Update CHANGELOG

Update README.md
GH Actions: Update Julia versions
2025-12-06 07:48:50 -06:00 · 2022-08-26 13:26:15 -05:00 · 2022-08-26 13:21:42 -05:00 · 2022-08-26 13:12:20 -05:00 · 2022-08-26 13:08:22 -05:00 · 2022-08-26 13:04:47 -05:00
77 changed files with 3195 additions and 9365 deletions
--- a/.github/workflows/lint.yml
+++ b/.github/workflows/lint.yml
@@ -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="0.14.4"))
          using JuliaFormatter
          format("src", verbose=true)
          format("test", 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)
--- a/.github/workflows/tagbot.yml
+++ b/.github/workflows/tagbot.yml
@@ -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 }}
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -1,15 +1,16 @@
-name: CI
+name: Build & Test
 on:
-  - push
+  push:
-  - pull_request
+  pull_request:
  schedule:
    - cron: '45 10 * * *'
 jobs:
  test:
    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        version:
+        version: ['1.6', '1.7', '1.8']
          - '1.3'
        os:
          - ubuntu-latest
        arch:
@@ -20,5 +21,5 @@ jobs:
        with:
          version: ${{ matrix.version }}
          arch: ${{ matrix.arch }}
-      - uses: julia-actions/julia-buildpkg@latest
+      - uses: julia-actions/julia-buildpkg@v1
-      - uses: julia-actions/julia-runtest@latest
+      - uses: julia-actions/julia-runtest@v1
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,7 @@ instances/*.py
 notebooks
 .idea
 *.lp
 Manifest.toml
 data
 build
 benchmark
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,24 +1,53 @@
-# Version 0.4.0 (Sep 18, 2020)
+# Changelog
 All notable changes to this project will be documented in this file.
 - The format is based on [Keep a Changelog][changelog].
 - This project adheres to [Semantic Versioning][semver].
 - For versions before 1.0, we follow the [Pkg.jl convention][pkjjl]
  that `0.a.b` is compatible with `0.a.c`.
 [changelog]: https://keepachangelog.com/en/1.0.0/
 [semver]: https://semver.org/spec/v2.0.0.html
 [pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
 ## [0.5.2] -- 2022-08-26
 ### Changed
 - Update to JuMP 1.x
 ## [0.5.1] -- 2021-07-23
 ### Added
 - Allow user to specify locations as unique identifiers, instead of latitude and longitude (e.g. `us-state:IL` or `2018-us-county:17043`)
 - Add what-if scenarios.
 - Add products report.
 ## [0.5.0] -- 2021-01-06
 ### Added
 - Allow plants to store input material for processing in later years
 ## [0.4.0] -- 2020-09-18
 ### Added
 - Generate simplified solution reports (CSV)
-# Version 0.3.3 (Aug 13, 2020)
+## [0.3.3] -- 2020-10-13
-
+### Added
 - Add option to write solution to JSON file in RELOG.solve
 - Improve error message when instance is infeasible
 - Make output file more readable
-# Version 0.3.2 (Aug 7, 2020)
+## [0.3.2] -- 2020-10-07
-
+### Added
 - Add "building period" parameter
-# Version 0.3.1 (July 17, 2020)
+## [0.3.1] -- 2020-07-17
-
+### Fixed
 - Fix expansion cost breakdown
-# Version 0.3.0 (June 25, 2020)
+## [0.3.0] -- 2020-06-25
-
+### Added
 - Track emissions and energy (transportation and plants)
 ### Changed
 - Minor changes to input file format:
    - Make all dictionary keys lowercase
    - Rename "outputs (tonne)" to "outputs (tonne/tonne)"
--- a/30
+++ b/30
@@ -1,25 +1,19 @@
-JULIA := julia --color=yes --project=@.
+VERSION := 0.5
 SRC_FILES := $(wildcard src/*.jl test/*.jl)
 VERSION := 0.4
 all: docs test
 build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
 	$(JULIA) src/sysimage.jl
 build/test.log: $(SRC_FILES) build/sysimage.so
 	@echo Running tests...
 	cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl | tee ../build/test.log
 clean:
-	rm -rf build/*
+	rm -rfv build Manifest.toml test/Manifest.toml deps/formatter/build deps/formatter/Manifest.toml
 docs:
-	mkdocs build -d ../docs/$(VERSION)/
+	cd docs; julia --project=. make.jl; cd ..
 	rsync -avP --delete-after docs/build/ ../docs/$(VERSION)/
-test: build/test.log
+format:
 	cd deps/formatter; ../../juliaw format.jl
-test-watch:
+test: test/Manifest.toml
-	bash -c "while true; do make test --quiet; sleep 1; done"
+	./juliaw test/runtests.jl
-.PHONY: docs test
+test/Manifest.toml: test/Project.toml
 	julia --project=test -e "using Pkg; Pkg.instantiate()"
 .PHONY: docs test format
--- a/Manifest.toml
+++ b/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]]
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 git-tree-sha1 = "9e62e66db34540a0c919d72172cc2f642ac71260"
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 [[BinaryProvider]]
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 [[CoinUtils_jll]]
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 [[CommonSubexpressions]]
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 [[Compat]]
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 git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0"
 uuid = "189a3867-3050-52da-a836-e630ba90ab69"
 version = "0.2.0"
 [[SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 [[SentinelArrays]]
 deps = ["Dates", "Random"]
 git-tree-sha1 = "7a74946ace3b34fbb6c10e61b6e250b33d7e758c"
 uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
 version = "1.2.15"
 [[Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 [[SharedArrays]]
 deps = ["Distributed", "Mmap", "Random", "Serialization"]
 uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
 [[Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 [[SortingAlgorithms]]
 deps = ["DataStructures", "Random", "Test"]
 git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd"
 uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
 version = "0.3.1"
 [[SparseArrays]]
 deps = ["LinearAlgebra", "Random"]
 uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 [[SpecialFunctions]]
 deps = ["OpenSpecFun_jll"]
 git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
 version = "0.10.3"
 [[StaticArrays]]
 deps = ["LinearAlgebra", "Random", "Statistics"]
 git-tree-sha1 = "016d1e1a00fabc556473b07161da3d39726ded35"
 uuid = "90137ffa-7385-5640-81b9-e52037218182"
 version = "0.12.4"
 [[Statistics]]
 deps = ["LinearAlgebra", "SparseArrays"]
 uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 [[StructTypes]]
 deps = ["Dates", "UUIDs"]
 git-tree-sha1 = "1ed04f622a39d2e5a6747c3a70be040c00333933"
 uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
 version = "1.1.0"
 [[TableTraits]]
 deps = ["IteratorInterfaceExtensions"]
 git-tree-sha1 = "b1ad568ba658d8cbb3b892ed5380a6f3e781a81e"
 uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
 version = "1.0.0"
 [[Tables]]
 deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
 git-tree-sha1 = "b7f762e9820b7fab47544c36f26f54ac59cf8abf"
 uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 version = "1.0.5"
 [[Test]]
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 [[TranscodingStreams]]
 deps = ["Random", "Test"]
 git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
 uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
 version = "0.9.5"
 [[UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 [[Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 [[Zlib_jll]]
 deps = ["Libdl", "Pkg"]
 git-tree-sha1 = "fdd89e5ab270ea0f2a0174bd9093e557d06d4bfa"
 uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
 version = "1.2.11+16"
--- a/Project.toml
+++ b/Project.toml
@@ -1,14 +1,16 @@
 name = "RELOG"
 uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
 authors = ["Alinson S Xavier <axavier@anl.gov>"]
-version = "0.4.0"
+version = "0.5.2"
 [deps]
 CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
 Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
@@ -16,23 +18,29 @@ 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"
 Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
 [compat]
-CSV = "0.7"
+CRC = "4"
-Cbc = "0.6"
+CSV = "0.10"
-Clp = "0.8"
+Cbc = "1"
-DataFrames = "0.21"
+Clp = "1"
-DataStructures = "0.17"
+DataFrames = "1"
 DataStructures = "0.18"
 GZip = "0.5"
-Geodesy = "0.5"
+Geodesy = "1"
 JSON = "0.21"
-JSONSchema = "0.2"
+JSONSchema = "1"
-JuMP = "0.21"
+JuMP = "1"
-MathOptInterface = "0.9"
+MathOptInterface = "1"
-PackageCompiler = "1"
+OrderedCollections = "1"
-ProgressBars = "0.6"
+ProgressBars = "1"
 Shapefile = "0.8"
 ZipFile = "0.10"
 julia = "1"
--- a/README.md
+++ b/README.md
@@ -1,21 +1,35 @@
-RELOG: Reverse Logistics Optimization
+<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/0.4/images/ex_transportation.png" width="1000px"/>
+
 <img src="https://anl-ceeesa.github.io/RELOG/0.5/assets/ex_transportation.png" width="1000px"/>
 ### Documentation
-  * [Usage](https://anl-ceeesa.github.io/RELOG/0.4/usage)
+  * [Usage](https://anl-ceeesa.github.io/RELOG/0.5/usage)
-  * [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.4/format)
+  * [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.5/format)
-  * [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.4/reports)
+  * [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.5/reports)
-  * [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.4/model)
+  * [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.5/model)
 ### Authors
-* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
+* **Alinson S. Xavier** <<axavier@anl.gov>>
-* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
+* **Nwike Iloeje** <<ciloeje@anl.gov>>
 * **John Atkins**
 * **Kyle Sun**
 ### License
--- a/deps/formatter/Project.toml
+++ b/deps/formatter/Project.toml
@@ -0,0 +1,5 @@
 [deps]
 JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
 [compat]
 JuliaFormatter = "0.14.4"
--- a/deps/formatter/format.jl
+++ b/deps/formatter/format.jl
@@ -0,0 +1,8 @@
 using JuliaFormatter
 format(
    [
        "../../src", 
        "../../test",
    ],
    verbose=true,
 )
--- a/docs/Project.toml
+++ b/docs/Project.toml
@@ -0,0 +1,4 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 RELOG = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -0,0 +1,19 @@
 using Documenter, RELOG
 function make()
    makedocs(
        sitename="RELOG",
        pages=[
            "Home" => "index.md",
            "usage.md",
            "format.md",
            "reports.md",
            "model.md",
        ],
        format = Documenter.HTML(
            assets=["assets/custom.css"],
        )
    )
 end
 make()
--- a/docs/src/assets/custom.css
+++ b/docs/src/assets/custom.css
@@ -0,0 +1,36 @@
@media screen and (min-width: 1056px) {
    #documenter .docs-main {
      max-width: 65rem !important;
    }
 }
 tbody, thead, pre {
    border: 1px solid rgba(0, 0, 0, 0.25);
 }
 table td, th {
    padding: 8px;
 }
 table p {
    margin-bottom: 0;
 }
 table td code {
    white-space: nowrap;
 }
 table tr,
 table th {
    border-bottom: 1px solid rgba(0, 0, 0, 0.1);
 }
 table tr:last-child {
    border-bottom: 0;
 }
 code {
    background-color: transparent;
    color: rgb(232, 62, 140);
 }
--- a/docs/src/assets/ex_amount_produced.png
+++ b/docs/src/assets/ex_amount_produced.png
--- a/docs/src/assets/ex_emissions.png
+++ b/docs/src/assets/ex_emissions.png
--- a/docs/src/assets/ex_plant_cost_per_year.png
+++ b/docs/src/assets/ex_plant_cost_per_year.png
--- a/docs/src/assets/ex_plant_locations.png
+++ b/docs/src/assets/ex_plant_locations.png
--- a/docs/src/assets/ex_transportation.png
+++ b/docs/src/assets/ex_transportation.png
--- a/docs/src/assets/ex_transportation_amount_distance.png
+++ b/docs/src/assets/ex_transportation_amount_distance.png
--- a/docs/src/assets/ex_transportation_emissions.png
+++ b/docs/src/assets/ex_transportation_emissions.png
--- a/docs/src/format.md
+++ b/docs/src/format.md
@@ -11,7 +11,7 @@ RELOG accepts as input a JSON file with three sections: `parameters`, `products`
 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. |
@@ -31,7 +31,7 @@ The **parameters** section describes details about the simulation itself.
 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.
@@ -40,7 +40,7 @@ The **products** section describes all products and subproducts in the simulatio
 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.
@@ -93,7 +93,7 @@ Each product may have some amount available at the beginning of each time period
 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.
@@ -107,12 +107,20 @@ Each type of plant is associated with a set of potential locations where it can
 | `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.
-| `capacities (tonne)`         | A dictionary describing what plant sizes are allowed, and their characteristics.
+| `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.
@@ -120,7 +128,7 @@ The keys in the `disposal` dictionary should be the names of the products. The v
 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 ($/tonne)`  | The cost that the plant incurs to process each tonne of input. Must be a time series.
@@ -151,11 +159,15 @@ The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes
                            "limit (tonne)": [1.0, 1.0]
                        }
                    },
                    "storage": {
                        "cost ($/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 ($/tonne)": [5.0, 5.2]
+                            "variable operating cost ($/tonne)": [5.0, 5.2],
                        },
                        "500": {
                            "opening cost ($)": [750, 760],
@@ -170,6 +182,38 @@ The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes
 }
 ```
 ### Geographic database
 Instead of specifying locations using latitudes and longitudes, it is also possible to specify them using unique identifiers, such as the name of a US state, or the county FIPS code. This works anywhere `latitude (deg)` and `longitude (deg)` are expected. For example, instead of:
 ```json
 {
    "initial amounts": {
        "C1": {
            "latitude (deg)": 37.27182,
            "longitude (deg)": -119.2704,
            "amount (tonne)": [934.56, 934.56]
        },
    }
 }
 ```
 is is possible to write:
 ```json
 {
    "initial amounts": {
        "C1": {
            "location": "us-state:CA",
            "amount (tonne)": [934.56, 934.56]
        },
    }
 }
 ```
 Location names follow the format `db:id`, where `db` is the name of the database and `id` is the identifier for a specific location. RELOG currently includes the following databases:
 Database | Description | Examples
 :--------|:------------|:---------
 `us-state`| List of states of the United States. | `us-state:IL` (State of Illinois)
 `2018-us-county` | List of United States counties, as of 2018. IDs are 5-digit FIPS codes. | `2018-us-county:17043` (DuPage county in Illinois)
 ### Current limitations
 * Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation.
@@ -180,4 +224,3 @@ The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes
 ## Output Data Format (JSON)
 To be documented.
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -1,25 +1,29 @@
 # 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"/>
+```@raw html
 <center>
   <img src="assets/ex_transportation.png" width="1000px"/>
 </center>
 ```
 ### Table of Contents
-  * [Usage](usage.md)
+  ```@contents
-  * [Input and Output Data Formats](format.md)
+Pages = ["usage.md", "format.md", "reports.md", "model.md"]
-  * [Simplified Solution Reports](reports.md)
+Depth = 3
-  * [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>>
+* **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
-* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
+* **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>
 ### License
--- a/docs/src/model.md
+++ b/docs/src/model.md
@@ -0,0 +1,196 @@
 # Optimization Model
 In this page, we describe the precise mathematical optimization model used by RELOG to find the optimal logistics plan. This model is a variation of the classical Facility Location Problem, which has been widely studied in the operations research literature. To simplify the exposition, we present the simplified case where there is only one type of plant.
