Restrict NearestNeighbors version; remove debug statement

Bump version to 0.6
Make distance metric configurable; fix building period bug
2025-12-05 23:38:52 -06:00 · 2022-12-15 11:03:44 -06:00 · 2022-12-15 10:36:44 -06:00 · 2022-12-15 10:26:10 -06:00 · 2022-12-15 09:49:38 -06:00 · 2022-10-28 14:05:53 -05:00
77 changed files with 3121 additions and 9512 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
@@ -1,14 +1,16 @@
 name: Build & Test
 on:
-  - push
-  - pull_request
+  push:
+  pull_request:
+  schedule:
+    - cron: '45 10 * * *'
 jobs:
  test:
    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
    runs-on: ${{ matrix.os }}
    strategy:
      matrix:
-        version: ['1.3', '1.4', '1.5', 'nightly']
+        version: ['1.6', '1.7', '1.8']
        os:
          - ubuntu-latest
        arch:
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,7 @@ instances/*.py
 notebooks
 .idea
 *.lp
+Manifest.toml
+data
+build
+benchmark
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,28 +1,60 @@
-# Version 0.5.0 (TBD)
+# 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
+
+# [0.6.0] -- 2022-12-15
+### Added
+- Allow RELOG to calculate approximate driving distances, instead of just straight-line distances between points.
+
+### Fixed
+- Fix bug that caused building period parameter to be ignored
+
+## [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

-# Version 0.4.0 (Sep 18, 2020)
-
+## [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=@.
-SRC_FILES := $(wildcard src/*.jl test/*.jl)
-VERSION := 0.5
-
-all: docs test
-
-build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
-	mkdir -p build
-	$(JULIA) src/sysimage.jl
-
-build/test.log: $(SRC_FILES) build/sysimage.so
-	cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl
+VERSION := 0.6

 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)/
+	
+format:
+	cd deps/formatter; ../../juliaw format.jl

-test: build/test.log
+test: test/Manifest.toml
+	./juliaw test/runtests.jl

-test-watch:
-	bash -c "while true; do make test --quiet; sleep 1; done"
+test/Manifest.toml: test/Project.toml
+	julia --project=test -e "using Pkg; Pkg.instantiate()"

-.PHONY: docs test
+.PHONY: docs test format
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,441 +0,0 @@
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-deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"]
-uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-
-[[PooledArrays]]
-deps = ["DataAPI"]
-git-tree-sha1 = "b1333d4eced1826e15adbdf01a4ecaccca9d353c"
-uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
-version = "0.5.3"
-
-[[Printf]]
-deps = ["Unicode"]
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-
-[[ProgressBars]]
-deps = ["Printf"]
-git-tree-sha1 = "e66732bbdaad368cfc642cef1f639df5812dc818"
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-version = "0.6.0"
-
-[[REPL]]
-deps = ["InteractiveUtils", "Markdown", "Sockets"]
-uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
-
-[[Random]]
-deps = ["Serialization"]
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-
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-
-[[SHA]]
-uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
-
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-version = "1.2.15"
-
-[[Serialization]]
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-
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-uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
-
-[[Sockets]]
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-
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-version = "0.3.1"
-
-[[SparseArrays]]
-deps = ["LinearAlgebra", "Random"]
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-
-[[SpecialFunctions]]
-deps = ["OpenSpecFun_jll"]
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-
-[[StaticArrays]]
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-version = "0.12.4"
-
-[[Statistics]]
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-
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-
-[[TableTraits]]
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-
-[[Tables]]
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-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 = "0.5.0"
+version = "0.6.0"

 [deps]
+CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
 Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
+Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
 GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
 Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
@@ -16,24 +18,31 @@ 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"
+NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
+Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"

 [compat]
-CSV = "0.7"
-Cbc = "0.6"
-Clp = "0.8"
-DataFrames = "0.21"
-DataStructures = "0.17"
+CRC = "4"
+CSV = "0.10"
+Cbc = "1"
+Clp = "1"
+DataFrames = "1"
+DataStructures = "0.18"
 GZip = "0.5"
-Geodesy = "0.5"
+Geodesy = "1"
 JSON = "0.21"
-JSONSchema = "0.2"
-JuMP = "0.21"
-MathOptInterface = "0.9"
-PackageCompiler = "1"
-ProgressBars = "0.6"
+JSONSchema = "1"
+JuMP = "1"
+MathOptInterface = "1"
+OrderedCollections = "1"
+ProgressBars = "1"
+Shapefile = "0.8"
+ZipFile = "0.10"
 julia = "1"
+NearestNeighbors = "0.4"
--- a/README.md
+++ b/README.md
@@ -15,19 +15,22 @@



-<img src="https://anl-ceeesa.github.io/RELOG/0.5/images/ex_transportation.png" width="1000px"/>
+<img src="https://anl-ceeesa.github.io/RELOG/0.6/assets/ex_transportation.png" width="1000px"/>

 ### Documentation

-  * [Usage](https://anl-ceeesa.github.io/RELOG/0.5/usage)
-  * [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.5/format)
-  * [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.5/reports)
-  * [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.5/model)
+  * [Usage](https://anl-ceeesa.github.io/RELOG/0.6/usage)
+  * [Input and Output Data Formats](https://anl-ceeesa.github.io/RELOG/0.6/format)
+  * [Simplified Solution Reports](https://anl-ceeesa.github.io/RELOG/0.6/reports)
+  * [Optimization Model](https://anl-ceeesa.github.io/RELOG/0.6/model)

 ### Authors

-* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
-* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
+* **Alinson S. Xavier** <<axavier@anl.gov>>
+* **Nwike Iloeje** <<ciloeje@anl.gov>>
+* **John Atkins**
+* **Kyle Sun**
+* **Audrey Gallier**

 ### 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
@@ -11,9 +11,10 @@ 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. |
+|`distance metric` | Metric used to compute distances between pairs of locations. Valid options are: `"Euclidean"`, for the straight-line distance between points; or `"driving"` for an approximated driving distance. If not specified, defaults to `"Euclidean"`.


