7,3.834652057e7,4.2898688058e8,5.2432037286e8
|
||||
Mega Plant,"District of Columbia, District of Columbia",1,38.930028,-76.974164,317458.4,317458.4,100.0,1.76880602e6,2.12288167e7,2.746685387e7,2.602109584e7,2.4590327664e8,3.2062004305e8
|
||||
Mega Plant,"Maricopa, Arizona",1,33.647365,-111.893669,720000.0,720000.0,100.0,4.0116763e6,2.10206911e7,6.60955172e7,4.70124619e7,5.57712e8,6.918406702e8
|
||||
|
BIN
test/fixtures/nimh_solution.json.gz
vendored
BIN
test/fixtures/nimh_solution.json.gz
vendored
Binary file not shown.
3618
test/fixtures/nimh_transportation.csv
vendored
3618
test/fixtures/nimh_transportation.csv
vendored
File diff suppressed because it is too large
Load Diff
3618
test/fixtures/nimh_transportation_emissions.csv
vendored
3618
test/fixtures/nimh_transportation_emissions.csv
vendored
File diff suppressed because it is too large
Load Diff
202
test/fixtures/s1-wrong-length.json
vendored
202
test/fixtures/s1-wrong-length.json
vendored
@@ -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 ($/tonne)": [-15.0, -15.0]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
164
test/fixtures/simple.json
vendored
Normal file
164
test/fixtures/simple.json
vendored
Normal file
@@ -0,0 +1,164 @@
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 4,
|
||||
"building period (years)": [1],
|
||||
"distance metric": "driving"
|
||||
},
|
||||
"products": {
|
||||
"P1": {
|
||||
"transportation cost ($/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]
|
||||
},
|
||||
"components": ["1", "2"]
|
||||
},
|
||||
"P2": {
|
||||
"transportation cost ($/km/tonne)": [0.015, 0.015, 0.015, 0.015],
|
||||
"transportation energy (J/km/tonne)": [0.12, 0.12, 0.12, 0.12],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [0.052, 0.052, 0.052, 0.052],
|
||||
"CH4": [0.003, 0.003, 0.003, 0.003]
|
||||
},
|
||||
"components": ["1"]
|
||||
},
|
||||
"P3": {
|
||||
"transportation cost ($/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]
|
||||
},
|
||||
"components": ["1"]
|
||||
},
|
||||
"P4": {
|
||||
"transportation cost ($/km/tonne)": [0.015, 0.015, 0.015, 0.015],
|
||||
"transportation energy (J/km/tonne)": [0.12, 0.12, 0.12, 0.12],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [0.052, 0.052, 0.052, 0.052],
|
||||
"CH4": [0.003, 0.003, 0.003, 0.003]
|
||||
},
|
||||
"components": ["1"]
|
||||
}
|
||||
},
|
||||
"centers": {
|
||||
"C1": {
|
||||
"latitude (deg)": 41.881,
|
||||
"longitude (deg)": -87.623,
|
||||
"input": "P1",
|
||||
"outputs": ["P2", "P3"],
|
||||
"fixed output (tonne)": {
|
||||
"P2": [[100], [50], [0], [0]],
|
||||
"P3": [[20], [10], [0], [0]]
|
||||
},
|
||||
"variable output (tonne/tonne)": {
|
||||
"P2": [0.2, 0.25, 0.12],
|
||||
"P3": [0.25, 0.25, 0.25]
|
||||
},
|
||||
"revenue ($/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 ($/tonne)": {
|
||||
"P2": [0.23, 0.23, 0.23, 0.23],
|
||||
"P3": [1.0, 1.0, 1.0, 1.0]
|
||||
}
|
||||
},
|
||||
"C2": {
|
||||
"latitude (deg)": 42.881,
|
||||
"longitude (deg)": -87.623,
|
||||
"input": null,
|
||||
"outputs": ["P1"],
|
||||
"variable output (tonne/tonne)": {
|
||||
"P1": 0
|
||||
},
|
||||
"fixed output (tonne)": {
|
||||
"P1": [
|
||||
[50, 5],
|
||||
[60, 6],
|
||||
[70, 7],
|
||||
[80, 8]
|
||||
]
|
||||
},
|
||||
"revenue ($/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 ($/tonne)": 5.0
|
||||
}
|
||||
],
|
||||
"initial capacity (tonne)": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
39
test/fixtures/storage.json
vendored
39
test/fixtures/storage.json
vendored
@@ -1,39 +0,0 @@
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 3
|
||||
},
|
||||
"products": {
|
||||
"battery": {
|
||||
"initial amounts": {
|
||||
"Chicago": {
|
||||
"latitude (deg)": 0.0,
|
||||
"longitude (deg)": 0.0,
|
||||
"amount (tonne)": [100.0, 0.0, 0.0]
|
||||
}
|
||||
},
|
||||