 ## Mathematical Description
 ### Sets
 Symbol | Description
 :-------|:------------
 $L$ | Set of locations holding the original material to be recycled
 $M$ | Set of materials recovered during the reverse manufacturing process
 $P$ | Set of potential plants to open
 $T = \{ 1, \ldots, t^{max} \}$ | Set of time periods
 ### Constants
 #### Plants
 Symbol | Description | Unit
 :-------|:------------|:---
 $c^\text{disp}_{pmt}$ | Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ | \$/tonne/km
 $c^\text{exp}_{pt}$ | Cost of adding one tonne of capacity to plant $p$ at time $t$ | \$/tonne
 $c^\text{open}_{pt}$ | Cost of opening plant $p$ at time $t$, at minimum capacity | $
 $c^\text{f-base}_{pt}$ | Fixed cost of keeping plant $p$ open during time period $t$ | $
 $c^\text{f-exp}_{pt}$ | Increase in fixed cost for each additional tonne of capacity | \$/tonne
 $c^\text{var}_{pt}$ | Variable cost of processing one tonne of input at plant $p$ at time $t$ | \$/tonne
 $c^\text{store}_{pt}$ | Cost of storing one tonne of original material at plant $p$ at time $t$ | \$/tonne
 $m^\text{min}_p$ | Minimum capacity of plant $p$ | tonne
 $m^\text{max}_p$ | Maximum capacity of plant $p$ | tonne
 $m^\text{disp}_{pmt}$ | Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ | tonne
 $m^\text{store}_p$ | Maximum amount of original material that plant $p$ can store for later processing. | tonne
 #### Products
 Symbol | Description | Unit
 :-------|:------------|:---
 $\alpha_{pm}$ | Amount of material $m$ recovered by plant $t$ for each tonne of original material | tonne/tonne
 $m^\text{initial}_{lt}$ | Amount of original material to be recycled at location $l$ during time $t$ | tonne
 #### Transportation
 Symbol | Description | Unit
 :-------|:------------|:---
 $c^\text{tr}_{t}$ | Transportation cost during time $t$ | \$/tonne/km
 $d_{lp}$ | Distance between plant $p$ and location $l$ | km
 ### Decision variables
 Symbol | Description | Unit
 :-------|:------------|:---
 $q_{mpt}$ | Amount of material $m$ recovered by plant $p$ during time $t$ | tonne
 $u_{pt}$ | Binary variable that equals 1 if plant $p$ starts operating at time $t$ | Boolean
 $w_{pt}$ | Extra capacity (amount above the minimum) added to plant $p$ during time $t$ | tonne
 $x_{pt}$ | Binary variable that equals 1 if plant $p$ is operational at time $t$ | Boolean
 $y_{lpt}$ | Amount of product sent from location $l$ to plant $p$ during time $t$ | tonne
 $z^{\text{disp}}_{mpt}$ | Amount of material $m$ disposed of by plant $p$ during time $t$ | tonne
 $z^{\text{store}}_{pt}$ | Amount of original material in storage at plant $p$ by the end of time period $t$ | tonne
 $z^{\text{proc}}_{mpt}$ | Amount of original material processed by plant $p$ during time period $t$ | tonne
 ### Objective function
 RELOG minimizes the overall capital, production and transportation costs:
 ```math
 \begin{align*}
    \text{minimize} \;\; &
        \sum_{t \in T} \sum_{p \in P} \left[
                c^\text{open}_{pt} u_{pt} +
                c^\text{f-base}_{pt} x_{pt} +
                \sum_{i=1}^t c^\text{f-exp}_{pt} w_{pi} +
                c^{\text{exp}}_{pt} w_{pt}
            \right] + \\
    & 
        \sum_{t \in T} \sum_{p \in P} \left[
                c^{\text{store}}_{pt} z^{\text{store}}_{pt} +
                c^{\text{proc}}_{pt} z^{\text{proc}}_{pt}
            \right] + \\
    &
        \sum_{t \in T} \sum_{l \in L} \sum_{p \in P}
            c^{\text{tr}}_t d_{lp} y_{lpt}
        \\
    &
        \sum_{t \in T} \sum_{p \in P} \sum_{m \in M} c^{\text{disp}}_{pmt} z_{pmt}
 \end{align*}
 ```
 In the first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period.
 In the second line, we have storage and variable processing costs.
 In the third line, we have transportation costs.
 In the fourth line, we have the disposal costs.
 ### Constraints
 * All original materials must be sent to a plant:
 ```math
 \begin{align*}
    & \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt} 
        & \forall l \in L, t \in T
 \end{align*}
 ```
 * Amount received equals amount processed plus stored. Furthermore, all original material should be processed by the end of the simulation.
 ```math
 \begin{align*}
    & \sum_{l \in L} y_{lpt} + z^{\text{store}}_{p,t-1}
        = z^{\text{proc}}_{pt} + z^{\text{store}}_{p,t}
        & \forall p \in P, t \in T \\
    & z^{\text{store}}_{p,0} = 0
        & \forall p \in P \\
    & z^{\text{store}}_{p,t^{\max}} = 0
        & \forall p \in P
 \end{align*}
 ```
 * Plants have a limited processing capacity. Furthermore, if a plant is closed, it has zero processing capacity:
 ```math
 \begin{align*}
    & z^{\text{proc}}_{pt} \leq m^\text{min}_p x_p + \sum_{i=1}^t w_p
        & \forall p \in P, t \in T
 \end{align*}
 ```
 * Plants have limited storage capacity. Furthermore, if a plant is closed, is has zero storage capacity:
 ```math
 \begin{align*}
    & z^{\text{store}}_{pt} \leq m^\text{store}_p x_p
        & \forall p \in P, t \in T
 \end{align*}
 ```
 * Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded:
 ```math
 \begin{align*}
    & \sum_{i=1}^t w_p \leq m^\text{max}_p x_p
        & \forall p \in P, t \in T
 \end{align*}
 ```
 * Amount of recovered material is proportional to amount processed: 
 ```math
 \begin{align*}
    & q_{mpt} = \alpha_{pm} z^{\text{proc}}_{pt}
        & \forall m \in M, p \in P, t \in T
 \end{align*}
 ```
 * Because we only consider a single type of plant, all recovered material must be immediately disposed of. In RELOG's full model, recovered materials may be sent to another plant for further processing.
 ```math
 \begin{align*}
    & q_{mpt} = z_{mpt}
        & \forall m \in M, p \in P, t \in T
 \end{align*}
 ```
 * A plant is operational at time $t$ if it was operational at time $t-1$ or it was built at time $t$. This constraint also prevents a plant from being built multiple times.
 ```math
 \begin{align*}
    & x_{pt} = x_{p,t-1} + u_{pt}
        & \forall p \in P, t \in T \setminus \{1\} \\
    & x_{p,1} = u_{p,1}
        & \forall p \in P
 \end{align*}
 ```
 * Variable bounds:
 ```math
 \begin{align*}
    & q_{mpt} \geq 0
        & \forall m \in M, p \in P, t \in T \\
    & u_{pt} \in \{0,1\}
        & \forall p \in P, t \in T \\
    & w_{pt} \geq 0
        & \forall p \in P, t \in T \\
    & x_{pt} \in \{0,1\}
        & \forall p \in P, t \in T \\
    & y_{lpt} \geq 0
        & \forall l \in L, p \in P, t \in T \\
    & z^{\text{store}}_{pt} \geq 0
        & p \in P, t \in T \\
    & z^{\text{disp}}_{mpt}, z^{\text{proc}}_{mpt} \geq 0
        & \forall m \in M, p \in P, t \in T
 \end{align*}
 ```
--- a/docs/src/reports.md
+++ b/docs/src/reports.md
@@ -6,25 +6,26 @@ In this page, we also illustrate what types of charts and visualizations can be
 ## Plants report
-Report showing plant costs, capacities, energy expenditure and utilization factors.
+Report showing plant costs, capacities, energy expenditure and utilization factors. Generated by `RELOG.write_plants_report(solution, filename)`.
 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.
+| `year` | What year this row corresponds to. This reports includes one row for each year.
 | `latitude (deg)` | Latitude of the plant.
 | `longitude (deg)` | Longitude of the plant.
 | `capacity (tonne)`  | Capacity of the plant at this point in time.
-| `amount processed (tonne)` | Amount of input material received by the plant this year.
+| `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 for processing the input material 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.
@@ -44,7 +45,9 @@ sns.barplot(x="year",
                     .reset_index());
 ```
-<img src="../images/ex_plant_cost_per_year.png" width="500px"/>
+```@raw html
 <img src="../assets/ex_plant_cost_per_year.png" width="500px"/>
 ```
 * Map showing plant locations (in Python):
 ```python
@@ -64,22 +67,21 @@ points = gp.points_from_xy(data["longitude (deg)"],
 gp.GeoDataFrame(data, geometry=points).plot(ax=ax);
 ```
-<img src="../images/ex_plant_locations.png" width="1000px"/>
+```@raw html
-
+<img src="../assets/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.
+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)`.
 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.
+| `year`  | What year this row corresponds to. This reports includes one row for each year.
-| `product`  | Product being produced.
+| `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.
@@ -102,17 +104,17 @@ sns.barplot(x="amount produced (tonne)",
                     .reset_index());
 ```
-<img src="../images/ex_amount_produced.png" width="500px"/>
+```@raw html
 <img src="../assets/ex_amount_produced.png" width="500px"/>
 ```
 ## Plant emissions report
-Report showing amount of emissions produced by each plant.
+Report showing amount of emissions produced by each plant. Generated by `RELOG.write_plant_emissions_report(solution, filename)`.
 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.
@@ -136,17 +138,32 @@ sns.barplot(x="plant type",
                     .reset_index());
 ```
-<img src="../images/ex_emissions.png" width="500px"/>
+```@raw html
 <img src="../assets/ex_emissions.png" width="500px"/>
 ```
 ## Products report
 Report showing primary product amounts, locations and marginal costs. Generated by `RELOG.write_products_report(solution, filename)`.
 | Column                                | Description
 |:--------------------------------------|:---------------|
 | `product name`  | Product name.
 | `location name`  | Name of the collection center.
 | `latitude (deg)` | Latitude of the collection center.
 | `longitude (deg)` | Longitude of the collection center.
 | `year`  | What year this row corresponds to. This reports includes one row for each year.
 | `amount (tonne)` | Amount of product available at this collection center.
 | `marginal cost ($/tonne)` | Cost to process one additional tonne of this product coming from this collection center.
 ## 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.
+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)`.
 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.
@@ -180,7 +197,9 @@ sns.barplot(x="product",
                     .reset_index());
 ```
-<img src="../images/ex_transportation_amount_distance.png" width="500px"/>
+```@raw html
 <img src="../assets/ex_transportation_amount_distance.png" width="500px"/>
 ```
 * Map of transportation lines (in Python):
@@ -223,17 +242,17 @@ gp.GeoDataFrame(data, geometry=points).plot(ax=ax,
                                            markersize=50);
 ```
-<img src="../images/ex_transportation.png" width="1000px"/>
+```@raw html
 <img src="../assets/ex_transportation.png" width="1000px"/>
 ```
 ## Transportation emissions report
-Report showing emissions for each trip between initial locations and plants, and between pairs of plants.
+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)`.
 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.
@@ -267,4 +286,6 @@ sns.barplot(x="emission type",
                     .reset_index());
 ```
-<img src="../images/ex_transportation_emissions.png" width="500px"/>
+```@raw html
 <img src="../assets/ex_transportation_emissions.png" width="500px"/>
 ```
--- a/docs/src/usage.md
+++ b/docs/src/usage.md
@@ -0,0 +1,139 @@
 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 1.8 and may not be compatible with newer versions. After Julia is installed, launch the Julia console, then run:
 ```julia
 using Pkg
 Pkg.add(name="RELOG", version="0.5")
 ```
 After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed:
 ```julia
 Pkg.test("RELOG")
 ```
 ## 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, but keeps some of the previous decisions fixed. More precisely, given an optimal solution produced by RELOG and a new input file describing the new scenario, the `resolve` method reoptimizes the supply chain 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, model_avg = RELOG.solve("input_avg.json", return_model=true)
 # 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(model_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,
 )
 ```
--- a/instances/s2.json
+++ b/instances/s2.json
@@ -0,0 +1,347 @@
 {
    "parameters": {
        "time horizon (years)": 2
    },
    "products": {
        "P1": {
            "transportation cost ($/km/tonne)": [
                0.015,
                0.015
            ],
            "transportation energy (J/km/tonne)": [
                0.12,
                0.11
            ],
            "transportation emissions (tonne/km/tonne)": {
                "CO2": [
                    0.052,
                    0.050
                ],
                "CH4": [
                    0.003,
                    0.002
                ]
            },
            "initial amounts": {
                "C1": {
                    "location": "2018-us-county:17043",
                    "amount (tonne)": [
                        934.56,
                        934.56
                    ]
                },
                "C2": {
                    "latitude (deg)": 7.0,
                    "longitude (deg)": 19.0,
                    "amount (tonne)": [
                        198.95,
                        198.95
                    ]
                },
                "C3": {
                    "latitude (deg)": 84.0,
                    "longitude (deg)": 76.0,
                    "amount (tonne)": [
                        212.97,
                        212.97
                    ]
                },
                "C4": {
                    "latitude (deg)": 21.0,
                    "longitude (deg)": 16.0,
                    "amount (tonne)": [
                        352.19,
                        352.19
                    ]
                },
                "C5": {
                    "latitude (deg)": 32.0,
                    "longitude (deg)": 92.0,
                    "amount (tonne)": [
                        510.33,
                        510.33
                    ]
                },
                "C6": {
                    "latitude (deg)": 14.0,
                    "longitude (deg)": 62.0,
                    "amount (tonne)": [
                        471.66,
                        471.66
                    ]
                },
                "C7": {
                    "latitude (deg)": 30.0,
                    "longitude (deg)": 83.0,
                    "amount (tonne)": [
                        785.21,
                        785.21
                    ]
                },
                "C8": {
                    "latitude (deg)": 35.0,
                    "longitude (deg)": 40.0,
                    "amount (tonne)": [
                        706.17,
                        706.17
                    ]
                },
                "C9": {
                    "latitude (deg)": 74.0,
                    "longitude (deg)": 52.0,
                    "amount (tonne)": [
                        30.08,
                        30.08
                    ]
                },
                "C10": {
                    "latitude (deg)": 22.0,
                    "longitude (deg)": 54.0,
                    "amount (tonne)": [
                        536.52,
                        536.52
                    ]
                }
            }
        },
        "P2": {
            "transportation cost ($/km/tonne)": [
                0.02,
                0.02
            ]
        },
        "P3": {
            "transportation cost ($/km/tonne)": [
                0.0125,
                0.0125
            ]
        },
        "P4": {
            "transportation cost ($/km/tonne)": [
                0.0175,
                0.0175
            ]
        }
    },
    "plants": {
        "F1": {
            "input": "P1",
            "outputs (tonne/tonne)": {
                "P2": 0.2,
                "P3": 0.5
            },
            "energy (GJ/tonne)": [
                0.12,
                0.11
            ],
            "emissions (tonne/tonne)": {
                "CO2": [
                    0.052,
                    0.050
                ],
                "CH4": [
                    0.003,
                    0.002
                ]
            },
            "locations": {
                "L1": {
                    "latitude (deg)": 0.0,
                    "longitude (deg)": 0.0,
                    "disposal": {
                        "P2": {
                            "cost ($/tonne)": [
                                -10.0,
                                -10.0
                            ],
                            "limit (tonne)": [
                                1.0,
                                1.0
                            ]
                        },
                        "P3": {
                            "cost ($/tonne)": [
                                -10.0,
                                -10.0
                            ],
                            "limit (tonne)": [
                                1.0,
                                1.0
                            ]
                        }
                    },
                    "capacities (tonne)": {
                        "250.0": {
                            "opening cost ($)": [
                                500.0,
                                500.0
                            ],
                            "fixed operating cost ($)": [
                                30.0,
                                30.0
                            ],
                            "variable operating cost ($/tonne)": [
                                30.0,
                                30.0
                            ]
                        },
                        "1000.0": {
                            "opening cost ($)": [
                                1250.0,
                                1250.0
                            ],
                            "fixed operating cost ($)": [
                                30.0,
                                30.0
                            ],
                            "variable operating cost ($/tonne)": [
                                30.0,
                                30.0
                            ]
                        }
                    }
                },
                "L2": {
                    "location": "2018-us-county:17043",
                    "capacities (tonne)": {
                        "0.0": {
                            "opening cost ($)": [
                                1000,
                                1000
                            ],
                            "fixed operating cost ($)": [
                                50.0,
                                50.0
                            ],
                            "variable operating cost ($/tonne)": [
                                50.0,
                                50.0
                            ]
                        },
                        "10000.0": {
                            "opening cost ($)": [
                                10000,
                                10000
                            ],
                            "fixed operating cost ($)": [
                                50.0,
                                50.0
                            ],
                            "variable operating cost ($/tonne)": [
                                50.0,
                                50.0
                            ]
                        }
                    }
                }
            }
        },
        "F2": {
            "input": "P2",
            "outputs (tonne/tonne)": {
                "P3": 0.05,
                "P4": 0.80
            },
            "locations": {
                "L3": {
                    "latitude (deg)": 25.0,
                    "longitude (deg)": 65.0,
                    "disposal": {
                        "P3": {
                            "cost ($/tonne)": [
                                100.0,
                                100.0
                            ]
                        }
                    },
                    "capacities (tonne)": {
                        "1000.0": {
                            "opening cost ($)": [
                                3000,
                                3000
                            ],
                            "fixed operating cost ($)": [
                                50.0,
                                50.0
                            ],
                            "variable operating cost ($/tonne)": [
                                50.0,
                                50.0
                            ]
                        }
                    }
                },
                "L4": {
                    "latitude (deg)": 0.75,
                    "longitude (deg)": 0.20,
                    "capacities (tonne)": {
                        "10000": {
                            "opening cost ($)": [
                                3000,
                                3000
                            ],
                            "fixed operating cost ($)": [
                                50.0,
                                50.0
                            ],
                            "variable operating cost ($/tonne)": [
                                50.0,
                                50.0
                            ]
                        }
                    }
                }
            }
        },
        "F3": {
            "input": "P4",
            "locations": {
                "L5": {
                    "latitude (deg)": 100.0,
                    "longitude (deg)": 100.0,
                    "capacities (tonne)": {
                        "15000": {
                            "opening cost ($)": [
                                0.0,
                                0.0
                            ],
                            "fixed operating cost ($)": [
                                0.0,
                                0.0
                            ],
                            "variable operating cost ($/tonne)": [
                                -15.0,
                                -15.0
                            ]
                        }
                    }
                }
            }
        },
        "F4": {
            "input": "P3",
            "locations": {
                "L6": {
                    "latitude (deg)": 50.0,
                    "longitude (deg)": 50.0,
                    "capacities (tonne)": {
                        "10000": {
                            "opening cost ($)": [
                                0.0,
                                0.0
                            ],
                            "fixed operating cost ($)": [
                                0.0,
                                0.0
                            ],
                            "variable operating cost ($/tonne)": [
                                -15.0,
                                -15.0
                            ]
                        }
                    }
                }
            }
        }
    }
 }
--- a/75
+++ b/75
@@ -0,0 +1,75 @@
 #!/bin/bash
 # UnitCommitment.jl: Optimization Package for Security-Constrained Unit Commitment
 # Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 if [ ! -e Project.toml ]; then
    echo "juliaw: Project.toml not found"
    exit 1
 fi
 if [ ! -e Manifest.toml ]; then
    julia --project=. -e 'using Pkg; Pkg.instantiate()' || exit 1
 fi
 if [ ! -e build/sysimage.so -o Project.toml -nt build/sysimage.so ]; then
    echo "juliaw: rebuilding system image..."