 #### Example
@@ -21,7 +22,8 @@ The **parameters** section describes details about the simulation itself.
 {
    "parameters": {
        "time horizon (years)": 2,
-        "building period (years)": [1]
+        "building period (years)": [1],
+        "distance metric": "driving",
    }
 }
 ```
@@ -31,7 +33,7 @@ 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 +42,7 @@ 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 +95,7 @@ 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.
@@ -113,14 +115,14 @@ Each type of plant is associated with a set of potential locations where it can
 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.

@@ -128,7 +130,7 @@ 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.
@@ -182,14 +184,46 @@ 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.
 * Plants can be expanded at any time, even long after they are open.
 * All material available at the beginning of a time period must be entirely processed by the end of that time period. It is not possible to store unprocessed materials from one time period to the next.
 * Up to two plant sizes are currently supported. Variable operating costs must be the same for all plant sizes.
+* Accurate driving distances are only available for the continental United States.

 ## 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)
-  * [Input and Output Data Formats](format.md)
-  * [Simplified Solution Reports](reports.md)
-  * [Optimization Model](model.md)
+  ```@contents
+Pages = ["usage.md", "format.md", "reports.md", "model.md"]
+Depth = 3
+```
+
  
 ### Source Code

  * [https://github.com/ANL-CEEESA/RELOG](https://github.com/ANL-CEEESA/RELOG)

 ### Authors
-* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>>
-* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>>
+* **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
+* **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>

 ### License

--- a/docs/src/model.md
+++ b/docs/src/model.md
@@ -6,53 +6,65 @@ In this page, we describe the precise mathematical optimization model used by RE

 ### 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
+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:**
+#### Plants

-* $c^\text{disp}_{pmt}$ - Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ (`$/tonne/km`)
-* $c^\text{exp}_{pt}$ - Cost of adding one tonne of capacity to plant $p$ at time $t$ (`$/tonne`)
-* $c^\text{open}_{pt}$ - Cost of opening plant $p$ at time $t$, at minimum capacity (`$`)
-* $c^\text{f-base}_{pt}$ - Fixed cost of keeping plant $p$ open during time period $t$ (`$`)
-* $c^\text{f-exp}_{pt}$ - Increase in fixed cost for each additional tonne of capacity (`$/tonne`)
-* $c^\text{var}_{pt}$ - Variable cost of processing one tonne of input at plant $p$ at time $t$ (`$/tonne`)
-* $c^\text{store}_{pt}$ - Cost of storing one tonne of original material at plant $p$ at time $t$ (`$/tonne`)
-* $m^\text{min}_p$ - Minimum capacity of plant $p$ (`tonne`)
-* $m^\text{max}_p$ - Maximum capacity of plant $p$ (`tonne`)
-* $m^\text{disp}_{pmt}$ - Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ (`tonne`)
-* $m^\text{store}_p$ - Maximum amount of original material that plant $p$ can store for later processing.
+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:**
+#### 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`)
+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:**
+#### Transportation

-* $c^\text{tr}_{t}$ - Transportation cost during time $t$ (`$/tonne/km`)
-* $d_{lp}$ - Distance between plant $p$ and location $l$ (`km`)
+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
-* $q_{mpt}$ - Amount of material $m$ recovered by plant $p$ during time $t$ (`tonne`)
-* $u_{pt}$ - Binary variable that equals 1 if plant $p$ starts operating at time $t$ (`bool`)
-* $w_{pt}$ - Extra capacity (amount above the minimum) added to plant $p$ during time $t$ (`tonne`)
-* $x_{pt}$ - Binary variable that equals 1 if plant $p$ is operational at time $t$ (`bool`)
-* $y_{lpt}$ - Amount of product sent from location $l$ to plant $p$ during time $t$ (`tonne`)
-* $z^{\text{disp}}_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ (`tonne`)
-* $z^{\text{store}}_{pt}$ - Amount of original material in storage at plant $p$ by the end of time period $t$ (`tonne`)
-* $z^{\text{proc}}_{mpt}$ - Amount of original material processed by plant $p$ during time period $t$ (`tonne`)
+
+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[
@@ -73,6 +85,7 @@ RELOG minimizes the overall capital, production and transportation costs:
    &
        \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.
@@ -83,14 +96,17 @@ In the fourth line, we have the disposal costs.

 * All original materials must be sent to a plant:

-\begin{align}
+```math
+\begin{align*}
    & \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt} 
        & \forall l \in L, t \in T
-\end{align}
+\end{align*}
+```

 * Amount received equals amount processed plus stored. Furthermore, all original material should be processed by the end of the simulation.

-\begin{align}
+```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 \\
@@ -98,56 +114,70 @@ In the fourth line, we have the disposal costs.
        & \forall p \in P \\
    & z^{\text{store}}_{p,t^{\max}} = 0
        & \forall p \in P
-\end{align}
+\end{align*}
+```

 * Plants have a limited processing capacity. Furthermore, if a plant is closed, it has zero processing capacity:

-\begin{align}
+```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}
+\end{align*}
+```

 * Plants have limited storage capacity. Furthermore, if a plant is closed, is has zero storage capacity:

-\begin{align}
+```math
+\begin{align*}
    & z^{\text{store}}_{pt} \leq m^\text{store}_p x_p
        & \forall p \in P, t \in T
-\end{align}
+\end{align*}
+```

 * Plants can only be expanded up to their maximum capacity. Furthermore, if a plant is closed, it cannot be expanded:

-\begin{align}
+```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}
+\end{align*}
+```

 * Amount of recovered material is proportional to amount processed: 

-\begin{align}
+```math
+\begin{align*}
    & q_{mpt} = \alpha_{pm} z^{\text{proc}}_{pt}
        & \forall m \in M, p \in P, t \in T
-\end{align}
+\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}
+```math
+\begin{align*}
    & q_{mpt} = z_{mpt}
        & \forall m \in M, p \in P, t \in T
-\end{align}
+\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}
+```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}
+\end{align*}
+```


 * Variable bounds:

-\begin{align}
+```math
+\begin{align*}
    & q_{mpt} \geq 0
        & \forall m \in M, p \in P, t \in T \\
    & u_{pt} \in \{0,1\}
@@ -162,4 +192,5 @@ In the fourth line, we have the disposal costs.
        & 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}
+\end{align*}
+```
--- a/docs/src/reports.md
+++ b/docs/src/reports.md
@@ -6,15 +6,13 @@ 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.
-
-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).
+Report showing plant costs, capacities, energy expenditure and utilization factors. Generated by `RELOG.write_plants_report(solution, filename)`.

 | 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.
@@ -47,7 +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
@@ -67,21 +67,20 @@ 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.
-
-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).
+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)`.


 | 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 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.
@@ -105,17 +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.
-
-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).
+Report showing amount of emissions produced by each plant. Generated by `RELOG.write_plant_emissions_report(solution, filename)`.

 | Column                                | Description
-|:--------------------------------------|---------------|
+|:--------------------------------------|:---------------|
 | `plant type` | Plant type.
 | `location name` | Location name.
 | `year` | Year.
@@ -139,17 +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.
-
-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).
+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)`.


 | 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.
@@ -183,7 +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):