"transportation cost ($/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]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
650
test/fixtures/waste_example/example.json
vendored
Normal file
650
test/fixtures/waste_example/example.json
vendored
Normal file
@@ -0,0 +1,650 @@
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 5,
|
||||
"building period (years)": [1],
|
||||
"distance metric": "Euclidean"
|
||||
},
|
||||
"products": {
|
||||
"Waste": {
|
||||
"transportation cost ($/km/tonne)": 0.3,
|
||||
"transportation energy (J/km/tonne)": 7500,
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": 2.68
|
||||
},
|
||||
"components": ["Film", "Paper", "Cardboard"]
|
||||
},
|
||||
"Film Bale": {
|
||||
"transportation cost ($/km/tonne)": 0.3,
|
||||
"transportation energy (J/km/tonne)": 7500,
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": 2.68
|
||||
},
|
||||
"components": ["Film", "Paper", "Cardboard"]
|
||||
},
|
||||
"Cardboard Bale": {
|
||||
"transportation cost ($/km/tonne)": 0.3,
|
||||
"transportation energy (J/km/tonne)": 7500,
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": 2.68
|
||||
},
|
||||
"components": ["Film", "Paper", "Cardboard"]
|
||||
}
|
||||
},
|
||||
"centers": {
|
||||
"Collection Center (Chicago)": {
|
||||
"input": null,
|
||||
"outputs": ["Waste"],
|
||||
"fixed output (tonne)": {
|
||||
"Waste": [20, 100, 100]
|
||||
},
|
||||
"variable output (tonne/tonne)": {},
|
||||
"revenue ($/tonne)": null,
|
||||
"collection cost ($/tonne)": {
|
||||
"Waste": 10
|
||||
},
|
||||
"operating cost ($)": 0,
|
||||
"disposal limit (tonne)": {
|
||||
"Waste": null
|
||||
},
|
||||
"disposal cost ($/tonne)": {
|
||||
"Waste": 0
|
||||
},
|
||||
"latitude (deg)": 41.881832,
|
||||
"longitude (deg)": -87.623177
|
||||
},
|
||||
"Collection Center (Phoenix)": {
|
||||
"input": null,
|
||||
"outputs": ["Waste"],
|
||||
"fixed output (tonne)": {
|
||||
"Waste": [20, 100, 100]
|
||||
},
|
||||
"variable output (tonne/tonne)": {},
|
||||
"revenue ($/tonne)": null,
|
||||
"collection cost ($/tonne)": {
|
||||
"Waste": 10
|
||||
},
|
||||
"operating cost ($)": 0,
|
||||
"disposal limit (tonne)": {
|
||||
"Waste": null
|
||||
},
|
||||
"disposal cost ($/tonne)": {
|
||||
"Waste": 0
|
||||
},
|
||||
"latitude (deg)": 33.448376,
|
||||
"longitude (deg)": -112.074036
|
||||
},
|
||||
"Collection Center (Dallas)": {
|
||||
"input": null,
|
||||
"outputs": ["Waste"],
|
||||
"fixed output (tonne)": {
|
||||
"Waste": [20, 100, 100]
|
||||
},
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
"Waste": [
|
||||
[0.0, 0.0, 0.0],
|
||||
[0.0, 0.02, 0.0],
|
||||
[0.0, 0.0, 0.75]
|
||||
]
|
||||
}
|
||||
},
|
||||
"processing emissions (tonne)": {
|
||||
"CO2": 5
|
||||
},
|
||||
"storage cost ($/tonne)": {
|
||||
"Waste": 500
|
||||
},
|
||||
"storage limit (tonne)": {
|
||||
"Waste": 5
|
||||
},
|
||||
"disposal cost ($/tonne)": {
|
||||
"Film Bale": -10,
|
||||
"Cardboard Bale": -10
|
||||
},
|
||||
"disposal limit (tonne)": {
|
||||
"Film Bale": null,
|
||||
"Cardboard Bale": null
|
||||
},
|
||||
"capacities": [
|
||||
{
|
||||
"size (tonne)": 500,
|
||||
"opening cost ($)": 100000,
|
||||
"fixed operating cost ($)": 250000,
|
||||
"variable operating cost ($/tonne)": 5
|
||||
},
|
||||
{
|
||||
"size (tonne)": 1000,
|
||||
"opening cost ($)": 2000000,
|
||||
"fixed operating cost ($)": 500000,
|
||||
"variable operating cost ($/tonne)": 5
|
||||
}
|
||||
],
|
||||
"initial capacity (tonne)": 0,
|
||||
"latitude (deg)": 37.338208,
|
||||
"longitude (deg)": -121.886329
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,42 +0,0 @@
|
||||
# Copyright (C) 2020 Argonne National Laboratory
|
||||
# Written by Alinson Santos Xavier <axavier@anl.gov>
|
||||
|
||||
using RELOG
|
||||
|
||||
@testset "Graph" begin
|
||||
@testset "build_graph" begin
|
||||
basedir = dirname(@__FILE__)
|
||||
instance = RELOG.parsefile("$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