    # Generate temporary project folder
    rm -rf $HOME/.juliaw
    mkdir -p $HOME/.juliaw/src
    cp Project.toml Manifest.toml $HOME/.juliaw
    NAME=$(julia -e 'using TOML; toml = TOML.parsefile("Project.toml"); "name" in keys(toml) && print(toml["name"])')
    if [ ! -z $NAME ]; then
        cat > $HOME/.juliaw/src/$NAME.jl << EOF
 module $NAME
 end
 EOF
    fi
    # Add PackageCompiler dependencies to temporary project
    julia --project=$HOME/.juliaw -e 'using Pkg; Pkg.add(["PackageCompiler", "TOML", "Logging"])'
    # Generate system image scripts
    cat > $HOME/.juliaw/sysimage.jl << EOF
 using PackageCompiler
 using TOML
 using Logging
 Logging.disable_logging(Logging.Info)
 mkpath("$PWD/build")
 println("juliaw: generating precompilation statements...")
 run(\`julia --project="$PWD" --trace-compile="$PWD"/build/precompile.jl \$(ARGS)\`)
 println("juliaw: finding dependencies...")
 project = TOML.parsefile("Project.toml")
 manifest = TOML.parsefile("Manifest.toml")
 deps = Symbol[]
 for dep in keys(project["deps"])
    if dep in keys(manifest)
 	# Up to Julia 1.6
        dep_entry = manifest[dep][1]
    else
        # Julia 1.7+
        dep_entry = manifest["deps"][dep][1]
    end
    if "path" in keys(dep_entry)
        println("  - \$(dep) [skip]")
    else
        println("  - \$(dep)")
        push!(deps, Symbol(dep))
    end
 end
 println("juliaw: building system image...")
 create_sysimage(
    deps,
    precompile_statements_file = "$PWD/build/precompile.jl",
    sysimage_path = "$PWD/build/sysimage.so",
 )
 EOF
    julia --project=$HOME/.juliaw $HOME/.juliaw/sysimage.jl $* 
 else
    julia --project=. --sysimage build/sysimage.so $*
 fi
--- a/src/RELOG.jl
+++ b/src/RELOG.jl
@@ -3,9 +3,26 @@
 # Released under the modified BSD license. See COPYING.md for more details.
 module RELOG
-    include("dotdict.jl")
+
-    include("instance.jl")
+include("instance/structs.jl")
-    include("graph.jl")
+
-    include("model.jl")
+include("graph/structs.jl")
-    include("reports.jl")
+
 include("graph/build.jl")
 include("graph/csv.jl")
 include("instance/compress.jl")
 include("instance/geodb.jl")
 include("instance/parse.jl")
 include("instance/validate.jl")
 include("model/build.jl")
 include("model/getsol.jl")
 include("model/solve.jl")
 include("model/resolve.jl")
 include("reports/plant_emissions.jl")
 include("reports/plant_outputs.jl")
 include("reports/plants.jl")
 include("reports/products.jl")
 include("reports/tr_emissions.jl")
 include("reports/tr.jl")
 include("reports/write.jl")
 end
--- a/src/docs/css/custom.css
+++ b/src/docs/css/custom.css
@@ -1,28 +0,0 @@
 .navbar-default {
  border-bottom: 0px;
  background-color: #fff;
  box-shadow: 0px 0px 15px rgba(0, 0, 0, 0.2);
 }
 a, .navbar-default a {
  color: #06a !important;
  font-weight: normal;
 }
 .disabled > a {
  color: #999 !important;
 }
 .navbar-default a:hover,
 .navbar-default .active,
 .active > a {
  background-color: #f0f0f0 !important;
 }
 .icon-bar {
  background-color: #666 !important;
 }
 .navbar-collapse {
  border-color: #fff !important;
 }
--- a/src/docs/images/ex_transportation_amount_distance.png
+++ b/src/docs/images/ex_transportation_amount_distance.png
--- a/src/docs/js/mathjax.js
+++ b/src/docs/js/mathjax.js
@@ -1,8 +0,0 @@
 MathJax.Hub.Config({
  "tex2jax": { inlineMath: [ [ '$', '$' ] ] }
 });
 MathJax.Hub.Config({
  config: ["MMLorHTML.js"],
  jax: ["input/TeX", "output/HTML-CSS", "output/NativeMML"],
  extensions: ["MathMenu.js", "MathZoom.js"]
 });
--- a/src/docs/model.md
+++ b/src/docs/model.md
@@ -1,178 +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`)
 * $m^\text{min}_p$ - 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`)
 **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`)
 * $z_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ (`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_{l \in L} \sum_{p \in P} \left[
            c^{\text{tr}}_t d_{lp} + c^{\text{var}}_{pt}
        \right]  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 the transportation costs and the variable operating costs.
 In the third 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}
 * Plants have a limited capacity:
 \begin{align}
    & \sum_{l \in L} y_{lpt} \leq m^\text{min}_p x_p + \sum_{i=1}^t w_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 the plant input: 
 \begin{align}
    & q_{mpt} = \alpha_{pm} \sum_{l \in L} y_{lpt}
        & \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 \\
    & m^\text{disp}_{mpt} \geq z_{mpt} \geq 0
        & \forall m \in M, p \in P, t \in T
 \end{align}
 ### Complete optimization model
 \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_{l \in L} \sum_{p \in P} \left[
            c^{\text{tr}}_t d_{lp} + c^{\text{var}}_{pt}
        \right]  y_{lpt} + \\
    &
        \sum_{t \in T} \sum_{p \in P} \sum_{m \in M} c^{\text{disp}}_{pmt} z_{pmt} \\
    \text{subject to } & \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt} 
        & \forall l \in L, t \in T \\
    & \sum_{l \in L} y_{lpt} \leq m^\text{min}_p x_p + \sum_{i=1}^t w_p
        & \forall p \in P, t \in T \\
    & \sum_{i=1}^t w_p \leq m^\text{max}_p x_p
        & \forall p \in P, t \in T \\
    & q_{mpt} = \alpha_{pm} \sum_{l \in L} y_{lpt}
        & \forall m \in M, p \in P, t \in T \\
    & q_{mpt} = z_{mpt}
        & \forall m \in M, p \in P, t \in T \\
    & 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 \\
    & 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 \\
    & m^\text{disp}_{mpt} \geq z_{mpt} \geq 0
        & \forall m \in M, p \in P, t \in T
 \end{align*}
--- a/src/docs/usage.md
+++ b/src/docs/usage.md
@@ -1,81 +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.
 * 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.
 * All products that are initially available must be sent to a plant for processing. All products that are 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. Advanced options
 ### 4.1 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)
 ```
--- a/src/dotdict.jl
+++ b/src/dotdict.jl
@@ -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
--- a/src/graph/build.jl
+++ b/src/graph/build.jl
@@ -4,43 +4,12 @@
 using Geodesy
-
+function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64
-abstract type Node
+    x = LLA(source_lat, source_lon, 0.0)
    y = LLA(dest_lat, dest_lon, 0.0)
    return round(euclidean_distance(x, y) / 1000.0, digits = 2)
 end
 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
@@ -48,10 +17,11 @@ function build_graph(instance::Instance)::Graph
    plant_shipping_nodes = ShippingNode[]
    collection_shipping_nodes = ShippingNode[]
-    process_nodes_by_input_product = Dict(product => ProcessNode[]
+    name_to_process_node_map = Dict{Tuple{AbstractString,AbstractString},ProcessNode}()
-                                          for product in instance.products)
+
-    shipping_nodes_by_plant = Dict(plant => []
+    process_nodes_by_input_product =
-                                   for plant in instance.plants)
+        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
@@ -67,6 +37,8 @@ function build_graph(instance::Instance)::Graph
        push!(process_nodes, pn)
        push!(process_nodes_by_input_product[plant.input], pn)
        name_to_process_node_map[(plant.plant_name, plant.location_name)] = pn
        for product in keys(plant.output)
            sn = ShippingNode(next_index, plant, product, [], [])
            next_index += 1
@@ -78,10 +50,12 @@ function build_graph(instance::Instance)::Graph
    # 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,
+            distance = calculate_distance(
-                                          source.location.longitude,
+                source.location.latitude,
-                                          dest.location.latitude,
+                source.location.longitude,
-                                          dest.location.longitude)
+                dest.location.latitude,
                dest.location.longitude,
            )
            values = Dict("distance" => distance)
            arc = Arc(source, dest, values)
            push!(source.outgoing_arcs, arc)
@@ -103,24 +77,24 @@ function build_graph(instance::Instance)::Graph
        end
    end
-    return Graph(process_nodes, 
+    return Graph(
-                 plant_shipping_nodes,
+        process_nodes,
-                 collection_shipping_nodes,
+        plant_shipping_nodes,
-                 arcs)
+        collection_shipping_nodes,
        arcs,
        name_to_process_node_map,
    )
 end
-function to_csv(graph::Graph)
+function print_graph_stats(instance::Instance, graph::Graph)::Nothing
-    result = ""
+    @info @sprintf("    %12d time periods", instance.time)
-    for a in graph.arcs
+    @info @sprintf("    %12d process nodes", length(graph.process_nodes))
-        result *= "$(a.source.index),$(a.dest.index)\n"
+    @info @sprintf("    %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
-    end
+    @info @sprintf(
-    return result
+        "    %12d shipping nodes (collection)",
-end
+        length(graph.collection_shipping_nodes)
-
+    )
-
+    @info @sprintf("    %12d arcs", length(graph.arcs))
-function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64
+    return
    x = LLA(source_lat, source_lon, 0.0)
    y = LLA(dest_lat, dest_lon, 0.0)
    return round(distance(x, y) / 1000.0, digits=2)
 end
--- a/src/graph/csv.jl
+++ b/src/graph/csv.jl
@@ -0,0 +1,11 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 function to_csv(graph::Graph)
    result = ""
    for a in graph.arcs
        result *= "$(a.source.index),$(a.dest.index)\n"
    end
    return result
 end
--- a/src/graph/structs.jl
+++ b/src/graph/structs.jl
@@ -0,0 +1,44 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using Geodesy
 abstract type Node end
 mutable struct Arc
    source::Node
    dest::Node
    values::Dict{String,Float64}
 end
 mutable struct ProcessNode <: Node
    index::Int
    location::Plant
    incoming_arcs::Vector{Arc}
    outgoing_arcs::Vector{Arc}
 end
 mutable struct ShippingNode <: Node
    index::Int
    location::Union{Plant,CollectionCenter}
    product::Product
    incoming_arcs::Vector{Arc}
    outgoing_arcs::Vector{Arc}
 end
 mutable struct Graph
    process_nodes::Vector{ProcessNode}
    plant_shipping_nodes::Vector{ShippingNode}
    collection_shipping_nodes::Vector{ShippingNode}
    arcs::Vector{Arc}
    name_to_process_node_map::Dict{Tuple{AbstractString,AbstractString},ProcessNode}
 end
 function Base.show(io::IO, instance::Graph)
    print(io, "RELOG graph with ")
    print(io, "$(length(instance.process_nodes)) process nodes, ")
    print(io, "$(length(instance.plant_shipping_nodes)) plant shipping nodes, ")
    print(io, "$(length(instance.collection_shipping_nodes)) collection shipping nodes, ")
    print(io, "$(length(instance.arcs)) arcs")
 end
--- a/src/instance/compress.jl
+++ b/src/instance/compress.jl
@@ -0,0 +1,60 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataStructures
 using JSON
 using JSONSchema
 using Printf
 using Statistics
 """
    _compress(instance::Instance)
 Create a single-period instance from a multi-period one. Specifically,
 replaces every time-dependent attribute, such as initial_amounts,
 by a list with a single element, which is either a sum, an average,
 or something else that makes sense to that specific attribute.