@@ -226,17 +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.
-
-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).
+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)`.

 | 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.
@@ -270,4 +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
@@ -3,22 +3,17 @@ 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:
+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:

-```text
-(@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git
+```julia
+using Pkg
+Pkg.add(name="RELOG", version="0.6")
 ```

 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
+```julia
+Pkg.test("RELOG")
 ```

 ## 2. Modeling the problem
@@ -66,25 +61,65 @@ 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. What-If Analysis

-### 4.1 Changing the solver
+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)
+gurobi = optimizer_with_attributes(
+    Gurobi.Optimizer,
+    "TimeLimit" => 3600,
+    "MIPGap" => 0.001,
+)

-RELOG.solve("instance.json",
-            output="solution.json",
-            optimizer=gurobi)
+RELOG.solve(
+    "instance.json",
+    output="solution.json",
+    optimizer=gurobi,
+)
 ```

-### 4.2 Multi-period heuristics
+### 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:

@@ -97,6 +132,8 @@ To solve an instance using this heuristic, use the option `heuristic=true`, as s
 ```julia
 using RELOG

-solution = RELOG.solve("/home/user/instance.json",
-                       heuristic=true)
+solution = RELOG.solve(
+    "/home/user/instance.json",
+    heuristic=true,
+)
 ```
--- a/instances/s1.json
+++ b/instances/s1.json
@@ -1,6 +1,7 @@
 {
    "parameters": {
-        "time horizon (years)": 2
+        "time horizon (years)": 2,
+        "distance metric": "driving"
    },
    "products": {
        "P1": {
--- 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("graph.jl")
-    include("model.jl")
-    include("reports.jl")
+
+include("instance/structs.jl")
+include("graph/structs.jl")
+
+include("instance/geodb.jl")
+include("graph/dist.jl")
+include("graph/build.jl")
+include("graph/csv.jl")
+include("instance/compress.jl")
+include("instance/parse.jl")
+include("instance/validate.jl")
+include("model/build.jl")
+include("model/getsol.jl")
+include("model/resolve.jl")
+include("model/solve.jl")
+include("reports/plant_emissions.jl")
+include("reports/plant_outputs.jl")
+include("reports/plants.jl")
+include("reports/products.jl")
+include("reports/tr_emissions.jl")
+include("reports/tr.jl")
+include("reports/write.jl")
 end
--- 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/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/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
@@ -2,71 +2,35 @@
 # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 # Released under the modified BSD license. See COPYING.md for more details.

-using Geodesy
-
-
-abstract type Node
-end
-
-
-mutable struct Arc
-    source::Node
-    dest::Node
-    values::Dict{String, Float64}
-end
-
-
-mutable struct ProcessNode <: Node
-    index::Int
-    location::Plant
-    incoming_arcs::Array{Arc}
-    outgoing_arcs::Array{Arc}
-end
-
-
-mutable struct ShippingNode <: Node
-    index::Int
-    location::Union{Plant, CollectionCenter}
-    product::Product
-    incoming_arcs::Array{Arc}
-    outgoing_arcs::Array{Arc}
-end
-
-
-mutable struct Graph
-    process_nodes::Array{ProcessNode}
-    plant_shipping_nodes::Array{ShippingNode}
-    collection_shipping_nodes::Array{ShippingNode}
-    arcs::Array{Arc}
-end
-
-
 function build_graph(instance::Instance)::Graph
    arcs = []
    next_index = 0
    process_nodes = ProcessNode[]
    plant_shipping_nodes = ShippingNode[]
    collection_shipping_nodes = ShippingNode[]
-    
-    process_nodes_by_input_product = Dict(product => ProcessNode[]
-                                          for product in instance.products)
-    shipping_nodes_by_plant = Dict(plant => []
-                                   for plant in instance.plants)
-    
+
+    name_to_process_node_map = Dict{Tuple{AbstractString,AbstractString},ProcessNode}()
+
+    process_nodes_by_input_product =
+        Dict(product => ProcessNode[] for product in instance.products)
+    shipping_nodes_by_plant = Dict(plant => [] for plant in instance.plants)
+
    # Build collection center shipping nodes
    for center in instance.collection_centers
        node = ShippingNode(next_index, center, center.product, [], [])
        next_index += 1
        push!(collection_shipping_nodes, node)
    end
-    
+
    # Build process and shipping nodes for plants
    for plant in instance.plants
        pn = ProcessNode(next_index, plant, [], [])
        next_index += 1
        push!(process_nodes, pn)
        push!(process_nodes_by_input_product[plant.input], pn)
-        
+
+        name_to_process_node_map[(plant.plant_name, plant.location_name)] = pn
+
        for product in keys(plant.output)
            sn = ShippingNode(next_index, plant, product, [], [])
            next_index += 1
@@ -74,14 +38,17 @@ function build_graph(instance::Instance)::Graph
            push!(shipping_nodes_by_plant[plant], sn)
        end
    end
-    
+
    # Build arcs from collection centers to plants, and from one plant to another
    for source in [collection_shipping_nodes; plant_shipping_nodes]
        for dest in process_nodes_by_input_product[source.product]
-            distance = calculate_distance(source.location.latitude,
-                                          source.location.longitude,
-                                          dest.location.latitude,
-                                          dest.location.longitude)
+            distance = _calculate_distance(
+                source.location.latitude,
+                source.location.longitude,
+                dest.location.latitude,
+                dest.location.longitude,
+                instance.distance_metric,
+            )
            values = Dict("distance" => distance)
            arc = Arc(source, dest, values)
            push!(source.outgoing_arcs, arc)
@@ -89,7 +56,7 @@ function build_graph(instance::Instance)::Graph
            push!(arcs, arc)
        end
    end
-    
+
    # Build arcs from process nodes to shipping nodes within a plant
    for source in process_nodes
        plant = source.location
@@ -102,25 +69,25 @@ function build_graph(instance::Instance)::Graph
            push!(arcs, arc)
        end
    end
-    
-    return Graph(process_nodes, 
-                 plant_shipping_nodes,
-                 collection_shipping_nodes,
-                 arcs)
+
+    return Graph(
+        process_nodes,
+        plant_shipping_nodes,
+        collection_shipping_nodes,
+        arcs,
+        name_to_process_node_map,
+    )
 end


-function to_csv(graph::Graph)
-    result = ""
-    for a in graph.arcs
-        result *= "$(a.source.index),$(a.dest.index)\n"
-    end
-    return result
-end
-
-
-function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64
-    x = LLA(source_lat, source_lon, 0.0)