|
||||
end
|
||||
|
||||
@@ -1,127 +0,0 @@
|
||||
# Copyright (C) 2020 Argonne National Laboratory
|
||||
# Written by Alinson Santos Xavier <axavier@anl.gov>
|
||||
|
||||
using RELOG
|
||||
|
||||
@testset "Instance" begin
|
||||
@testset "load" 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 "validate timeseries" begin
|
||||
@test_throws String RELOG.parsefile("fixtures/s1-wrong-length.json")
|
||||
end
|
||||
|
||||
@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
|
||||
end
|
||||
|
||||
@@ -1,100 +0,0 @@
|
||||
# Copyright (C) 2020 Argonne National Laboratory
|
||||
# Written by Alinson Santos Xavier <axavier@anl.gov>
|
||||
|
||||
using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
|
||||
|
||||
@testset "Model" begin
|
||||
@testset "build" begin
|
||||
basedir = dirname(@__FILE__)
|
||||
instance = RELOG.parsefile("$basedir/../instances/s1.json")
|
||||
graph = RELOG.build_graph(instance)
|
||||
model = RELOG.build_model(instance, graph, Cbc.Optimizer)
|
||||
set_optimizer_attribute(model.mip, "logLevel", 0)
|
||||
|
||||
process_node_by_location_name = Dict(n.location.location_name => n
|
||||
for n in graph.process_nodes)
|
||||
|
||||
shipping_node_by_location_and_product_names = Dict((n.location.location_name, n.product.name) => n
|
||||
for n in graph.plant_shipping_nodes)
|
||||
|
||||
@test length(model.vars.flow) == 76
|
||||
@test length(model.vars.dispose) == 16
|
||||
@test length(model.vars.open_plant) == 12
|
||||
@test length(model.vars.capacity) == 12
|
||||
@test length(model.vars.expansion) == 12
|
||||
|
||||
l1 = process_node_by_location_name["L1"]
|
||||
v = model.vars.capacity[l1, 1]
|
||||
@test lower_bound(v) == 0.0
|
||||
@test upper_bound(v) == 1000.0
|
||||
|
||||
v = model.vars.expansion[l1, 1]
|
||||
@test lower_bound(v) == 0.0
|
||||
@test upper_bound(v) == 750.0
|
||||
|
||||
v = model.vars.dispose[shipping_node_by_location_and_product_names["L1", "P2"], 1]
|
||||
@test lower_bound(v) == 0.0
|
||||
@test upper_bound(v) == 1.0
|
||||
|
||||
# dest = FileFormats.Model(format = FileFormats.FORMAT_LP)
|
||||
# MOI.copy_to(dest, model.mip)
|
||||
# MOI.write_to_file(dest, "model.lp")
|
||||
end
|
||||
|
||||
@testset "solve (exact)" begin
|
||||
solution_filename_a = tempname()
|
||||
solution_filename_b = tempname()
|
||||
solution = RELOG.solve("$(pwd())/../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("$(pwd())/../instances/s1.json", heuristic=true)
|
||||
end
|
||||
|
||||
@testset "infeasible solve" begin
|
||||
json = JSON.parsefile("$(pwd())/../instances/s1.json")
|
||||
for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
|
||||
location_dict["amount (tonne)"] *= 1000
|
||||
end
|
||||
RELOG.solve(RELOG.parse(json))
|
||||
end
|
||||
|
||||
@testset "storage" begin
|
||||
basedir = dirname(@__FILE__)
|
||||
filename = "$basedir/fixtures/storage.json"
|
||||
instance = RELOG.parsefile(filename)
|
||||
@test instance.plants[1].storage_limit == 50.0
|
||||
@test instance.plants[1].storage_cost == [2.0, 1.5, 1.0]
|
||||
|
||||
solution = RELOG.solve(filename)
|
||||
plant_dict = solution["Plants"]["mega plant"]["Chicago"]
|
||||
@test plant_dict["Variable operating cost (\$)"] == [500.0, 0.0, 100.0]
|
||||
@test plant_dict["Process (tonne)"] == [50.0, 0.0, 50.0]
|
||||
@test plant_dict["Storage (tonne)"] == [50.0, 50.0, 0.0]
|
||||
@test plant_dict["Storage cost (\$)"] == [100.0, 75.0, 0.0]
|
||||
|
||||
@test solution["Costs"]["Variable operating (\$)"] == [500.0, 0.0, 100.0]
|
||||
@test solution["Costs"]["Storage (\$)"] == [100.0, 75.0, 0.0]
|
||||
@test solution["Costs"]["Total (\$)"] == [600.0, 75.0, 100.0]
|
||||
end
|
||||
end
|
||||
@@ -1,41 +0,0 @@
|
||||
# RELOG: Reverse Logistics Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using RELOG, JSON, GZip
|
||||
|
||||