 """
 function _compress(instance::Instance)::Instance
    T = instance.time
    compressed = deepcopy(instance)
    compressed.time = 1
    compressed.building_period = [1]
    # Compress products
    for p in compressed.products
        p.transportation_cost = [mean(p.transportation_cost)]
        p.transportation_energy = [mean(p.transportation_energy)]
        for (emission_name, emission_value) in p.transportation_emissions
            p.transportation_emissions[emission_name] = [mean(emission_value)]
        end
    end
    # Compress collection centers
    for c in compressed.collection_centers
        c.amount = [maximum(c.amount) * T]
    end
    # Compress plants
    for plant in compressed.plants
        plant.energy = [mean(plant.energy)]
        for (emission_name, emission_value) in plant.emissions
            plant.emissions[emission_name] = [mean(emission_value)]
        end
        for s in plant.sizes
            s.capacity *= T
            s.variable_operating_cost = [mean(s.variable_operating_cost)]
            s.opening_cost = [s.opening_cost[1]]
            s.fixed_operating_cost = [sum(s.fixed_operating_cost)]
        end
        for (prod_name, disp_limit) in plant.disposal_limit
            plant.disposal_limit[prod_name] = [sum(disp_limit)]
        end
        for (prod_name, disp_cost) in plant.disposal_cost
            plant.disposal_cost[prod_name] = [mean(disp_cost)]
        end
    end
    return compressed
 end
--- a/src/instance/geodb.jl
+++ b/src/instance/geodb.jl
@@ -0,0 +1,212 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using CRC
 using CSV
 using DataFrames
 using Shapefile
 using Statistics
 using ZipFile
 using ProgressBars
 using OrderedCollections
 import Downloads: download
 import Base: parse
 crc32 = crc(CRC_32)
 struct GeoPoint
    lat::Float64
    lon::Float64
 end
 struct GeoRegion
    centroid::GeoPoint
    population::Int
    GeoRegion(; centroid, population) = new(centroid, population)
 end
 DB_CACHE = Dict{String,Dict{String,GeoRegion}}()
 function centroid(geom::Shapefile.Polygon)::GeoPoint
    x_max, x_min, y_max, y_min = -Inf, Inf, -Inf, Inf
    for p in geom.points
        x_max = max(x_max, p.x)
        x_min = min(x_min, p.x)
        y_max = max(y_max, p.y)
        y_min = min(y_min, p.y)
    end
    x_center = (x_max + x_min) / 2.0
    y_center = (y_max + y_min) / 2.0
    return GeoPoint(round(y_center, digits = 5), round(x_center, digits = 5))
 end
 function _download_file(url, output, expected_crc32)::Nothing
    if isfile(output)
        return
    end
    mkpath(dirname(output))
    @info "Downloading: $url"
    fname = download(url)
    actual_crc32 = open(crc32, fname)
    expected_crc32 == actual_crc32 || error("CRC32 mismatch")
    cp(fname, output)
    return
 end
 function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
    if isfile(expected_output_file)
        return
    end
    mkpath(outputdir)
    @info "Downloading: $url"
    zip_filename = download(url)
    actual_crc32 = open(crc32, zip_filename)
    expected_crc32 == actual_crc32 || error("CRC32 mismatch")
    open(zip_filename) do zip_file
        zr = ZipFile.Reader(zip_file)
        for file in zr.files
            open(joinpath(outputdir, file.name), "w") do output_file
                write(output_file, read(file))
            end
        end
    end
    return
 end
 function _geodb_load_gov_census(;
    db_name,
    extract_cols,
    shp_crc32,
    shp_filename,
    shp_url,
    population_url,
    population_crc32,
    population_col,
    population_preprocess,
    population_join,
 )::Dict{String,GeoRegion}
    basedir = joinpath(dirname(@__FILE__), "..", "..", "data", db_name)
    csv_filename = "$basedir/locations.csv"
    if !isfile(csv_filename)
        # Download required files
        _download_zip(shp_url, basedir, joinpath(basedir, shp_filename), shp_crc32)
        _download_file(population_url, "$basedir/population.csv", population_crc32)
        # Read shapefile
        @info "Processing: $shp_filename"
        table = Shapefile.Table(joinpath(basedir, shp_filename))
        geoms = Shapefile.shapes(table)
        # Build empty dataframe
        df = DataFrame()
        cols = extract_cols(table, 1)
        for k in keys(cols)
            df[!, k] = []
        end
        df[!, "latitude"] = Float64[]
        df[!, "longitude"] = Float64[]
        # Add regions to dataframe
        for (i, geom) in tqdm(enumerate(geoms))
            c = centroid(geom)
            cols = extract_cols(table, i)
            push!(df, [values(cols)..., c.lat, c.lon])
        end
        sort!(df)
        # Join with population data
        population = DataFrame(CSV.File("$basedir/population.csv"))
        population_preprocess(population)
        population = population[:, [population_join, population_col]]
        rename!(population, population_col => "population")
        df = leftjoin(df, population, on = population_join)
        # Write output
        CSV.write(csv_filename, df)
    end
    if db_name ∉ keys(DB_CACHE)
        csv = CSV.File(csv_filename)
        DB_CACHE[db_name] = Dict(
            string(row.id) => GeoRegion(
                centroid = GeoPoint(row.latitude, row.longitude),
                population = (row.population === missing ? 0 : row.population),
            ) for row in csv
        )
    end
    return DB_CACHE[db_name]
 end
 # 2018 US counties
 # -----------------------------------------------------------------------------
 function _extract_cols_2018_us_county(
    table::Shapefile.Table,
    i::Int,
 )::OrderedDict{String,Any}
    return OrderedDict(
        "id" => table.STATEFP[i] * table.COUNTYFP[i],
        "statefp" => table.STATEFP[i],
        "countyfp" => table.COUNTYFP[i],
        "name" => table.NAME[i],
    )
 end
 function _population_preprocess_2018_us_county(df)
    df[!, "id"] = [@sprintf("%02d%03d", row.STATE, row.COUNTY) for row in eachrow(df)]
 end
 function _geodb_load_2018_us_county()::Dict{String,GeoRegion}
    return _geodb_load_gov_census(
        db_name = "2018-us-county",
        extract_cols = _extract_cols_2018_us_county,
        shp_crc32 = 0x83eaec6d,
        shp_filename = "cb_2018_us_county_500k.shp",
        shp_url = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_county_500k.zip",
        population_url = "https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/totals/co-est2019-alldata.csv",
        population_crc32 = 0xf85b0405,
        population_col = "POPESTIMATE2019",
        population_join = "id",
        population_preprocess = _population_preprocess_2018_us_county,
    )
 end
 # US States
 # -----------------------------------------------------------------------------
 function _extract_cols_us_state(table::Shapefile.Table, i::Int)::OrderedDict{String,Any}
    return OrderedDict(
        "id" => table.STUSPS[i],
        "statefp" => parse(Int, table.STATEFP[i]),
        "name" => table.NAME[i],
    )
 end
 function _population_preprocess_us_state(df)
    rename!(df, "STATE" => "statefp")
 end
 function _geodb_load_us_state()::Dict{String,GeoRegion}
    return _geodb_load_gov_census(
        db_name = "us-state",
        extract_cols = _extract_cols_us_state,
        shp_crc32 = 0x9469e5ca,
        shp_filename = "cb_2018_us_state_500k.shp",
        shp_url = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip",
        population_url = "http://www2.census.gov/programs-surveys/popest/datasets/2010-2019/national/totals/nst-est2019-alldata.csv",
        population_crc32 = 0x191cc64c,
        population_col = "POPESTIMATE2019",
        population_join = "statefp",
        population_preprocess = _population_preprocess_us_state,
    )
 end
 function geodb_load(db_name::AbstractString)::Dict{String,GeoRegion}
    db_name == "2018-us-county" && return _geodb_load_2018_us_county()
    db_name == "us-state" && return _geodb_load_us_state()
    error("Unknown database: $db_name")
 end
 function geodb_query(name)::GeoRegion
    db_name, id = split(name, ":")
    return geodb_load(db_name)[id]
 end
--- a/src/instance/parse.jl
+++ b/src/instance/parse.jl
@@ -2,84 +2,19 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
-using JSON, JSONSchema, Printf
+using DataStructures
-
+using JSON
-
+using JSONSchema
-mutable struct Product
+using Printf
-    name::String
+using Statistics
    transportation_cost::Array{Float64}
    transportation_energy::Array{Float64}
    transportation_emissions::Dict{String, Array{Float64}}
 end
 mutable struct CollectionCenter
    index::Int64
    name::String
    latitude::Float64
    longitude::Float64
    product::Product
    amount::Array{Float64}
 end
 mutable struct PlantSize
    capacity::Float64
    variable_operating_cost::Array{Float64}
    fixed_operating_cost::Array{Float64}
    opening_cost::Array{Float64}
 end
 mutable struct Plant
    index::Int64
    plant_name::String
    location_name::String
    input::Product
    output::Dict{Product, Float64}
    latitude::Float64
    longitude::Float64
    disposal_limit::Dict{Product, Array{Float64}}
    disposal_cost::Dict{Product, Array{Float64}}
    sizes::Array{PlantSize}
    energy::Array{Float64}
    emissions::Dict{String, Array{Float64}}
 end
 mutable struct Instance
    time::Int64
    products::Array{Product, 1}
    collection_centers::Array{CollectionCenter, 1}
    plants::Array{Plant, 1}
    building_period::Array{Int64}
 end
 function validate(json, schema)
    result = JSONSchema.validate(json, schema)
    if result !== nothing
        if result isa JSONSchema.SingleIssue
            path = join(result.path, " → ")
            if length(path) == 0
                path = "root"
            end
            msg = "$(result.msg) in $(path)"
        else
            msg = convert(String, result)
        end
        throw(msg)
    end
 end
 function parsefile(path::String)::Instance
    return RELOG.parse(JSON.parsefile(path))
 end
-
+function parse(json)::Instance
 function parse(json::Dict)::Instance
    basedir = dirname(@__FILE__)
-    json_schema = JSON.parsefile("$basedir/schemas/input.json")
+    json_schema = JSON.parsefile("$basedir/../schemas/input.json")
    validate(json, Schema(json_schema))
    T = json["parameters"]["time horizon (years)"]
@@ -95,7 +30,7 @@ function parse(json::Dict)::Instance
    plants = Plant[]
    products = Product[]
    collection_centers = CollectionCenter[]
-    prod_name_to_product = Dict{String, Product}()
+    prod_name_to_product = Dict{String,Product}()
    # Create products
    for (product_name, product_dict) in json["products"]
@@ -118,12 +53,19 @@ function parse(json::Dict)::Instance
        # 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,
+                if "location" in keys(center_dict)
-                                          center_name,
+                    region = geodb_query(center_dict["location"])
-                                          center_dict["latitude (deg)"],
+                    center_dict["latitude (deg)"] = region.centroid.lat
-                                          center_dict["longitude (deg)"],
+                    center_dict["longitude (deg)"] = region.centroid.lon
-                                          product,
+                end
-                                          center_dict["amount (tonne)"])
+                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
@@ -136,9 +78,10 @@ function parse(json::Dict)::Instance
        # Plant outputs
        if "outputs (tonne/tonne)" in keys(plant_dict)
-            output = Dict(prod_name_to_product[key] => value
+            output = Dict(
-                          for (key, value) in plant_dict["outputs (tonne/tonne)"]
+                prod_name_to_product[key] => value for
-                          if value > 0)
+                (key, value) in plant_dict["outputs (tonne/tonne)"] if value > 0
            )
        end
        energy = zeros(T)
@@ -154,31 +97,53 @@ function parse(json::Dict)::Instance
        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_limit = Dict(p => [0.0 for t = 1:T] for p in keys(output))
-            disposal_cost = Dict(p => [0.0 for t in 1:T] for p in keys(output))
+            disposal_cost = Dict(p => [0.0 for t = 1:T] for p in keys(output))
            # GeoDB
            if "location" in keys(location_dict)
                region = geodb_query(location_dict["location"])
                location_dict["latitude (deg)"] = region.centroid.lat
                location_dict["longitude (deg)"] = region.centroid.lon
            end
            # Disposal
            if "disposal" in keys(location_dict)
                for (product_name, disposal_dict) in location_dict["disposal"]
-                    limit = [1e8 for t in 1:T]
+                    limit = [1e8 for t = 1:T]
                    if "limit (tonne)" in keys(disposal_dict)
-                       limit = disposal_dict["limit (tonne)"]
+                        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)"]
+                    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),
+                push!(
-                                       capacity_dict["variable operating cost (\$/tonne)"],
+                    sizes,
-                                       capacity_dict["fixed operating cost (\$)"],
+                    PlantSize(
-                                       capacity_dict["opening cost (\$)"]))
+                        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])
+            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")
@@ -187,18 +152,22 @@ function parse(json::Dict)::Instance
                throw("Variable operating costs must be the same for all capacities")
            end
-            plant = Plant(length(plants) + 1,
+            plant = Plant(
-                          plant_name,
+                length(plants) + 1,
-                          location_name,
+                plant_name,
-                          input,
+                location_name,
-                          output,
+                input,
-                          location_dict["latitude (deg)"],
+                output,
-                          location_dict["longitude (deg)"],
+                location_dict["latitude (deg)"],
-                          disposal_limit,
+                location_dict["longitude (deg)"],
-                          disposal_cost,
+                disposal_limit,
-                          sizes,
+                disposal_cost,
-                          energy,
+                sizes,
-                          emissions)
+                energy,
                emissions,
                storage_limit,
                storage_cost,
            )
            push!(plants, plant)
        end
--- a/src/instance/structs.jl
+++ b/src/instance/structs.jl
@@ -0,0 +1,57 @@
 # 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::Vector{Float64}
    transportation_energy::Vector{Float64}
    transportation_emissions::Dict{String,Vector{Float64}}
 end
 mutable struct CollectionCenter
    index::Int64
    name::String
    latitude::Float64
    longitude::Float64
    product::Product
    amount::Vector{Float64}
 end
 mutable struct PlantSize
    capacity::Float64
    variable_operating_cost::Vector{Float64}
    fixed_operating_cost::Vector{Float64}
    opening_cost::Vector{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,Vector{Float64}}
    disposal_cost::Dict{Product,Vector{Float64}}
    sizes::Vector{PlantSize}
    energy::Vector{Float64}
    emissions::Dict{String,Vector{Float64}}
    storage_limit::Float64
    storage_cost::Vector{Float64}
 end
 mutable struct Instance
    time::Int64
    products::Vector{Product}
    collection_centers::Vector{CollectionCenter}
    plants::Vector{Plant}
    building_period::Vector{Int64}
 end
--- a/src/instance/validate.jl
+++ b/src/instance/validate.jl
@@ -0,0 +1,21 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataStructures
 using JSON
 using JSONSchema
 using Printf
 using Statistics
 function validate(json, schema)
    result = JSONSchema.validate(json, schema)
    if result !== nothing
        if result isa JSONSchema.SingleIssue
            msg = "$(result.reason) in $(result.path)"
        else
            msg = convert(String, result)
        end
        throw("Error parsing input file: $(msg)")
    end
 end
--- a/src/model.jl
+++ b/src/model.jl
@@ -1,449 +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.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"]
            c += n.location.sizes[1].variable_operating_cost[t]
            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])
        # 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
    # Disposal costs
    for n in values(graph.plant_shipping_nodes), t in 1:T
        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
        # Output amount is implied by input amount
        input_sum = AffExpr(0.0)
        for a in n.incoming_arcs
            add_to_expression!(input_sum, 1.0, vars.flow[a, t])
        end
        for a in n.outgoing_arcs
            @constraint(mip, vars.flow[a, t] == a.values["weight"] * input_sum)
        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])
        # Input sum must be smaller than capacity
        @constraint(mip, input_sum <= 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
        # 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)
    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
    @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)
    @info "Extracting solution..."
    solution = get_solution(model)
    if output != nothing
        @info "Writing solution: $output"
        open(output, "w") do file
            JSON.print(file, solution, 2)
        end
    end
    return solution
 end
 function solve(filename::String; kwargs...)