-    y = LLA(dest_lat, dest_lon, 0.0)
-    return round(distance(x, y) / 1000.0, digits=2)
+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
--- 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/dist.jl
+++ b/src/graph/dist.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 Geodesy
+using NearestNeighbors
+using DataFrames
+
+function _calculate_distance(
+    source_lat,
+    source_lon,
+    dest_lat,
+    dest_lon,
+    ::EuclideanDistance,
+)::Float64
+    x = LLA(source_lat, source_lon, 0.0)
+    y = LLA(dest_lat, dest_lon, 0.0)
+    return round(euclidean_distance(x, y) / 1000.0, digits = 3)
+end
+
+function _calculate_distance(
+    source_lat,
+    source_lon,
+    dest_lat,
+    dest_lon,
+    metric::KnnDrivingDistance,
+)::Float64
+    if metric.tree === nothing
+        basedir = joinpath(dirname(@__FILE__), "..", "..", "data")
+        csv_filename = joinpath(basedir, "dist_driving.csv")
+
+        # Download pre-computed driving data
+        if !isfile(csv_filename)
+            _download_zip(
+                "https://axavier.org/RELOG/0.6/data/dist_driving_0b9a6ad6.zip",
+                basedir,
+                csv_filename,
+                0x0b9a6ad6,
+            )
+        end
+
+        # Fit kNN model
+        df = DataFrame(CSV.File(csv_filename))
+        coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
+        metric.ratios = Matrix(df[!, [:ratio]])
+        metric.tree = KDTree(coords)
+    end
+
+    # Compute Euclidean distance
+    dist_euclidean =
+        _calculate_distance(source_lat, source_lon, dest_lat, dest_lon, EuclideanDistance())
+
+    # Predict ratio
+    idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
+    ratio_pred = mean(metric.ratios[idxs])
+    return round(dist_euclidean * ratio_pred, digits = 3)
+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.jl
+++ b/src/instance.jl
@@ -1,281 +0,0 @@
-# RELOG: Reverse Logistics Optimization
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using DataStructures
-using JSON
-using JSONSchema
-using Printf
-using Statistics
-
-
-mutable struct Product
-    name::String
-    transportation_cost::Array{Float64}
-    transportation_energy::Array{Float64}
-    transportation_emissions::Dict{String, Array{Float64}}
-end
-
-
-mutable struct CollectionCenter
-    index::Int64
-    name::String
-    latitude::Float64
-    longitude::Float64
-    product::Product
-    amount::Array{Float64}
-end
-
-
-mutable struct PlantSize
-    capacity::Float64
-    variable_operating_cost::Array{Float64}
-    fixed_operating_cost::Array{Float64}
-    opening_cost::Array{Float64}
-end
-
-
-mutable struct Plant
-    index::Int64
-    plant_name::String
-    location_name::String
-    input::Product
-    output::Dict{Product, Float64}
-    latitude::Float64
-    longitude::Float64
-    disposal_limit::Dict{Product, Array{Float64}}
-    disposal_cost::Dict{Product, Array{Float64}}
-    sizes::Array{PlantSize}
-    energy::Array{Float64}
-    emissions::Dict{String, Array{Float64}}
-    storage_limit::Float64
-    storage_cost::Array{Float64}
-end
-
-
-mutable struct Instance
-    time::Int64
-    products::Array{Product, 1}
-    collection_centers::Array{CollectionCenter, 1}
-    plants::Array{Plant, 1}
-    building_period::Array{Int64}
-end
-
-
-function validate(json, schema)
-    result = JSONSchema.validate(json, schema)
-    if result !== nothing
-        if result isa JSONSchema.SingleIssue
-            path = join(result.path, " → ")
-            if length(path) == 0
-                path = "root"
-            end
-            msg = "$(result.msg) in $(path)"
-        else
-            msg = convert(String, result)
-        end
-        throw(msg)
-    end
-end
-
-
-function parsefile(path::String)::Instance
-    return RELOG.parse(JSON.parsefile(path))
-end
-
-
-function parse(json)::Instance
-    basedir = dirname(@__FILE__)
-    json_schema = JSON.parsefile("$basedir/schemas/input.json")
-    validate(json, Schema(json_schema))
-    
-    T = json["parameters"]["time horizon (years)"]
-    json_schema["definitions"]["TimeSeries"]["minItems"] = T
-    json_schema["definitions"]["TimeSeries"]["maxItems"] = T
-    validate(json, Schema(json_schema))
-    
-    building_period = [1]
-    if "building period (years)" in keys(json)
-        building_period = json["building period (years)"]
-    end
-    
-    plants = Plant[]
-    products = Product[]
-    collection_centers = CollectionCenter[]
-    prod_name_to_product = Dict{String, Product}()
-    
-    # Create products
-    for (product_name, product_dict) in json["products"]
-        cost = product_dict["transportation cost (\$/km/tonne)"]
-        energy = zeros(T)
-        emissions = Dict()
-        
-        if "transportation energy (J/km/tonne)" in keys(product_dict)
-            energy = product_dict["transportation energy (J/km/tonne)"]
-        end
-        
-        if "transportation emissions (tonne/km/tonne)" in keys(product_dict)
-            emissions = product_dict["transportation emissions (tonne/km/tonne)"]
-        end
-        
-        product = Product(product_name, cost, energy, emissions)
-        push!(products, product)
-        prod_name_to_product[product_name] = product
-        
-        # Create collection centers
-        if "initial amounts" in keys(product_dict)
-            for (center_name, center_dict) in product_dict["initial amounts"]
-                center = CollectionCenter(length(collection_centers) + 1,
-                                          center_name,
-                                          center_dict["latitude (deg)"],
-                                          center_dict["longitude (deg)"],
-                                          product,
-                                          center_dict["amount (tonne)"])
-                push!(collection_centers, center)
-            end
-        end
-    end
-    
-    # Create plants
-    for (plant_name, plant_dict) in json["plants"]
-        input = prod_name_to_product[plant_dict["input"]]
-        output = Dict()
-        
-        # Plant outputs
-        if "outputs (tonne/tonne)" in keys(plant_dict)
-            output = Dict(prod_name_to_product[key] => value
-                          for (key, value) in plant_dict["outputs (tonne/tonne)"]
-                          if value > 0)
-        end
-        
-        energy = zeros(T)
-        emissions = Dict()
-        
-        if "energy (GJ/tonne)" in keys(plant_dict)
-            energy = plant_dict["energy (GJ/tonne)"]
-        end
-        
-        if "emissions (tonne/tonne)" in keys(plant_dict)
-            emissions = plant_dict["emissions (tonne/tonne)"]
-        end
-        
-        for (location_name, location_dict) in plant_dict["locations"]
-            sizes = PlantSize[]
-            disposal_limit = Dict(p => [0.0 for t in 1:T] for p in keys(output))
-            disposal_cost = Dict(p => [0.0 for t in 1:T] for p in keys(output))
-            