load_json_gz(filename) = JSON.parse(GZip.gzopen(filename))
|
||||
|
||||
# function check(func, expected_csv_filename::String)
|
||||
# solution = load_json_gz("fixtures/nimh_solution.json.gz")
|
||||
# actual_csv_filename = tempname()
|
||||
# func(solution, actual_csv_filename)
|
||||
# @test isfile(actual_csv_filename)
|
||||
# if readlines(actual_csv_filename) != readlines(expected_csv_filename)
|
||||
# out_filename = replace(expected_csv_filename, ".csv" => "_actual.csv")
|
||||
# @error "$func: Unexpected CSV contents: $out_filename"
|
||||
# write(out_filename, read(actual_csv_filename))
|
||||
# @test false
|
||||
# end
|
||||
# end
|
||||
|
||||
@testset "Reports" begin
|
||||
# @testset "from fixture" begin
|
||||
# check(RELOG.write_plants_report, "fixtures/nimh_plants.csv")
|
||||
# check(RELOG.write_plant_outputs_report, "fixtures/nimh_plant_outputs.csv")
|
||||
# check(RELOG.write_plant_emissions_report, "fixtures/nimh_plant_emissions.csv")
|
||||
# check(RELOG.write_transportation_report, "fixtures/nimh_transportation.csv")
|
||||
# check(RELOG.write_transportation_emissions_report, "fixtures/nimh_transportation_emissions.csv")
|
||||
# end
|
||||
|
||||
@testset "from solve" begin
|
||||
solution = RELOG.solve("$(pwd())/../instances/s1.json")
|
||||
tmp_filename = tempname()
|
||||
# The following should not crash
|
||||
RELOG.write_plants_report(solution, tmp_filename)
|
||||
RELOG.write_plant_outputs_report(solution, tmp_filename)
|
||||
RELOG.write_plant_emissions_report(solution, tmp_filename)
|
||||
RELOG.write_transportation_report(solution, tmp_filename)
|
||||
RELOG.write_transportation_emissions_report(solution, tmp_filename)
|
||||
end
|
||||
end
|
||||
@@ -1,11 +0,0 @@
|
||||
# Copyright (C) 2020 Argonne National Laboratory
|
||||
# Written by Alinson Santos Xavier <axavier@anl.gov>
|
||||
|
||||
using Test
|
||||
|
||||
@testset "RELOG" begin
|
||||
include("instance_test.jl")
|
||||
include("graph_test.jl")
|
||||
include("model_test.jl")
|
||||
include("reports_test.jl")
|
||||
end
|
||||
38
test/src/RELOGT.jl
Normal file
38
test/src/RELOGT.jl
Normal file
@@ -0,0 +1,38 @@
|
||||
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
|
||||
end
|
||||
|
||||
function format()
|
||||
JuliaFormatter.format(basedir, verbose = true)
|
||||
JuliaFormatter.format("$basedir/../../src", verbose = true)
|
||||
JuliaFormatter.format("$basedir/../fixtures", verbose = true)
|
||||
return
|
||||
end
|
||||
|
||||
export format, runtests
|
||||
|
||||
end # module RELOGT
|
||||
77
test/src/instance/parse_test.jl
Normal file
77
test/src/instance/parse_test.jl
Normal file
@@ -0,0 +1,77 @@
|
||||
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 == "driving"
|
||||
|
||||
# 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.components == ["1", "2"]
|
||||
@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
|
||||
112
test/src/model/build_test.jl
Normal file
112
test/src/model/build_test.jl
Normal file
@@ -0,0 +1,112 @@
|
||||
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]
|
||||
x = model[:x]
|
||||
obj = objective_function(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
|
||||
)
|
||||
|
||||
# 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"
|
||||
@test repr(model[:eq_z_collected]["C2", "P1", 1]) == "eq_z_collected[C2,P1,1] : z_collected[C2,P1,1] = 55"
|
||||
|
||||
# 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])
|
||||
end
|
||||
10
test/src/model/dist_test.jl
Normal file
10
test/src/model/dist_test.jl
Normal file
@@ -0,0 +1,10 @@
|
||||
# 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) == 265.818
|
||||
end
|
||||
12
test/src/reports_test.jl
Normal file
12
test/src/reports_test.jl
Normal file
@@ -0,0 +1,12 @@
|
||||
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)
|
||||
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
|
||||
Reference in New Issue
Block a user