    @info "Reading $filename..."
    instance = RELOG.parsefile(filename)
    return solve(instance; kwargs...)
 end
 function get_solution(model::ManufacturingModel)
    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),
            "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
    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
    # 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],
        )
        output["Costs"]["Fixed operating (\$)"] += plant_dict["Fixed operating cost (\$)"]
        output["Costs"]["Opening (\$)"] += plant_dict["Opening cost (\$)"]
        output["Costs"]["Expansion (\$)"] += plant_dict["Expansion 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"],
                "Variable operating cost (\$)" => plant.sizes[1].variable_operating_cost .* vals,
                "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["Costs"]["Variable operating (\$)"] += dict["Variable operating 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
--- a/src/model/build.jl
+++ b/src/model/build.jl
@@ -0,0 +1,249 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
 function build_model(instance::Instance, graph::Graph, optimizer)::JuMP.Model
    model = Model(optimizer)
    model[:instance] = instance
    model[:graph] = graph
    create_vars!(model)
    create_objective_function!(model)
    create_shipping_node_constraints!(model)
    create_process_node_constraints!(model)
    return model
 end
 function create_vars!(model::JuMP.Model)
    graph, T = model[:graph], model[:instance].time
    model[:flow] =
        Dict((a, t) => @variable(model, lower_bound = 0) for a in graph.arcs, t = 1:T)
    model[:dispose] = Dict(
        (n, t) => @variable(
            model,
            lower_bound = 0,
            upper_bound = n.location.disposal_limit[n.product][t]
        ) for n in values(graph.plant_shipping_nodes), t = 1:T
    )
    model[:store] = Dict(
        (n, t) =>
            @variable(model, lower_bound = 0, upper_bound = n.location.storage_limit)
        for n in values(graph.process_nodes), t = 1:T
    )
    model[:process] = Dict(
        (n, t) => @variable(model, lower_bound = 0) for
        n in values(graph.process_nodes), t = 1:T
    )
    model[:open_plant] = Dict(
        (n, t) => @variable(model, binary = true) for n in values(graph.process_nodes),
        t = 1:T
    )
    model[:is_open] = Dict(
        (n, t) => @variable(model, binary = true) for n in values(graph.process_nodes),
        t = 1:T
    )
    model[:capacity] = Dict(
        (n, t) => @variable(
            model,
            lower_bound = 0,
            upper_bound = n.location.sizes[2].capacity
        ) for n in values(graph.process_nodes), t = 1:T
    )
    model[:expansion] = Dict(
        (n, t) => @variable(
            model,
            lower_bound = 0,
            upper_bound = n.location.sizes[2].capacity - n.location.sizes[1].capacity
        ) for n in values(graph.process_nodes), t = 1:T
    )
 end
 function slope_open(plant, t)
    if plant.sizes[2].capacity <= plant.sizes[1].capacity
        0.0
    else
        (plant.sizes[2].opening_cost[t] - plant.sizes[1].opening_cost[t]) /
        (plant.sizes[2].capacity - plant.sizes[1].capacity)
    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::JuMP.Model)
    graph, T = model[:graph], model[:instance].time
    obj = AffExpr(0.0)
    # Process node costs
    for n in values(graph.process_nodes), t = 1:T
        # Transportation and variable operating costs
        for a in n.incoming_arcs
            c = n.location.input.transportation_cost[t] * a.values["distance"]
            add_to_expression!(obj, c, model[:flow][a, t])
        end
        # Opening costs
        add_to_expression!(
            obj,
            n.location.sizes[1].opening_cost[t],
            model[:open_plant][n, t],
        )
        # Fixed operating costs (base)
        add_to_expression!(
            obj,
            n.location.sizes[1].fixed_operating_cost[t],
            model[:is_open][n, t],
        )
        # Fixed operating costs (expansion)
        add_to_expression!(obj, slope_fix_oper_cost(n.location, t), model[:expansion][n, t])
        # Processing costs
        add_to_expression!(
            obj,
            n.location.sizes[1].variable_operating_cost[t],
            model[:process][n, t],
        )
        # Storage costs
        add_to_expression!(obj, n.location.storage_cost[t], model[:store][n, t])
        # Expansion costs
        if t < T
            add_to_expression!(
                obj,
                slope_open(n.location, t) - slope_open(n.location, t + 1),
                model[:expansion][n, t],
            )
        else
            add_to_expression!(obj, slope_open(n.location, t), model[:expansion][n, t])
        end
    end
    # Shipping node costs
    for n in values(graph.plant_shipping_nodes), t = 1:T
        # Disposal costs
        add_to_expression!(
            obj,
            n.location.disposal_cost[n.product][t],
            model[:dispose][n, t],
        )
    end
    @objective(model, Min, obj)
 end
 function create_shipping_node_constraints!(model::JuMP.Model)
    graph, T = model[:graph], model[:instance].time
    model[:eq_balance] = OrderedDict()
    for t = 1:T
        # Collection centers
        for n in graph.collection_shipping_nodes
            model[:eq_balance][n, t] = @constraint(
                model,
                sum(model[:flow][a, t] for a in n.outgoing_arcs) == n.location.amount[t]
            )
        end
        # Plants
        for n in graph.plant_shipping_nodes
            @constraint(
                model,
                sum(model[:flow][a, t] for a in n.incoming_arcs) ==
                sum(model[:flow][a, t] for a in n.outgoing_arcs) + model[:dispose][n, t]
            )
        end
    end
 end
 function create_process_node_constraints!(model::JuMP.Model)
    graph, T = model[:graph], model[:instance].time
    for t = 1:T, n in graph.process_nodes
        input_sum = AffExpr(0.0)
        for a in n.incoming_arcs
            add_to_expression!(input_sum, 1.0, model[:flow][a, t])
        end
        # Output amount is implied by amount processed
        for a in n.outgoing_arcs
            @constraint(
                model,
                model[:flow][a, t] == a.values["weight"] * model[:process][n, t]
            )
        end
        # If plant is closed, capacity is zero
        @constraint(
            model,
            model[:capacity][n, t] <= n.location.sizes[2].capacity * model[:is_open][n, t]
        )
        # If plant is open, capacity is greater than base
        @constraint(
            model,
            model[:capacity][n, t] >= n.location.sizes[1].capacity * model[:is_open][n, t]
        )
        # Capacity is linked to expansion
        @constraint(
            model,
            model[:capacity][n, t] <=
            n.location.sizes[1].capacity + model[:expansion][n, t]
        )
        # Can only process up to capacity
        @constraint(model, model[:process][n, t] <= model[:capacity][n, t])
        if t > 1
            # Plant capacity can only increase over time
            @constraint(model, model[:capacity][n, t] >= model[:capacity][n, t-1])
            @constraint(model, model[:expansion][n, t] >= model[:expansion][n, t-1])
        end
        # Amount received equals amount processed plus stored
        store_in = 0
        if t > 1
            store_in = model[:store][n, t-1]
        end
        if t == T
            @constraint(model, model[:store][n, t] == 0)
        end
        @constraint(
            model,
            input_sum + store_in == model[:store][n, t] + model[:process][n, t]
        )
        # Plant is currently open if it was already open in the previous time period or
        # if it was built just now
        if t > 1
            @constraint(
                model,
                model[:is_open][n, t] == model[:is_open][n, t-1] + model[:open_plant][n, t]
            )
        else
            @constraint(model, model[:is_open][n, t] == model[:open_plant][n, t])
        end
        # Plant can only be opened during building period
        if t ∉ model[:instance].building_period
            @constraint(model, model[:open_plant][n, t] == 0)
        end
    end
 end
--- a/src/model/getsol.jl
+++ b/src/model/getsol.jl
@@ -0,0 +1,227 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
 function get_solution(model::JuMP.Model; marginal_costs = true)
    graph, instance = model[:graph], model[:instance]
    T = instance.time
    output = OrderedDict(
        "Plants" => OrderedDict(),
        "Products" => OrderedDict(),
        "Costs" => OrderedDict(
            "Fixed operating (\$)" => zeros(T),
            "Variable operating (\$)" => zeros(T),
            "Opening (\$)" => zeros(T),
            "Transportation (\$)" => zeros(T),
            "Disposal (\$)" => zeros(T),
            "Expansion (\$)" => zeros(T),
            "Storage (\$)" => zeros(T),
            "Total (\$)" => zeros(T),
        ),
        "Energy" =>
            OrderedDict("Plants (GJ)" => zeros(T), "Transportation (GJ)" => zeros(T)),
        "Emissions" => OrderedDict(
            "Plants (tonne)" => OrderedDict(),
            "Transportation (tonne)" => OrderedDict(),
        ),
    )
    plant_to_process_node = OrderedDict(n.location => n for n in graph.process_nodes)
    plant_to_shipping_nodes = OrderedDict()
    for p in instance.plants
        plant_to_shipping_nodes[p] = []
        for a in plant_to_process_node[p].outgoing_arcs
            push!(plant_to_shipping_nodes[p], a.dest)
        end
    end
    # Products
    if marginal_costs
        for n in graph.collection_shipping_nodes
            location_dict = OrderedDict{Any,Any}(
                "Marginal cost (\$/tonne)" => [
                    round(abs(JuMP.shadow_price(model[:eq_balance][n, t])), digits = 2) for t = 1:T
                ],
                "Latitude (deg)" => n.location.latitude,
                "Longitude (deg)" => n.location.longitude,
                "Amount (tonne)" => n.location.amount,
            )
            if n.product.name ∉ keys(output["Products"])
                output["Products"][n.product.name] = OrderedDict()
            end
            output["Products"][n.product.name][n.location.name] = location_dict
        end
    end
    # Plants
    for plant in instance.plants
        skip_plant = true
        process_node = plant_to_process_node[plant]
        plant_dict = OrderedDict{Any,Any}(
            "Input" => OrderedDict(),
            "Output" =>
                OrderedDict("Send" => OrderedDict(), "Dispose" => OrderedDict()),
            "Input product" => plant.input.name,
            "Total input (tonne)" => [0.0 for t = 1:T],
            "Total output" => OrderedDict(),
            "Latitude (deg)" => plant.latitude,
            "Longitude (deg)" => plant.longitude,
            "Capacity (tonne)" =>
                [JuMP.value(model[:capacity][process_node, t]) for t = 1:T],
            "Opening cost (\$)" => [
                JuMP.value(model[:open_plant][process_node, t]) *
                plant.sizes[1].opening_cost[t] for t = 1:T
            ],
            "Fixed operating cost (\$)" => [
                JuMP.value(model[:is_open][process_node, t]) *
                plant.sizes[1].fixed_operating_cost[t] +
                JuMP.value(model[:expansion][process_node, t]) *
                slope_fix_oper_cost(plant, t) for t = 1:T
            ],
            "Expansion cost (\$)" => [
                (
                    if t == 1
                        slope_open(plant, t) * JuMP.value(model[:expansion][process_node, t])
                    else
                        slope_open(plant, t) * (
                            JuMP.value(model[:expansion][process_node, t]) -
                            JuMP.value(model[:expansion][process_node, t-1])
                        )
                    end
                ) for t = 1:T
            ],
            "Process (tonne)" =>
                [JuMP.value(model[:process][process_node, t]) for t = 1:T],
            "Variable operating cost (\$)" => [
                JuMP.value(model[:process][process_node, t]) *
                plant.sizes[1].variable_operating_cost[t] for t = 1:T
            ],
            "Storage (tonne)" =>
                [JuMP.value(model[:store][process_node, t]) for t = 1:T],
            "Storage cost (\$)" => [
                JuMP.value(model[:store][process_node, t]) * plant.storage_cost[t]
                for t = 1:T
            ],
        )
        output["Costs"]["Fixed operating (\$)"] += plant_dict["Fixed operating cost (\$)"]
        output["Costs"]["Variable operating (\$)"] +=
            plant_dict["Variable operating cost (\$)"]
        output["Costs"]["Opening (\$)"] += plant_dict["Opening cost (\$)"]
        output["Costs"]["Expansion (\$)"] += plant_dict["Expansion cost (\$)"]
        output["Costs"]["Storage (\$)"] += plant_dict["Storage cost (\$)"]
        # Inputs
        for a in process_node.incoming_arcs
            vals = [JuMP.value(model[:flow][a, t]) for t = 1:T]
            if sum(vals) <= 1e-3
                continue
            end
            skip_plant = false
            dict = OrderedDict{Any,Any}(
                "Amount (tonne)" => vals,
                "Distance (km)" => a.values["distance"],
                "Latitude (deg)" => a.source.location.latitude,
                "Longitude (deg)" => a.source.location.longitude,
                "Transportation cost (\$)" =>
                    a.source.product.transportation_cost .* vals .* a.values["distance"],
                "Transportation energy (J)" =>
                    vals .* a.values["distance"] .* a.source.product.transportation_energy,
                "Emissions (tonne)" => OrderedDict(),
            )
            emissions_dict = output["Emissions"]["Transportation (tonne)"]
            for (em_name, em_values) in a.source.product.transportation_emissions
                dict["Emissions (tonne)"][em_name] =
                    em_values .* dict["Amount (tonne)"] .* a.values["distance"]
                if em_name ∉ keys(emissions_dict)
                    emissions_dict[em_name] = zeros(T)
                end
                emissions_dict[em_name] += dict["Emissions (tonne)"][em_name]
            end
            if a.source.location isa CollectionCenter
                plant_name = "Origin"
                location_name = a.source.location.name
            else
                plant_name = a.source.location.plant_name
                location_name = a.source.location.location_name
            end
            if plant_name ∉ keys(plant_dict["Input"])
                plant_dict["Input"][plant_name] = OrderedDict()
            end
            plant_dict["Input"][plant_name][location_name] = dict
            plant_dict["Total input (tonne)"] += vals
            output["Costs"]["Transportation (\$)"] += dict["Transportation cost (\$)"]
            output["Energy"]["Transportation (GJ)"] +=
                dict["Transportation energy (J)"] / 1e9
        end
        plant_dict["Energy (GJ)"] = plant_dict["Total input (tonne)"] .* plant.energy
        output["Energy"]["Plants (GJ)"] += plant_dict["Energy (GJ)"]
        plant_dict["Emissions (tonne)"] = OrderedDict()
        emissions_dict = output["Emissions"]["Plants (tonne)"]
        for (em_name, em_values) in plant.emissions
            plant_dict["Emissions (tonne)"][em_name] =
                em_values .* plant_dict["Total input (tonne)"]
            if em_name ∉ keys(emissions_dict)
                emissions_dict[em_name] = zeros(T)
            end
            emissions_dict[em_name] += plant_dict["Emissions (tonne)"][em_name]
        end
        # Outputs
        for shipping_node in plant_to_shipping_nodes[plant]
            product_name = shipping_node.product.name
            plant_dict["Total output"][product_name] = zeros(T)
            plant_dict["Output"]["Send"][product_name] = product_dict = OrderedDict()
            disposal_amount = [JuMP.value(model[:dispose][shipping_node, t]) for t = 1:T]
            if sum(disposal_amount) > 1e-5
                skip_plant = false
                plant_dict["Output"]["Dispose"][product_name] =
                    disposal_dict = OrderedDict()
                disposal_dict["Amount (tonne)"] =
                    [JuMP.value(model[:dispose][shipping_node, t]) for t = 1:T]
                disposal_dict["Cost (\$)"] = [
                    disposal_dict["Amount (tonne)"][t] *
                    plant.disposal_cost[shipping_node.product][t] for t = 1:T
                ]
                plant_dict["Total output"][product_name] += disposal_amount
                output["Costs"]["Disposal (\$)"] += disposal_dict["Cost (\$)"]
            end
            for a in shipping_node.outgoing_arcs
                vals = [JuMP.value(model[:flow][a, t]) for t = 1:T]
                if sum(vals) <= 1e-3
                    continue
                end
                skip_plant = false
                dict = OrderedDict(
                    "Amount (tonne)" => vals,
                    "Distance (km)" => a.values["distance"],
                    "Latitude (deg)" => a.dest.location.latitude,
                    "Longitude (deg)" => a.dest.location.longitude,
                )
                if a.dest.location.plant_name ∉ keys(product_dict)
                    product_dict[a.dest.location.plant_name] = OrderedDict()
                end
                product_dict[a.dest.location.plant_name][a.dest.location.location_name] =
                    dict
                plant_dict["Total output"][product_name] += vals
            end
        end
        if !skip_plant
            if plant.plant_name ∉ keys(output["Plants"])
                output["Plants"][plant.plant_name] = OrderedDict()
            end
            output["Plants"][plant.plant_name][plant.location_name] = plant_dict
        end
    end
    output["Costs"]["Total (\$)"] = sum(values(output["Costs"]))
    return output
 end
--- a/src/model/resolve.jl
+++ b/src/model/resolve.jl
@@ -0,0 +1,97 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP
 function resolve(model_old, filename::AbstractString; kwargs...)::OrderedDict
    @info "Reading $filename..."
    instance = RELOG.parsefile(filename)
    return resolve(model_old, instance; kwargs...)
 end
 function resolve(model_old, instance::Instance; optimizer = nothing)::OrderedDict
    milp_optimizer = lp_optimizer = optimizer
    if optimizer === nothing
        milp_optimizer = _get_default_milp_optimizer()
        lp_optimizer = _get_default_lp_optimizer()
    end
    @info "Building new graph..."
    graph = build_graph(instance)
    _print_graph_stats(instance, graph)
    @info "Building new optimization model..."
    model_new = RELOG.build_model(instance, graph, milp_optimizer)
    @info "Fixing decision variables..."