-            # Disposal
-            if "disposal" in keys(location_dict)
-                for (product_name, disposal_dict) in location_dict["disposal"]
-                    limit = [1e8 for t in 1:T]
-                    if "limit (tonne)" in keys(disposal_dict)
-                       limit = disposal_dict["limit (tonne)"]
-                    end
-                    disposal_limit[prod_name_to_product[product_name]] = limit
-                    disposal_cost[prod_name_to_product[product_name]] = disposal_dict["cost (\$/tonne)"]
-                end
-            end
-            
-            # Capacities
-            for (capacity_name, capacity_dict) in location_dict["capacities (tonne)"]
-                push!(sizes, PlantSize(Base.parse(Float64, capacity_name),
-                                       capacity_dict["variable operating cost (\$/tonne)"],
-                                       capacity_dict["fixed operating cost (\$)"],
-                                       capacity_dict["opening cost (\$)"]))
-            end
-            length(sizes) > 1 ||  push!(sizes, sizes[1])
-            sort!(sizes, by = x -> x.capacity)
-            
-            # Storage
-            storage_limit = 0
-            storage_cost = zeros(T)
-            if "storage" in keys(location_dict)
-                storage_dict = location_dict["storage"]
-                storage_limit = storage_dict["limit (tonne)"]
-                storage_cost = storage_dict["cost (\$/tonne)"]
-            end
-            
-            # Validation: Capacities
-            if length(sizes) != 2
-                throw("At most two capacities are supported")
-            end
-            if sizes[1].variable_operating_cost != sizes[2].variable_operating_cost
-                throw("Variable operating costs must be the same for all capacities")
-            end
-
-            plant = Plant(length(plants) + 1,
-                          plant_name,
-                          location_name,
-                          input,
-                          output,
-                          location_dict["latitude (deg)"],
-                          location_dict["longitude (deg)"],
-                          disposal_limit,
-                          disposal_cost,
-                          sizes,
-                          energy,
-                          emissions,
-                          storage_limit,
-                          storage_cost)
-            
-            push!(plants, plant)
-        end
-    end
-    
-    @info @sprintf("%12d collection centers", length(collection_centers))
-    @info @sprintf("%12d candidate plant locations", length(plants))
-    
-    return Instance(T, products, collection_centers, plants, building_period)
-end
-
-
-"""
-    _compress(instance::Instance)
-
-Create a single-period instance from a multi-period one. Specifically,
-replaces every time-dependent attribute, such as initial_amounts,
-by a list with a single element, which is either a sum, an average,
-or something else that makes sense to that specific attribute.
-"""
-function _compress(instance::Instance)::Instance
-    T = instance.time
-    compressed = deepcopy(instance)
-    compressed.time = 1
-    compressed.building_period = [1]
-    
-    # Compress products
-    for p in compressed.products
-        p.transportation_cost = [mean(p.transportation_cost)]
-        p.transportation_energy = [mean(p.transportation_energy)]
-        for (emission_name, emission_value) in p.transportation_emissions
-            p.transportation_emissions[emission_name] = [mean(emission_value)]
-        end
-    end
-    
-    # Compress collection centers
-    for c in compressed.collection_centers
-        c.amount = [maximum(c.amount) * T]
-    end
-    
-    # Compress plants
-    for plant in compressed.plants
-        plant.energy = [mean(plant.energy)]
-        for (emission_name, emission_value) in plant.emissions
-            plant.emissions[emission_name] = [mean(emission_value)]
-        end
-        for s in plant.sizes
-            s.capacity *= T
-            s.variable_operating_cost = [mean(s.variable_operating_cost)]
-            s.opening_cost = [s.opening_cost[1]]
-            s.fixed_operating_cost = [sum(s.fixed_operating_cost)]
-        end
-        for (prod_name, disp_limit) in plant.disposal_limit
-            plant.disposal_limit[prod_name] = [sum(disp_limit)]
-        end
-        for (prod_name, disp_cost) in plant.disposal_cost
-            plant.disposal_cost[prod_name] = [mean(disp_cost)]
-        end
-    end
-    
-    return compressed
-end
--- 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: $db_name")
+end
+
+function geodb_query(name)::GeoRegion
+    db_name, id = split(name, ":")
+    return geodb_load(db_name)[id]
+end
--- a/src/instance/parse.jl
+++ b/src/instance/parse.jl
@@ -0,0 +1,199 @@
+# RELOG: Reverse Logistics Optimization
+# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using DataStructures
+using JSON
+using JSONSchema
+using Printf
+using Statistics
+
+function parsefile(path::String)::Instance
+    return RELOG.parse(JSON.parsefile(path))
+end
+
+function parse(json)::Instance
+    basedir = dirname(@__FILE__)
+    json_schema = JSON.parsefile("$basedir/../schemas/input.json")
+    validate(json, Schema(json_schema))
+
+    T = json["parameters"]["time horizon (years)"]
+    json_schema["definitions"]["TimeSeries"]["minItems"] = T
+    json_schema["definitions"]["TimeSeries"]["maxItems"] = T
+    validate(json, Schema(json_schema))
+
+    building_period = [1]
+    if "building period (years)" in keys(json["parameters"])
+        building_period = json["parameters"]["building period (years)"]
+    end
+
+    distance_metric = EuclideanDistance()
+    if "distance metric" in keys(json["parameters"])
+        metric_name = json["parameters"]["distance metric"]
+        if metric_name == "driving"
+            distance_metric = KnnDrivingDistance()
+        elseif metric_name == "Euclidean"
+            # nop
+        else
+            error("Unknown distance metric: $metric_name")
+        end
+    end
+
+    plants = Plant[]
+    products = Product[]
+    collection_centers = CollectionCenter[]
+    prod_name_to_product = Dict{String,Product}()
+
+    # Create products
+    for (product_name, product_dict) in json["products"]
+        cost = product_dict["transportation cost (\$/km/tonne)"]
+        energy = zeros(T)
+        emissions = Dict()
+
+        if "transportation energy (J/km/tonne)" in keys(product_dict)
+            energy = product_dict["transportation energy (J/km/tonne)"]
+        end
+
+        if "transportation emissions (tonne/km/tonne)" in keys(product_dict)
+            emissions = product_dict["transportation emissions (tonne/km/tonne)"]
+        end
+
+        product = Product(product_name, cost, energy, emissions)
+        push!(products, product)
+        prod_name_to_product[product_name] = product
+
+        # Create collection centers
+        if "initial amounts" in keys(product_dict)
+            for (center_name, center_dict) in product_dict["initial amounts"]
+                if "location" in keys(center_dict)
+                    region = geodb_query(center_dict["location"])
+                    center_dict["latitude (deg)"] = region.centroid.lat
+                    center_dict["longitude (deg)"] = region.centroid.lon
+                end
+                center = CollectionCenter(
+                    length(collection_centers) + 1,
+                    center_name,
+                    center_dict["latitude (deg)"],
+                    center_dict["longitude (deg)"],
+                    product,
+                    center_dict["amount (tonne)"],
+                )
+                push!(collection_centers, center)
+            end
+        end
+    end
+
+    # Create plants
+    for (plant_name, plant_dict) in json["plants"]
+        input = prod_name_to_product[plant_dict["input"]]
+        output = Dict()
+
+        # Plant outputs
+        if "outputs (tonne/tonne)" in keys(plant_dict)
+            output = Dict(
+                prod_name_to_product[key] => value for
+                (key, value) in plant_dict["outputs (tonne/tonne)"] if value > 0
+            )
+        end
+
+        energy = zeros(T)
+        emissions = Dict()
+
+        if "energy (GJ/tonne)" in keys(plant_dict)
+            energy = plant_dict["energy (GJ/tonne)"]
+        end
+
+        if "emissions (tonne/tonne)" in keys(plant_dict)
+            emissions = plant_dict["emissions (tonne/tonne)"]
+        end
+
+        for (location_name, location_dict) in plant_dict["locations"]
+            sizes = PlantSize[]
+            disposal_limit = Dict(p => [0.0 for t = 1:T] for p in keys(output))
+            disposal_cost = Dict(p => [0.0 for t = 1:T] for p in keys(output))
+
+            # GeoDB
+            if "location" in keys(location_dict)
+                region = geodb_query(location_dict["location"])
+                location_dict["latitude (deg)"] = region.centroid.lat
+                location_dict["longitude (deg)"] = region.centroid.lon
+            end
+
+            # Disposal
+            if "disposal" in keys(location_dict)
+                for (product_name, disposal_dict) in location_dict["disposal"]
+                    limit = [1e8 for t = 1:T]
+                    if "limit (tonne)" in keys(disposal_dict)
+                        limit = disposal_dict["limit (tonne)"]
+                    end
+                    disposal_limit[prod_name_to_product[product_name]] = limit
+                    disposal_cost[prod_name_to_product[product_name]] =
+                        disposal_dict["cost (\$/tonne)"]
+                end
+            end
+
+            # Capacities
+            for (capacity_name, capacity_dict) in location_dict["capacities (tonne)"]
+                push!(
+                    sizes,
+                    PlantSize(
+                        Base.parse(Float64, capacity_name),
+                        capacity_dict["variable operating cost (\$/tonne)"],
+                        capacity_dict["fixed operating cost (\$)"],
+                        capacity_dict["opening cost (\$)"],
+                    ),
+                )
+            end
+            length(sizes) > 1 || push!(sizes, sizes[1])
+            sort!(sizes, by = x -> x.capacity)
+
+            # Storage
+            storage_limit = 0
+            storage_cost = zeros(T)
+            if "storage" in keys(location_dict)
+                storage_dict = location_dict["storage"]
+                storage_limit = storage_dict["limit (tonne)"]
+                storage_cost = storage_dict["cost (\$/tonne)"]
+            end
+
+            # Validation: Capacities
+            if length(sizes) != 2
+                throw("At most two capacities are supported")
+            end
+            if sizes[1].variable_operating_cost != sizes[2].variable_operating_cost
+                throw("Variable operating costs must be the same for all capacities")
+            end
+
+            plant = Plant(
+                length(plants) + 1,
+                plant_name,
+                location_name,
+                input,
+                output,
+                location_dict["latitude (deg)"],
+                location_dict["longitude (deg)"],
+                disposal_limit,
+                disposal_cost,
+                sizes,
+                energy,
+                emissions,
+                storage_limit,
+                storage_cost,
+            )
+
+            push!(plants, plant)
+        end
+    end
+
+    @info @sprintf("%12d collection centers", length(collection_centers))
+    @info @sprintf("%12d candidate plant locations", length(plants))
+
+    return Instance(
+        T,
+        products,
+        collection_centers,
+        plants,
+        building_period,
+        distance_metric,
+    )
+end
--- a/src/instance/structs.jl
+++ b/src/instance/structs.jl
@@ -0,0 +1,68 @@
+# 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
+
+
+abstract type DistanceMetric end
+
+Base.@kwdef mutable struct KnnDrivingDistance <: DistanceMetric
+    tree = nothing
+    ratios = nothing
+end
+
+mutable struct EuclideanDistance <: DistanceMetric end
+
+mutable struct Instance
+    time::Int64
+    products::Vector{Product}
+    collection_centers::Vector{CollectionCenter}
+    plants::Vector{Plant}
+    building_period::Vector{Int64}
+    distance_metric::DistanceMetric
+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
@@ -1,535 +0,0 @@
-# RELOG: Reverse Logistics Optimization
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
-
-
-mutable struct ManufacturingModel
-    mip::JuMP.Model
-    vars::DotDict
-    eqs::DotDict
-    instance::Instance
-    graph::Graph
-end
-
-
-function build_model(instance::Instance, graph::Graph, optimizer)::ManufacturingModel
-    model = ManufacturingModel(Model(optimizer), DotDict(), DotDict(), instance, graph)
-    create_vars!(model)
-    create_objective_function!(model)
-    create_shipping_node_constraints!(model)
-    create_process_node_constraints!(model)
-    return model
-end
-
-
-function create_vars!(model::ManufacturingModel)
-    mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
-    
-    vars.flow = Dict((a, t) => @variable(mip, lower_bound=0)
-                    for a in graph.arcs, t in 1:T)
-   
-    vars.dispose = Dict((n, t) => @variable(mip,
-                                            lower_bound=0,
-                                            upper_bound=n.location.disposal_limit[n.product][t])
-                        for n in values(graph.plant_shipping_nodes), t in 1:T)
-    
-    vars.store = Dict((n, t) => @variable(mip,
-                                            lower_bound=0,
-                                            upper_bound=n.location.storage_limit)
-                        for n in values(graph.process_nodes), t in 1:T)
-    
-    vars.process = Dict((n, t) => @variable(mip,