    _fix_plants!(model_old, model_new)
    JuMP.set_optimizer(model_new, lp_optimizer)
    @info "Optimizing MILP..."
    JuMP.optimize!(model_new)
    if !has_values(model_new)
        @warn("No solution available")
        return OrderedDict()
    end
    @info "Extracting solution..."
    solution = get_solution(model_new, marginal_costs = true)
    return solution
 end
 function _fix_plants!(model_old, model_new)::Nothing
    T = model_new[:instance].time
    # Fix open_plant variables
    for ((node_old, t), var_old) in model_old[:open_plant]
        value_old = JuMP.value(var_old)
        node_new = model_new[:graph].name_to_process_node_map[(
            node_old.location.plant_name,
            node_old.location.location_name,
        )]
        var_new = model_new[:open_plant][node_new, t]
        JuMP.unset_binary(var_new)
        JuMP.fix(var_new, value_old)
    end
    # Fix is_open variables
    for ((node_old, t), var_old) in model_old[:is_open]
        value_old = JuMP.value(var_old)
        node_new = model_new[:graph].name_to_process_node_map[(
            node_old.location.plant_name,
            node_old.location.location_name,
        )]
        var_new = model_new[:is_open][node_new, t]
        JuMP.unset_binary(var_new)
        JuMP.fix(var_new, value_old)
    end
    # Fix plant capacities
    for ((node_old, t), var_old) in model_old[:capacity]
        value_old = JuMP.value(var_old)
        node_new = model_new[:graph].name_to_process_node_map[(
            node_old.location.plant_name,
            node_old.location.location_name,
        )]
        var_new = model_new[:capacity][node_new, t]
        JuMP.delete_lower_bound(var_new)
        JuMP.delete_upper_bound(var_new)
        JuMP.fix(var_new, value_old)
    end
    # Fix plant expansion
    for ((node_old, t), var_old) in model_old[:expansion]
        value_old = JuMP.value(var_old)
        node_new = model_new[:graph].name_to_process_node_map[(
            node_old.location.plant_name,
            node_old.location.location_name,
        )]
        var_new = model_new[:expansion][node_new, t]
        JuMP.delete_lower_bound(var_new)
        JuMP.delete_upper_bound(var_new)
        JuMP.fix(var_new, value_old)
    end
 end
--- a/src/model/solve.jl
+++ b/src/model/solve.jl
@@ -0,0 +1,109 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
 function _get_default_milp_optimizer()
    return optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
 end
 function _get_default_lp_optimizer()
    return optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
 end
 function _print_graph_stats(instance::Instance, graph::Graph)::Nothing
    @info @sprintf("    %12d time periods", instance.time)
    @info @sprintf("    %12d process nodes", length(graph.process_nodes))
    @info @sprintf("    %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
    @info @sprintf(
        "    %12d shipping nodes (collection)",
        length(graph.collection_shipping_nodes)
    )
    @info @sprintf("    %12d arcs", length(graph.arcs))
    return
 end
 function solve(
    instance::Instance;
    optimizer = nothing,
    output = nothing,
    marginal_costs = true,
    return_model = false,
 )
    milp_optimizer = lp_optimizer = optimizer
    if optimizer == nothing
        milp_optimizer = _get_default_milp_optimizer()
        lp_optimizer = _get_default_lp_optimizer()
    end
    @info "Building graph..."
    graph = RELOG.build_graph(instance)
    _print_graph_stats(instance, graph)
    @info "Building optimization model..."
    model = RELOG.build_model(instance, graph, milp_optimizer)
    @info "Optimizing MILP..."
    JuMP.optimize!(model)
    if !has_values(model)
        error("No solution available")
    end
    if marginal_costs
        @info "Re-optimizing with integer variables fixed..."
        all_vars = JuMP.all_variables(model)
        vals = OrderedDict(var => JuMP.value(var) for var in all_vars)
        JuMP.set_optimizer(model, lp_optimizer)
        for var in all_vars
            if JuMP.is_binary(var)
                JuMP.unset_binary(var)
                JuMP.fix(var, vals[var])
            end
        end
        JuMP.optimize!(model)
    end
    @info "Extracting solution..."
    solution = get_solution(model, marginal_costs = marginal_costs)
    if output != nothing
        write(solution, output)
    end
    if return_model
        return solution, model
    else
        return solution
    end
 end
 function solve(filename::AbstractString; heuristic = false, kwargs...)
    @info "Reading $filename..."
    instance = RELOG.parsefile(filename)
    if heuristic && instance.time > 1
        @info "Solving single-period version..."
        compressed = _compress(instance)
        csol = solve(compressed; output = nothing, marginal_costs = false, kwargs...)
        @info "Filtering candidate locations..."
        selected_pairs = []
        for (plant_name, plant_dict) in csol["Plants"]
            for (location_name, location_dict) in plant_dict
                push!(selected_pairs, (plant_name, location_name))
            end
        end
        filtered_plants = []
        for p in instance.plants
            if (p.plant_name, p.location_name) in selected_pairs
                push!(filtered_plants, p)
            end
        end
        instance.plants = filtered_plants
        @info "Solving original version..."
    end
    sol = solve(instance; kwargs...)
    return sol
 end
--- a/src/reports.jl
+++ b/src/reports.jl
@@ -1,268 +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::Dict)::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."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."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
            var_cost = zeros(T)
            for (src_plant_name, src_plant_dict) in location_dict["Input"]
                for (src_location_name, src_location_dict) in src_plant_dict
                    var_cost += src_location_dict["Variable operating cost (\$)"]
                end
            end
            var_cost = round.(var_cost, digits=2)
            for year in 1:T
                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)
                total_cost = round(var_cost[year] + opening_cost + expansion_cost + fixed_cost, digits=2)
                capacity = round(location_dict["Capacity (tonne)"][year], digits=2)
                processed = round(location_dict["Total input (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)
                push!(df, [
                    plant_name,
                    location_name,
                    year,
                    latitude,
                    longitude,
                    capacity,
                    processed,
                    utilization_factor,
                    energy,
                    opening_cost,
                    expansion_cost,
                    fixed_cost,
                    var_cost[year],
                    total_cost,
                ])
            end
        end
    end
    return df
 end
 function plant_outputs_report(solution::Dict)::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::Dict)::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::Dict)::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)"][year], digits=2),
                            round(src_location_dict["Amount (tonne)"][year], digits=2),
                            round(src_location_dict["Amount (tonne)"][year] *
                                      src_location_dict["Distance (km)"][year],
                                  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::Dict)::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)"][year], digits=2),
                                round(src_location_dict["Amount (tonne)"][year], digits=2),
                                round(src_location_dict["Amount (tonne)"][year] *
                                          src_location_dict["Distance (km)"][year],
                                      digits=2),
                                emission_name,
                                round(emission_amount[year], digits=2),
                            ])
                        end
                    end
                end
            end
        end
    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))
 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))
--- a/src/reports/plant_emissions.jl
+++ b/src/reports/plant_emissions.jl
@@ -0,0 +1,38 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 function plant_emissions_report(solution)::DataFrame
    df = DataFrame()
    df."plant type" = String[]
    df."location name" = String[]
    df."year" = Int[]
    df."emission type" = String[]
    df."emission amount (tonne)" = Float64[]
    T = length(solution["Energy"]["Plants (GJ)"])
    for (plant_name, plant_dict) in solution["Plants"]
        for (location_name, location_dict) in plant_dict
            for (emission_name, emission_amount) in location_dict["Emissions (tonne)"]
                for year = 1:T
                    push!(
                        df,
                        [
                            plant_name,
                            location_name,
                            year,
                            emission_name,
                            round(emission_amount[year], digits = 2),
                        ],
                    )
                end
            end
        end
    end
    return df
 end
 write_plant_emissions_report(solution, filename) =
    CSV.write(filename, plant_emissions_report(solution))
--- a/src/reports/plant_outputs.jl
+++ b/src/reports/plant_outputs.jl
@@ -0,0 +1,66 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 function plant_outputs_report(solution)::DataFrame
    df = DataFrame()
    df."plant type" = String[]
    df."location name" = String[]
    df."year" = Int[]
    df."product name" = String[]
    df."amount produced (tonne)" = Float64[]
    df."amount sent (tonne)" = Float64[]
    df."amount disposed (tonne)" = Float64[]
    df."disposal cost (\$)" = Float64[]
    T = length(solution["Energy"]["Plants (GJ)"])
    for (plant_name, plant_dict) in solution["Plants"]
        for (location_name, location_dict) in plant_dict
            for (product_name, amount_produced) in location_dict["Total output"]
                send_dict = location_dict["Output"]["Send"]
                disposal_dict = location_dict["Output"]["Dispose"]
                sent = zeros(T)
                if product_name in keys(send_dict)
                    for (dst_plant_name, dst_plant_dict) in send_dict[product_name]
                        for (dst_location_name, dst_location_dict) in dst_plant_dict
                            sent += dst_location_dict["Amount (tonne)"]
                        end
                    end
                end
                sent = round.(sent, digits = 2)
                disposal_amount = zeros(T)
                disposal_cost = zeros(T)
                if product_name in keys(disposal_dict)
                    disposal_amount += disposal_dict[product_name]["Amount (tonne)"]
                    disposal_cost += disposal_dict[product_name]["Cost (\$)"]
                end
                disposal_amount = round.(disposal_amount, digits = 2)
                disposal_cost = round.(disposal_cost, digits = 2)
                for year = 1:T
                    push!(
                        df,
                        [
                            plant_name,
                            location_name,
                            year,
                            product_name,
                            round(amount_produced[year], digits = 2),
                            sent[year],
                            disposal_amount[year],
                            disposal_cost[year],
                        ],
                    )
                end
            end
        end
    end
    return df
 end
 write_plant_outputs_report(solution, filename) =
    CSV.write(filename, plant_outputs_report(solution))
--- a/src/reports/plants.jl
+++ b/src/reports/plants.jl
@@ -0,0 +1,79 @@
 # 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 = 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
 write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution))
--- a/src/reports/products.jl
+++ b/src/reports/products.jl
@@ -0,0 +1,43 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 function products_report(solution; marginal_costs = true)::DataFrame
    df = DataFrame()
    df."product name" = String[]
    df."location name" = String[]
    df."latitude (deg)" = Float64[]
    df."longitude (deg)" = Float64[]
    df."year" = Int[]
    df."amount (tonne)" = Float64[]
    df."marginal cost (\$/tonne)" = Float64[]
    T = length(solution["Energy"]["Plants (GJ)"])
    for (prod_name, prod_dict) in solution["Products"]
        for (location_name, location_dict) in prod_dict
            for year = 1:T
                marginal_cost = location_dict["Marginal cost (\$/tonne)"][year]
                latitude = round(location_dict["Latitude (deg)"], digits = 6)
                longitude = round(location_dict["Longitude (deg)"], digits = 6)
                amount = location_dict["Amount (tonne)"][year]
                push!(
                    df,
                    [
                        prod_name,
                        location_name,
                        latitude,
                        longitude,
                        year,
                        amount,
                        marginal_cost,
                    ],
                )
            end
        end
    end
    return df
 end
 write_products_report(solution, filename) = CSV.write(filename, products_report(solution))
--- a/src/reports/tr.jl
+++ b/src/reports/tr.jl
@@ -0,0 +1,75 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 function transportation_report(solution)::DataFrame
    df = DataFrame()
    df."source type" = String[]
    df."source location name" = String[]
    df."source latitude (deg)" = Float64[]
    df."source longitude (deg)" = Float64[]
    df."destination type" = String[]
    df."destination location name" = String[]
    df."destination latitude (deg)" = Float64[]
    df."destination longitude (deg)" = Float64[]
    df."product" = String[]
    df."year" = Int[]
    df."distance (km)" = Float64[]
    df."amount (tonne)" = Float64[]
    df."amount-distance (tonne-km)" = Float64[]
    df."transportation cost (\$)" = Float64[]
    df."transportation energy (GJ)" = Float64[]
    T = length(solution["Energy"]["Plants (GJ)"])
    for (dst_plant_name, dst_plant_dict) in solution["Plants"]
        for (dst_location_name, dst_location_dict) in dst_plant_dict
            for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
                for (src_location_name, src_location_dict) in src_plant_dict
                    for year = 1:T
                        push!(
                            df,
                            [
                                src_plant_name,
                                src_location_name,
                                round(src_location_dict["Latitude (deg)"], digits = 6),
                                round(src_location_dict["Longitude (deg)"], digits = 6),
                                dst_plant_name,
                                dst_location_name,
                                round(dst_location_dict["Latitude (deg)"], digits = 6),
                                round(dst_location_dict["Longitude (deg)"], digits = 6),
                                dst_location_dict["Input product"],
                                year,
                                round(src_location_dict["Distance (km)"], digits = 2),
                                round(
                                    src_location_dict["Amount (tonne)"][year],
                                    digits = 2,
                                ),
                                round(
                                    src_location_dict["Amount (tonne)"][year] *
                                    src_location_dict["Distance (km)"],
                                    digits = 2,
                                ),
                                round(
                                    src_location_dict["Transportation cost (\$)"][year],
                                    digits = 2,
                                ),
                                round(
                                    src_location_dict["Transportation energy (J)"][year] /
                                    1e9,
                                    digits = 2,
                                ),
                            ],
                        )
                    end
                end
            end
        end
    end
    return df
 end
 write_transportation_report(solution, filename) =
    CSV.write(filename, transportation_report(solution))
--- a/src/reports/tr_emissions.jl
+++ b/src/reports/tr_emissions.jl
@@ -0,0 +1,71 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 function transportation_emissions_report(solution)::DataFrame
    df = DataFrame()
    df."source type" = String[]
    df."source location name" = String[]
    df."source latitude (deg)" = Float64[]
    df."source longitude (deg)" = Float64[]
    df."destination type" = String[]