-                                            lower_bound = 0)
-                        for n in values(graph.process_nodes), t in 1:T)
-    
-    vars.open_plant = Dict((n, t) => @variable(mip, binary=true)
-                           for n in values(graph.process_nodes), t in 1:T)
-
-    vars.is_open = Dict((n, t) => @variable(mip, binary=true)
-                        for n in values(graph.process_nodes), t in 1:T)
-
-    vars.capacity = Dict((n, t) => @variable(mip,
-                                             lower_bound = 0,
-                                             upper_bound = n.location.sizes[2].capacity)
-                         for n in values(graph.process_nodes), t in 1:T)
-    
-    vars.expansion = Dict((n, t) => @variable(mip,
-                                              lower_bound = 0,
-                                              upper_bound = n.location.sizes[2].capacity - 
-                                                            n.location.sizes[1].capacity)
-                         for n in values(graph.process_nodes), t in 1:T)
-end
-
-
-function slope_open(plant, t)
-    if plant.sizes[2].capacity <= plant.sizes[1].capacity
-        0.0
-    else
-        (plant.sizes[2].opening_cost[t] - plant.sizes[1].opening_cost[t]) /
-            (plant.sizes[2].capacity - plant.sizes[1].capacity)
-    end
-end
-
-function slope_fix_oper_cost(plant, t)
-    if plant.sizes[2].capacity <= plant.sizes[1].capacity
-        0.0
-    else
-        (plant.sizes[2].fixed_operating_cost[t] - plant.sizes[1].fixed_operating_cost[t]) /
-            (plant.sizes[2].capacity - plant.sizes[1].capacity)
-    end
-end
-
-function create_objective_function!(model::ManufacturingModel)
-    mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
-    obj = AffExpr(0.0)
-
-    # Process node costs
-    for n in values(graph.process_nodes), t in 1:T
-        
-        # Transportation and variable operating costs
-        for a in n.incoming_arcs
-            c = n.location.input.transportation_cost[t] * a.values["distance"]
-            add_to_expression!(obj, c, vars.flow[a, t])
-        end
-        
-        # Opening costs
-        add_to_expression!(obj,
-                           n.location.sizes[1].opening_cost[t],
-                           vars.open_plant[n, t])
-        
-        # Fixed operating costs (base)
-        add_to_expression!(obj,
-                           n.location.sizes[1].fixed_operating_cost[t],
-                           vars.is_open[n, t])
-        
-        # Fixed operating costs (expansion)
-        add_to_expression!(obj,
-                           slope_fix_oper_cost(n.location, t),
-                           vars.expansion[n, t])
-        
-        # Processing costs
-        add_to_expression!(obj,
-                           n.location.sizes[1].variable_operating_cost[t],
-                           vars.process[n, t])
-        
-        # Storage costs
-        add_to_expression!(obj,
-                           n.location.storage_cost[t],
-                           vars.store[n, t])
-        
-        # Expansion costs
-        if t < T
-            add_to_expression!(obj,
-                               slope_open(n.location, t) - slope_open(n.location, t + 1),
-                               vars.expansion[n, t])
-        else
-            add_to_expression!(obj,
-                               slope_open(n.location, t),
-                               vars.expansion[n, t])
-        end
-    end
-
-    # Shipping node costs
-    for n in values(graph.plant_shipping_nodes), t in 1:T
-        
-        # Disposal costs
-        add_to_expression!(obj,
-                           n.location.disposal_cost[n.product][t],
-                           vars.dispose[n, t])
-    end
-
-    @objective(mip, Min, obj)
-end    
-
-
-function create_shipping_node_constraints!(model::ManufacturingModel)
-    mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
-    eqs = model.eqs
-    
-    eqs.balance = OrderedDict()
-    
-    for t in 1:T
-        # Collection centers
-        for n in graph.collection_shipping_nodes
-            eqs.balance[n, t] = @constraint(mip,
-                                            sum(vars.flow[a, t] for a in n.outgoing_arcs)
-                                              == n.location.amount[t])
-        end
-
-        # Plants
-        for n in graph.plant_shipping_nodes
-            @constraint(mip,
-                sum(vars.flow[a, t] for a in n.incoming_arcs) ==
-                sum(vars.flow[a, t] for a in n.outgoing_arcs) + vars.dispose[n, t])
-        end
-    end
-    
-end
-
-
-function create_process_node_constraints!(model::ManufacturingModel)
-    mip, vars, graph, T = model.mip, model.vars, model.graph, model.instance.time
-
-    for t in 1:T, n in graph.process_nodes
-        input_sum = AffExpr(0.0)
-        for a in n.incoming_arcs
-            add_to_expression!(input_sum, 1.0, vars.flow[a, t])
-        end
-        
-        # Output amount is implied by amount processed
-        for a in n.outgoing_arcs
-            @constraint(mip, vars.flow[a, t] == a.values["weight"] * vars.process[n, t])
-        end
-        
-        # If plant is closed, capacity is zero
-        @constraint(mip, vars.capacity[n, t] <= n.location.sizes[2].capacity * vars.is_open[n, t])
-
-        # If plant is open, capacity is greater than base
-        @constraint(mip, vars.capacity[n, t] >= n.location.sizes[1].capacity * vars.is_open[n, t])
-
-        # Capacity is linked to expansion
-        @constraint(mip, vars.capacity[n, t] <= n.location.sizes[1].capacity + vars.expansion[n, t])
-
-        # Can only process up to capacity
-        @constraint(mip, vars.process[n, t] <= vars.capacity[n, t])
-
-        if t > 1
-            # Plant capacity can only increase over time
-            @constraint(mip, vars.capacity[n, t] >= vars.capacity[n, t-1])
-            @constraint(mip, vars.expansion[n, t] >= vars.expansion[n, t-1])
-        end
-        
-        # Amount received equals amount processed plus stored
-        store_in = 0
-        if t > 1
-            store_in = vars.store[n, t-1]
-        end
-        if t == T
-            @constraint(mip, vars.store[n, t] == 0)
-        end
-        @constraint(mip,
-                    input_sum + store_in == vars.store[n, t] + vars.process[n, t])
-        
-
-        # Plant is currently open if it was already open in the previous time period or
-        # if it was built just now
-        if t > 1
-            @constraint(mip, vars.is_open[n, t] == vars.is_open[n, t-1] + vars.open_plant[n, t])
-        else
-            @constraint(mip, vars.is_open[n, t] == vars.open_plant[n, t])
-        end
-        
-        # Plant can only be opened during building period
-        if t ∉ model.instance.building_period
-            @constraint(mip, vars.open_plant[n, t] == 0)
-        end
-    end
-end
-
-default_milp_optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-default_lp_optimizer = optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