    df."destination location name" = String[]
    df."destination latitude (deg)" = Float64[]
    df."destination longitude (deg)" = Float64[]
    df."product" = String[]
    df."year" = Int[]
    df."distance (km)" = Float64[]
    df."shipped amount (tonne)" = Float64[]
    df."shipped amount-distance (tonne-km)" = Float64[]
    df."emission type" = String[]
    df."emission amount (tonne)" = Float64[]
    T = length(solution["Energy"]["Plants (GJ)"])
    for (dst_plant_name, dst_plant_dict) in solution["Plants"]
        for (dst_location_name, dst_location_dict) in dst_plant_dict
            for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
                for (src_location_name, src_location_dict) in src_plant_dict
                    for (emission_name, emission_amount) in
                        src_location_dict["Emissions (tonne)"]
                        for year = 1:T
                            push!(
                                df,
                                [
                                    src_plant_name,
                                    src_location_name,
                                    round(src_location_dict["Latitude (deg)"], digits = 6),
                                    round(src_location_dict["Longitude (deg)"], digits = 6),
                                    dst_plant_name,
                                    dst_location_name,
                                    round(dst_location_dict["Latitude (deg)"], digits = 6),
                                    round(dst_location_dict["Longitude (deg)"], digits = 6),
                                    dst_location_dict["Input product"],
                                    year,
                                    round(src_location_dict["Distance (km)"], digits = 2),
                                    round(
                                        src_location_dict["Amount (tonne)"][year],
                                        digits = 2,
                                    ),
                                    round(
                                        src_location_dict["Amount (tonne)"][year] *
                                        src_location_dict["Distance (km)"],
                                        digits = 2,
                                    ),
                                    emission_name,
                                    round(emission_amount[year], digits = 2),
                                ],
                            )
                        end
                    end
                end
            end
        end
    end
    return df
 end
 write_transportation_emissions_report(solution, filename) =
    CSV.write(filename, transportation_emissions_report(solution))
--- a/src/reports/write.jl
+++ b/src/reports/write.jl
@@ -0,0 +1,14 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using DataFrames
 using CSV
 import Base: write
 function write(solution::AbstractDict, filename::AbstractString)
    @info "Writing solution: $filename"
    open(filename, "w") do file
        JSON.print(file, solution, 2)
    end
 end
--- a/src/schemas/input.json
+++ b/src/schemas/input.json
@@ -12,7 +12,9 @@
        "Parameters": {
            "type": "object",
            "properties": {
-                "time horizon (years)": { "type": "number" }
+                "time horizon (years)": {
                    "type": "number"
                }
            },
            "required": [
                "time horizon (years)"
@@ -23,17 +25,27 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "input": { "type": "string" },
+                    "input": {
                        "type": "string"
                    },
                    "outputs (tonne/tonne)": {
                        "type": "object",
-                        "additionalProperties": { "type": "number" }
+                        "additionalProperties": {
                            "type": "number"
                        }
                    },
                    "energy (GJ/tonne)": {
                        "$ref": "#/definitions/TimeSeries"
                    },
                    "energy (GJ/tonne)": { "$ref": "#/definitions/TimeSeries" },
                    "emissions (tonne/tonne)": {
                        "type": "object",
-                        "additionalProperties": { "$ref": "#/definitions/TimeSeries" }
+                        "additionalProperties": {
                            "$ref": "#/definitions/TimeSeries"
                        }
                    },
-                    "locations": { "$ref": "#/definitions/PlantLocation" }
+                    "locations": {
                        "$ref": "#/definitions/PlantLocation"
                    }
                },
                "required": [
                    "input",
@@ -46,29 +58,61 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "latitude (deg)": { "type": "number" },
+                    "location": {
-                    "longitude (deg)": { "type": "number" },
+                        "type": "string"
                    },
                    "latitude (deg)": {
                        "type": "number"
                    },
                    "longitude (deg)": {
                        "type": "number"
                    },
                    "disposal": {
                        "type": "object",
                        "additionalProperties": {
                            "type": "object",
                            "properties": {
-                                "cost ($/tonne)": { "$ref": "#/definitions/TimeSeries" },
+                                "cost ($/tonne)": {
-                                "limit (tonne)": { "$ref": "#/definitions/TimeSeries" }
+                                    "$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)": { "$ref": "#/definitions/TimeSeries" },
+                                "variable operating cost ($/tonne)": {
-                                "fixed operating cost ($)": { "$ref": "#/definitions/TimeSeries" },
+                                    "$ref": "#/definitions/TimeSeries"
-                                "opening cost ($)": { "$ref": "#/definitions/TimeSeries" }
+                                },
                                "fixed operating cost ($)": {
                                    "$ref": "#/definitions/TimeSeries"
                                },
                                "opening cost ($)": {
                                    "$ref": "#/definitions/TimeSeries"
                                }
                            },
                            "required": [
                                "variable operating cost ($/tonne)",
@@ -79,8 +123,6 @@
                    }
                },
                "required": [
                    "latitude (deg)",
                    "longitude (deg)",
                    "capacities (tonne)"
                ]
            }
@@ -90,13 +132,20 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "latitude (deg)": { "type": "number" },
+                    "location": {
-                    "longitude (deg)": { "type": "number" },
+                        "type": "string"
-                    "amount (tonne)": { "$ref": "#/definitions/TimeSeries" }
+                    },
                    "latitude (deg)": {
                        "type": "number"
                    },
                    "longitude (deg)": {
                        "type": "number"
                    },
                    "amount (tonne)": {
                        "$ref": "#/definitions/TimeSeries"
                    }
                },
                "required": [
                    "latitude (deg)",
                    "longitude (deg)",
                    "amount (tonne)"
                ]
            }
@@ -106,13 +155,21 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "transportation cost ($/km/tonne)": { "$ref": "#/definitions/TimeSeries" },
+                    "transportation cost ($/km/tonne)": {
-                    "transportation energy (J/km/tonne)": { "$ref": "#/definitions/TimeSeries" },
+                        "$ref": "#/definitions/TimeSeries"
                    },
                    "transportation energy (J/km/tonne)": {
                        "$ref": "#/definitions/TimeSeries"
                    },
                    "transportation emissions (tonne/km/tonne)": {
                        "type": "object",
-                        "additionalProperties": { "$ref": "#/definitions/TimeSeries" }
+                        "additionalProperties": {
                            "$ref": "#/definitions/TimeSeries"
                        }
                    },
-                    "initial amounts": { "$ref": "#/definitions/InitialAmount" }
+                    "initial amounts": {
                        "$ref": "#/definitions/InitialAmount"
                    }
                },
                "required": [
                    "transportation cost ($/km/tonne)"
@@ -122,9 +179,15 @@
    },
    "type": "object",
    "properties": {
-        "parameters": { "$ref": "#/definitions/Parameters" },
+        "parameters": {
-        "plants": { "$ref": "#/definitions/Plant" },
+            "$ref": "#/definitions/Parameters"
-        "products": { "$ref": "#/definitions/Product" }
+        },
        "plants": {
            "$ref": "#/definitions/Plant"
        },
        "products": {
            "$ref": "#/definitions/Product"
        }
    },
    "required": [
        "parameters",
--- a/src/sysimage.jl
+++ b/src/sysimage.jl
@@ -9,14 +9,7 @@ using JuMP
 using MathOptInterface
 using ProgressBars
-pkg = [:Cbc,
+pkg = [:Cbc, :Clp, :Geodesy, :JSON, :JSONSchema, :JuMP, :MathOptInterface, :ProgressBars]
       :Clp,
       :Geodesy,
       :JSON,
       :JSONSchema,
       :JuMP,
       :MathOptInterface,
       :ProgressBars]
@info "Building system image..."
-create_sysimage(pkg, sysimage_path="build/sysimage.so")
+create_sysimage(pkg, sysimage_path = "build/sysimage.so")
--- a/test/fixtures/nimh_plant_emissions.csv
+++ b/test/fixtures/nimh_plant_emissions.csv
@@ -1,7 +0,0 @@
 plant type,location name,year,emission type,emission amount (tonne)
 Rare Earth Recycling Plant,"Sebastian, Arkansas",1,CO2,40711.3
 Rare Earth Recycling Plant,"Stanly, North Carolina",1,CO2,23336.47
 Rare Earth Recycling Plant,"Lynn, Texas",1,CO2,52927.44
 Mega Plant,"Sebastian, Arkansas",1,CO2,110818.84
 Mega Plant,"District of Columbia, District of Columbia",1,CO2,63523.43
 Mega Plant,"Maricopa, Arizona",1,CO2,144072.0
--- a/test/fixtures/nimh_plant_outputs.csv
+++ b/test/fixtures/nimh_plant_outputs.csv
@@ -1,37 +0,0 @@
 plant type,location name,year,product name,amount produced (tonne),amount sent (tonne),amount disposed (tonne),disposal cost ($)
 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
--- a/test/fixtures/nimh_plants.csv
+++ b/test/fixtures/nimh_plants.csv
@@ -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 ($)
 Rare Earth Recycling Plant,"Sebastian, Arkansas",1,35.23416,-94.212943,44145.84,44145.84,100.0,1.13360359e6,6.9926855e6,1.793555439e7,1.677420707e7,1.0080261442e8,1.4250506138e8
 Rare Earth Recycling Plant,"Stanly, North Carolina",1,35.334445,-80.223231,25305.22,25305.22,100.0,649802.71,7.1653444e6,9.98954108e6,1.126536154e7,5.778193764e7,8.620218466e7
 Rare Earth Recycling Plant,"Lynn, Texas",1,33.166444,-101.793455,57392.58,57392.58,100.0,1.47376145e6,7.4243328e6,2.5154042e7,2.06474474e7,1.310502261e8,1.842760483e8
 Mega Plant,"Sebastian, Arkansas",1,35.23416,-94.212943,553817.3,553817.3,100.0,3.08574408e6,1.6858178e7,4.012879371e7,3.834652057e7,4.2898688058e8,5.2432037286e8
 Mega Plant,"District of Columbia, District of Columbia",1,38.930028,-76.974164,317458.4,317458.4,100.0,1.76880602e6,2.12288167e7,2.746685387e7,2.602109584e7,2.4590327664e8,3.2062004305e8
 Mega Plant,"Maricopa, Arizona",1,33.647365,-111.893669,720000.0,720000.0,100.0,4.0116763e6,2.10206911e7,6.60955172e7,4.70124619e7,5.57712e8,6.918406702e8
--- a/test/fixtures/nimh_solution.json.gz
+++ b/test/fixtures/nimh_solution.json.gz
--- a/test/fixtures/nimh_transportation.csv
+++ b/test/fixtures/nimh_transportation.csv
--- a/test/fixtures/nimh_transportation_emissions.csv
+++ b/test/fixtures/nimh_transportation_emissions.csv
--- a/test/fixtures/storage.json
+++ b/test/fixtures/storage.json
@@ -0,0 +1,39 @@
 {
    "parameters": {
        "time horizon (years)": 3
    },
    "products": {
        "battery": {
            "initial amounts": {
                "Chicago": {
                    "latitude (deg)": 0.0,
                    "longitude (deg)": 0.0,
                    "amount (tonne)": [100.0, 0.0, 0.0]
                }
            },
            "transportation cost ($/km/tonne)": [0.01, 0.01, 0.01]
        }
    },
    "plants": {
        "mega plant": {
            "input": "battery",
            "locations": {
                "Chicago": {
                    "latitude (deg)": 0.0,
                    "longitude (deg)": 0.0,
                    "storage": {
                        "cost ($/tonne)": [2.0, 1.5, 1.0],
                        "limit (tonne)": 50.0
                    },
                    "capacities (tonne)": {
                        "100": {
                            "opening cost ($)": [0.0, 0.0, 0],
                            "fixed operating cost ($)": [0.0, 0.0, 0.0],
                            "variable operating cost ($/tonne)": [10.0, 5.0, 2.0]
                        }
                    }
                }
            }
        }
    }
 }
--- a/test/graph/build_test.jl
+++ b/test/graph/build_test.jl
@@ -0,0 +1,39 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG
@testset "build_graph" begin
    basedir = dirname(@__FILE__)
    instance = RELOG.parsefile("$basedir/../../instances/s1.json")
    graph = RELOG.build_graph(instance)
    process_node_by_location_name =
        Dict(n.location.location_name => n for n in graph.process_nodes)
    @test length(graph.plant_shipping_nodes) == 8
    @test length(graph.collection_shipping_nodes) == 10
    @test length(graph.process_nodes) == 6
    node = graph.collection_shipping_nodes[1]
    @test node.location.name == "C1"
    @test length(node.incoming_arcs) == 0
    @test length(node.outgoing_arcs) == 2
    @test node.outgoing_arcs[1].source.location.name == "C1"
    @test node.outgoing_arcs[1].dest.location.plant_name == "F1"
    @test node.outgoing_arcs[1].dest.location.location_name == "L1"
    @test node.outgoing_arcs[1].values["distance"] == 1095.62
    node = process_node_by_location_name["L1"]
    @test node.location.plant_name == "F1"
    @test node.location.location_name == "L1"
    @test length(node.incoming_arcs) == 10
    @test length(node.outgoing_arcs) == 2
    node = process_node_by_location_name["L3"]
    @test node.location.plant_name == "F2"
    @test node.location.location_name == "L3"
    @test length(node.incoming_arcs) == 2
    @test length(node.outgoing_arcs) == 2
    @test length(graph.arcs) == 38
 end
--- a/test/graph_test.jl
+++ b/test/graph_test.jl
@@ -1,42 +0,0 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG
@testset "Graph" begin
    @testset "build_graph" begin
        basedir = dirname(@__FILE__)
        instance = RELOG.parsefile("$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
--- a/test/instance/compress_test.jl
+++ b/test/instance/compress_test.jl
@@ -0,0 +1,53 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG
@testset "compress" begin
    basedir = dirname(@__FILE__)
    instance = RELOG.parsefile("$basedir/../../instances/s1.json")
    compressed = RELOG._compress(instance)
    product_name_to_product = Dict(p.name => p for p in compressed.products)
    location_name_to_facility = Dict()
    for p in compressed.plants
        location_name_to_facility[p.location_name] = p
    end
    for c in compressed.collection_centers
        location_name_to_facility[c.name] = c
    end
    p1 = product_name_to_product["P1"]
    p2 = product_name_to_product["P2"]
    p3 = product_name_to_product["P3"]
    c1 = location_name_to_facility["C1"]
    l1 = location_name_to_facility["L1"]
    @test compressed.time == 1
    @test compressed.building_period == [1]
    @test p1.name == "P1"
    @test p1.transportation_cost ≈ [0.015]
    @test p1.transportation_energy ≈ [0.115]
    @test p1.transportation_emissions["CO2"] ≈ [0.051]
    @test p1.transportation_emissions["CH4"] ≈ [0.0025]
    @test c1.name == "C1"
    @test c1.amount ≈ [1869.12]
    @test l1.plant_name == "F1"
    @test l1.location_name == "L1"
    @test l1.energy ≈ [0.115]
    @test l1.emissions["CO2"] ≈ [0.051]
    @test l1.emissions["CH4"] ≈ [0.0025]
    @test l1.sizes[1].opening_cost ≈ [500]
    @test l1.sizes[2].opening_cost ≈ [1250]
    @test l1.sizes[1].fixed_operating_cost ≈ [60]
    @test l1.sizes[2].fixed_operating_cost ≈ [60]
    @test l1.sizes[1].variable_operating_cost ≈ [30]
    @test l1.sizes[2].variable_operating_cost ≈ [30]
    @test l1.disposal_limit[p2] ≈ [2.0]
    @test l1.disposal_limit[p3] ≈ [2.0]
    @test l1.disposal_cost[p2] ≈ [-10.0]
    @test l1.disposal_cost[p3] ≈ [-10.0]
 end
--- a/test/instance/geodb_test.jl
+++ b/test/instance/geodb_test.jl
@@ -0,0 +1,25 @@
 # RELOG: Reverse Logistics Optimization
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.
 using RELOG
@testset "geodb_query (2018-us-county)" begin
    region = RELOG.geodb_query("2018-us-county:17043")
    @test region.centroid.lat == 41.83956
    @test region.centroid.lon == -88.08857
    @test region.population == 922_921
 end
 # @testset "geodb_query (2018-us-zcta)" begin
 #     region = RELOG.geodb_query("2018-us-zcta:60439")
 #     @test region.centroid.lat == 41.68241
 #     @test region.centroid.lon == -87.98954
 # end
@testset "geodb_query (us-state)" begin
    region = RELOG.geodb_query("us-state:IL")
    @test region.centroid.lat == 39.73939
    @test region.centroid.lon == -89.50414
    @test region.population == 12_671_821
 end
--- a/test/instance/parse_test.jl
+++ b/test/instance/parse_test.jl
@@ -0,0 +1,86 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG
@testset "parse" begin
    basedir = dirname(@__FILE__)
    instance = RELOG.parsefile("$basedir/../../instances/s1.json")
    centers = instance.collection_centers
    plants = instance.plants
    products = instance.products
    location_name_to_plant = Dict(p.location_name => p for p in plants)
    product_name_to_product = Dict(p.name => p for p in products)
    @test length(centers) == 10
    @test centers[1].name == "C1"
    @test centers[1].latitude == 7
    @test centers[1].latitude == 7
    @test centers[1].longitude == 7
    @test centers[1].amount == [934.56, 934.56]
    @test centers[1].product.name == "P1"
    @test length(plants) == 6
    plant = location_name_to_plant["L1"]
    @test plant.plant_name == "F1"
    @test plant.location_name == "L1"
    @test plant.input.name == "P1"
    @test plant.latitude == 0
    @test plant.longitude == 0
    @test length(plant.sizes) == 2
    @test plant.sizes[1].capacity == 250
    @test plant.sizes[1].opening_cost == [500, 500]
    @test plant.sizes[1].fixed_operating_cost == [30, 30]
    @test plant.sizes[1].variable_operating_cost == [30, 30]
    @test plant.sizes[2].capacity == 1000
    @test plant.sizes[2].opening_cost == [1250, 1250]
    @test plant.sizes[2].fixed_operating_cost == [30, 30]
    @test plant.sizes[2].variable_operating_cost == [30, 30]
    p2 = product_name_to_product["P2"]
    p3 = product_name_to_product["P3"]
    @test length(plant.output) == 2
    @test plant.output[p2] == 0.2
    @test plant.output[p3] == 0.5
    @test plant.disposal_limit[p2] == [1, 1]
    @test plant.disposal_limit[p3] == [1, 1]
    @test plant.disposal_cost[p2] == [-10, -10]
    @test plant.disposal_cost[p3] == [-10, -10]
    plant = location_name_to_plant["L3"]
    @test plant.location_name == "L3"
    @test plant.input.name == "P2"
    @test plant.latitude == 25
    @test plant.longitude == 65
    @test length(plant.sizes) == 2
    @test plant.sizes[1].capacity == 1000.0
    @test plant.sizes[1].opening_cost == [3000, 3000]
    @test plant.sizes[1].fixed_operating_cost == [50, 50]
    @test plant.sizes[1].variable_operating_cost == [50, 50]
    @test plant.sizes[1] == plant.sizes[2]
    p4 = product_name_to_product["P4"]
    @test plant.output[p3] == 0.05
    @test plant.output[p4] == 0.8
    @test plant.disposal_limit[p3] == [1e8, 1e8]
    @test plant.disposal_limit[p4] == [0, 0]
 end
@testset "parse (geodb)" begin
    basedir = dirname(@__FILE__)
    instance = RELOG.parsefile("$basedir/../../instances/s2.json")
    centers = instance.collection_centers
    @test centers[1].name == "C1"
    @test centers[1].latitude == 41.83956
    @test centers[1].longitude == -88.08857
 end
 # @testset "parse (invalid)" begin
 #     basedir = dirname(@__FILE__)
 #     @test_throws ErrorException RELOG.parsefile("$basedir/../fixtures/s1-wrong-length.json")
 # end
--- a/test/instance_test.jl
+++ b/test/instance_test.jl
@@ -1,79 +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
 end
--- a/test/model/build_test.jl
+++ b/test/model/build_test.jl
@@ -0,0 +1,38 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
@testset "build" begin
    basedir = dirname(@__FILE__)
    instance = RELOG.parsefile("$basedir/../../instances/s1.json")
    graph = RELOG.build_graph(instance)
    model = RELOG.build_model(instance, graph, Cbc.Optimizer)
    set_optimizer_attribute(model, "logLevel", 0)
    process_node_by_location_name =
        Dict(n.location.location_name => n for n in graph.process_nodes)
    shipping_node_by_loc_and_prod_names = Dict(
        (n.location.location_name, n.product.name) => n for n in graph.plant_shipping_nodes
    )
    @test length(model[:flow]) == 76
    @test length(model[:dispose]) == 16
    @test length(model[:open_plant]) == 12
    @test length(model[:capacity]) == 12
    @test length(model[:expansion]) == 12
    l1 = process_node_by_location_name["L1"]
    v = model[:capacity][l1, 1]
    @test lower_bound(v) == 0.0
    @test upper_bound(v) == 1000.0
    v = model[:expansion][l1, 1]
    @test lower_bound(v) == 0.0
    @test upper_bound(v) == 750.0
    v = model[:dispose][shipping_node_by_loc_and_prod_names["L1", "P2"], 1]
    @test lower_bound(v) == 0.0
    @test upper_bound(v) == 1.0
 end
--- a/test/model/resolve_test.jl
+++ b/test/model/resolve_test.jl
@@ -0,0 +1,11 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG
@testset "Resolve" begin
    # Shoud not crash
    filename = "$(pwd())/../instances/s1.json"
    solution_old, model_old = RELOG.solve(filename, return_model = true)
    solution_new = RELOG.resolve(model_old, filename)
 end
--- a/test/model/solve_test.jl
+++ b/test/model/solve_test.jl
@@ -0,0 +1,61 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
 basedir = dirname(@__FILE__)
@testset "solve (exact)" begin
    solution_filename_a = tempname()
    solution_filename_b = tempname()
    solution = RELOG.solve("$basedir/../../instances/s1.json", output = solution_filename_a)
    @test isfile(solution_filename_a)
    RELOG.write(solution, solution_filename_b)
    @test isfile(solution_filename_b)
    @test "Costs" in keys(solution)
    @test "Fixed operating (\$)" in keys(solution["Costs"])
    @test "Transportation (\$)" in keys(solution["Costs"])
    @test "Variable operating (\$)" in keys(solution["Costs"])
    @test "Total (\$)" in keys(solution["Costs"])
    @test "Plants" in keys(solution)
    @test "F1" in keys(solution["Plants"])
    @test "F2" in keys(solution["Plants"])
    @test "F3" in keys(solution["Plants"])
    @test "F4" in keys(solution["Plants"])
 end
@testset "solve (heuristic)" begin
    # Should not crash
    solution = RELOG.solve("$basedir/../../instances/s1.json", heuristic = true)
 end
@testset "solve (infeasible)" begin
    json = JSON.parsefile("$basedir/../../instances/s1.json")
    for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
        location_dict["amount (tonne)"] *= 1000
    end
    @test_throws ErrorException("No solution available") RELOG.solve(RELOG.parse(json))
 end
@testset "solve (with storage)" begin
    basedir = dirname(@__FILE__)
    filename = "$basedir/../fixtures/storage.json"
    instance = RELOG.parsefile(filename)
    @test instance.plants[1].storage_limit == 50.0
    @test instance.plants[1].storage_cost == [2.0, 1.5, 1.0]
    solution = RELOG.solve(filename)
    plant_dict = solution["Plants"]["mega plant"]["Chicago"]
    @test plant_dict["Variable operating cost (\$)"] == [500.0, 0.0, 100.0]
    @test plant_dict["Process (tonne)"] == [50.0, 0.0, 50.0]
    @test plant_dict["Storage (tonne)"] == [50.0, 50.0, 0.0]
    @test plant_dict["Storage cost (\$)"] == [100.0, 75.0, 0.0]
    @test solution["Costs"]["Variable operating (\$)"] == [500.0, 0.0, 100.0]
    @test solution["Costs"]["Storage (\$)"] == [100.0, 75.0, 0.0]
    @test solution["Costs"]["Total (\$)"] == [600.0, 75.0, 100.0]
 end
--- a/test/model_test.jl
+++ b/test/model_test.jl
@@ -1,72 +0,0 @@
 # Copyright (C) 2020 Argonne National Laboratory
 # Written by Alinson Santos Xavier <axavier@anl.gov>
 using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
@testset "Model" begin
    @testset "build" begin
        basedir = dirname(@__FILE__)
        instance = RELOG.parsefile("$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" begin
        solution_filename = tempname()
        solution = RELOG.solve("$(pwd())/../instances/s1.json",
                               output=solution_filename)
        @test "Costs" in keys(solution)
        @test "Fixed operating (\$)" in keys(solution["Costs"])
        @test "Transportation (\$)" in keys(solution["Costs"])
        @test "Variable operating (\$)" in keys(solution["Costs"])
        @test "Total (\$)" in keys(solution["Costs"])
        @test "Plants" in keys(solution)
        @test "F1" in keys(solution["Plants"])
        @test "F2" in keys(solution["Plants"])
        @test "F3" in keys(solution["Plants"])
        @test "F4" in keys(solution["Plants"])
    end
    @testset "infeasible solve" begin
        json = JSON.parsefile("$(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
 end
--- a/test/reports_test.jl
+++ b/test/reports_test.jl
@@ -4,25 +4,16 @@
 using RELOG, JSON, GZip
-load_json_gz(filename) = JSON.parse(GZip.gzopen(filename))
+@testset "Reports" begin
-
+    @testset "from solve" begin
-function check(func, expected_csv_filename::String)
+        solution = RELOG.solve("$(pwd())/../instances/s1.json")
-    solution = load_json_gz("fixtures/nimh_solution.json.gz")
+        tmp_filename = tempname()
-    actual_csv_filename = tempname()
+        # The following should not crash
-    func(solution, actual_csv_filename)
+        RELOG.write_plant_emissions_report(solution, tmp_filename)
-    @test isfile(actual_csv_filename)
+        RELOG.write_plant_outputs_report(solution, tmp_filename)
-    if readlines(actual_csv_filename) != readlines(expected_csv_filename)
+        RELOG.write_plants_report(solution, tmp_filename)
-        out_filename = replace(expected_csv_filename, ".csv" => "_actual.csv")
+        RELOG.write_products_report(solution, tmp_filename)
-        @error "$func: Unexpected CSV contents: $out_filename"
+        RELOG.write_transportation_emissions_report(solution, tmp_filename)
-        write(out_filename, read(actual_csv_filename))
+        RELOG.write_transportation_report(solution, tmp_filename)
        @test false
    end
 end
@testset "Reports" 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
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -4,8 +4,18 @@
 using Test
@testset "RELOG" begin
-    include("instance_test.jl")
+    @testset "Instance" begin
-    include("graph_test.jl")
+        include("instance/compress_test.jl")
-    include("model_test.jl")
+        include("instance/geodb_test.jl")
        include("instance/parse_test.jl")
    end
    @testset "Graph" begin
        include("graph/build_test.jl")
    end
    @testset "Model" begin
        include("model/build_test.jl")
        include("model/solve_test.jl")
        include("model/resolve_test.jl")
    end
    include("reports_test.jl")
 end
Author	SHA1	Message	Date
Alinson S. Xavier	da158eb961	Update CHANGELOG	2022-08-26 13:26:15 -05:00
Alinson S. Xavier	e7eec937cb	Update README.md	2022-08-26 13:21:42 -05:00
Alinson S. Xavier	19bec961bd	GH Actions: Update Julia versions	2022-08-26 13:12:20 -05:00
Alinson S. Xavier	8f52c04702	Fix broken image link	2022-08-26 13:08:22 -05:00
Alinson S. Xavier	19a34fb5d2	Update dependencies; switch to Documenter.jl	2022-08-26 13:04:47 -05:00
Alinson S. Xavier	92d30460b9	Update README.md	2021-09-03 18:07:40 -05:00
Alinson S Xavier	9ebb2e49f9	Fix validation error on JSONSchema 0.3	2021-08-06 14:56:46 -05:00
Alinson S Xavier	505e3a8e1e	Update CHANGELOG	2021-07-23 17:42:48 -05:00
Alinson S Xavier	d4fa75297f	Fix OrderedCollections version	2021-07-23 17:42:29 -05:00
Alinson S Xavier	881957d6b5	Implement resolve	2021-07-21 14:53:49 -05:00
Alinson S Xavier	86cf7f5bd9	Throw exception for infeasible models	2021-07-21 14:18:10 -05:00
Alinson S Xavier	a8c7047e2d	Add custom show function for Instance and Graph Without these functions, Julia 1.5 enters an infinite loop whenever it tries to generate a stack trace, so any error (such as a missing method) causes the program to hang, instead of an error message to appear.	2021-07-21 14:11:01 -05:00
Alinson S Xavier	099e0fae3a	Docs: Minor fixes to what-if analsis section	2021-07-21 14:07:00 -05:00
Alinson S Xavier	1b8f392852	Docs: Add description of resolve	2021-07-21 14:07:00 -05:00
Alinson S Xavier	7a95aa66f6	Update CHANGELOG	2021-07-21 11:49:54 -05:00
Alinson S Xavier	40d28c727a	Add products report	2021-07-16 11:25:40 -05:00
Alinson S Xavier	a9ac164833	Fix GeoDB download	2021-07-16 10:31:21 -05:00
Alinson S Xavier	e244ded51d	GH Actions: Add Julia 1.6, remove nightly	2021-07-16 10:18:10 -05:00
Alinson S Xavier	7180651cfa	Reformat source code	2021-07-16 10:15:41 -05:00
Alinson S Xavier	0c9465411f	Document GeoDB; remove unused code; minor fixes	2021-07-16 10:13:58 -05:00
Alinson S Xavier	658d5ddbdc	Add population to region; disable zip codes	2021-07-01 17:14:00 -05:00
Alinson S Xavier	399db41f86	Temporarily disable failing test	2021-07-01 16:13:38 -05:00
Alinson S Xavier	e407a53ecf	Download and join population	2021-07-01 16:10:55 -05:00
Alinson S Xavier	33ab4c5f76	GeoDB: Prepare for population	2021-07-01 14:56:08 -05:00
Alinson S Xavier	c9391dd299	Update JSONSchema	2021-07-01 14:56:08 -05:00
Alinson S Xavier	6c70d9acd5	GeoDB: Add 2018-us-zcta and us-state	2021-07-01 14:56:08 -05:00
Alinson S Xavier	339255bf9b	Enable geodb in input files	2021-07-01 14:56:08 -05:00
Alinson S Xavier	ca187fe78e	Implement geodb.jl	2021-07-01 14:56:08 -05:00
Alinson S. Xavier	c256cd8b75	Update CHANGELOG.md	2021-06-25 06:16:20 -05:00
Alinson S. Xavier	05d48e2cbf	Update tagbot.yml	2021-06-25 06:13:27 -05:00
Alinson S Xavier	9446b1921d	Add tagbot.yml	2021-06-22 11:10:15 -05:00
Alinson S Xavier	1b0cc141bb	Remove Manifest.toml from repository	2021-06-22 11:00:11 -05:00
Alinson S Xavier	a333ab0b04	Remove unused code and test resources	2021-06-22 10:58:46 -05:00
Alinson S Xavier	630ae49d4a	Replace Array by Vector	2021-06-22 10:53:40 -05:00
Alinson S Xavier	9df416ed75	Split files	2021-06-22 10:50:11 -05:00
Alinson S Xavier	849f902562	Reformat code	2021-06-22 10:21:17 -05:00
Alinson S Xavier	1990563476	Remove dotdict	2021-06-22 10:20:40 -05:00
Alinson S Xavier	7e783c8b91	Replace ManufacturingModel by JuMP.Model	2021-06-22 10:19:31 -05:00
Alinson S Xavier	93cc6fbf32	Remove model.eqs	2021-06-22 10:10:48 -05:00
Alinson S Xavier	a7938b7260	Remove model.vars	2021-06-22 10:08:01 -05:00
Alinson S Xavier	56ef1f7bc2	Update dependencies	2021-06-22 10:07:40 -05:00
Alinson S Xavier	b00b24ffbc	Reformat source code; set up lint GH Action	2021-06-22 09:49:45 -05:00
Alinson S. Xavier	823db2838b	GH Actions: Run tests daily	2021-06-22 09:41:01 -05:00
Alinson S Xavier	7d91ac27d4	Test package on multiple Julia versions	2021-01-06 09:25:39 -06:00
Alinson S Xavier	4098057cc1	Update README.md	2021-01-06 09:20:03 -06:00
Alinson S Xavier	b91ca9e6cb	Re-add RELOG.write function	2021-01-06 09:15:28 -06:00
Alinson S. Xavier	d870f028c0	Update README.md	2020-12-05 15:42:24 -06:00
Alinson S Xavier	a518e3d3d6	Allow plants to store input material for later years	2020-10-29 15:23:40 -05:00
Alinson S Xavier	be5e09a4ec	Fix crash when generating transportation reports	2020-10-09 09:33:05 -05:00
Alinson S Xavier	77cd5871a2	Only use heuristic in multi-period instances	2020-10-06 14:26:26 -05:00
Alinson S Xavier	a6846be9eb	Implement multi-period heuristic	2020-10-06 14:16:14 -05:00
Alinson S Xavier	fe9cacef24	Docs: replace "product" by "product name"; fix incorrect chart	2020-09-21 11:43:56 -05:00
Alinson S Xavier	0f60b847dd	reports.jl: remove dict restriction Sometimes solutions are OrderedDicts instead.	2020-09-18 15:44:51 -05:00