-
-function solve(instance::Instance;
-               optimizer=nothing,
-               output=nothing,
-               marginal_costs=true,
-              )
-    
-    milp_optimizer = lp_optimizer = optimizer
-    if optimizer == nothing
-        milp_optimizer = default_milp_optimizer
-        lp_optimizer = default_lp_optimizer
-    end
-    
-    @info "Building graph..."
-    graph = RELOG.build_graph(instance)
-    @info @sprintf("    %12d time periods", instance.time)
-    @info @sprintf("    %12d process nodes", length(graph.process_nodes))
-    @info @sprintf("    %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
-    @info @sprintf("    %12d shipping nodes (collection)", length(graph.collection_shipping_nodes))
-    @info @sprintf("    %12d arcs", length(graph.arcs))
-    
-    @info "Building optimization model..."
-    model = RELOG.build_model(instance, graph, milp_optimizer)
-    
-    @info "Optimizing MILP..."
-    JuMP.optimize!(model.mip)
-    
-    if !has_values(model.mip)
-        @warn "No solution available"
-        return OrderedDict()
-    end
-    
-    if marginal_costs
-        @info "Re-optimizing with integer variables fixed..."
-        all_vars = JuMP.all_variables(model.mip)
-        vals = OrderedDict(var => JuMP.value(var) for var in all_vars)
-        JuMP.set_optimizer(model.mip, lp_optimizer)
-        for var in all_vars
-            if JuMP.is_binary(var)
-                JuMP.unset_binary(var)
-                JuMP.fix(var, vals[var])
-            end
-        end
-        JuMP.optimize!(model.mip)
-    end
-    
-    @info "Extracting solution..."
-    solution = get_solution(model, marginal_costs=marginal_costs)
-    
-    if output != nothing
-        write(solution, output)
-    end
-    
-    return solution
-end
-
-function solve(filename::AbstractString;
-               heuristic=false,
-               kwargs...,
-              )
-    @info "Reading $filename..."
-    instance = RELOG.parsefile(filename)
-    if heuristic && instance.time > 1
-        @info "Solving single-period version..."
-        compressed = _compress(instance)
-        csol = solve(compressed;
-                     output=nothing,
-                     marginal_costs=false,
-                     kwargs...)
-        @info "Filtering candidate locations..."
-        selected_pairs = []
-        for (plant_name, plant_dict) in csol["Plants"]
-            for (location_name, location_dict) in plant_dict
-                push!(selected_pairs, (plant_name, location_name))
-            end
-        end
-        filtered_plants = []
-        for p in instance.plants
-            if (p.plant_name, p.location_name) in selected_pairs
-                push!(filtered_plants, p)
-            end
-        end
-        instance.plants = filtered_plants
-        @info "Solving original version..."
-    end
-    sol = solve(instance; kwargs...)
-    return sol
-end
-
-
-function get_solution(model::ManufacturingModel;
-                      marginal_costs=true,
-                     )
-    mip, vars, eqs, graph, instance = model.mip, model.vars, model.eqs, model.graph, model.instance
-    T = instance.time
-    
-    output = OrderedDict(
-        "Plants" => OrderedDict(),
-        "Products" => OrderedDict(),
-        "Costs" => OrderedDict(
-            "Fixed operating (\$)" => zeros(T),
-            "Variable operating (\$)" => zeros(T),
-            "Opening (\$)" => zeros(T),
-            "Transportation (\$)" => zeros(T),
-            "Disposal (\$)" => zeros(T),
-            "Expansion (\$)" => zeros(T),
-            "Storage (\$)" => zeros(T),
-            "Total (\$)" => zeros(T),
-        ),
-        "Energy" => OrderedDict(
-            "Plants (GJ)" => zeros(T),
-            "Transportation (GJ)" => zeros(T),
-        ),
-        "Emissions" => OrderedDict(
-            "Plants (tonne)" => OrderedDict(),
-            "Transportation (tonne)" => OrderedDict(),
-        ),
-    )
-    
-    plant_to_process_node = OrderedDict(n.location => n for n in graph.process_nodes)
-    plant_to_shipping_nodes = OrderedDict()
-    for p in instance.plants
-        plant_to_shipping_nodes[p] = []
-        for a in plant_to_process_node[p].outgoing_arcs
-            push!(plant_to_shipping_nodes[p], a.dest)
-        end
-    end
-    
-    # Products
-    if marginal_costs
-        for n in graph.collection_shipping_nodes
-            location_dict = OrderedDict{Any, Any}(
-                "Marginal cost (\$/tonne)" => [round(abs(JuMP.shadow_price(eqs.balance[n, t])), digits=2)
-                                               for t in 1:T]
-            )
-            if n.product.name ∉ keys(output["Products"])
-                output["Products"][n.product.name] = OrderedDict()
-            end
-            output["Products"][n.product.name][n.location.name] = location_dict
-        end
-    end
-    
-    # Plants
-    for plant in instance.plants
-        skip_plant = true
-        process_node = plant_to_process_node[plant]
-        plant_dict = OrderedDict{Any, Any}(
-            "Input" => OrderedDict(),
-            "Output" => OrderedDict(
-                "Send" => OrderedDict(),
-                "Dispose" => OrderedDict(),
-            ),
-            "Input product" => plant.input.name,
-            "Total input (tonne)" => [0.0 for t in 1:T],
-            "Total output" => OrderedDict(),
-            "Latitude (deg)" => plant.latitude,
-            "Longitude (deg)" => plant.longitude,
-            "Capacity (tonne)" => [JuMP.value(vars.capacity[process_node, t])
-                                   for t in 1:T],
-            "Opening cost (\$)" => [JuMP.value(vars.open_plant[process_node, t]) *
-                                    plant.sizes[1].opening_cost[t]
-                                    for t in 1:T],
-            "Fixed operating cost (\$)" => [JuMP.value(vars.is_open[process_node, t]) *
-                                            plant.sizes[1].fixed_operating_cost[t] +
-                                            JuMP.value(vars.expansion[process_node, t]) *
-                                            slope_fix_oper_cost(plant, t)
-                                            for t in 1:T],
-            "Expansion cost (\$)" => [(if t == 1
-                                          slope_open(plant, t) * JuMP.value(vars.expansion[process_node, t])
-                                       else
-                                          slope_open(plant, t) * (
-                                              JuMP.value(vars.expansion[process_node, t]) -
-                                              JuMP.value(vars.expansion[process_node, t - 1])
-                                          )
-                                       end)
-                                      for t in 1:T],
-            "Process (tonne)" => [JuMP.value(vars.process[process_node, t])
-                                  for t in 1:T],
-            "Variable operating cost (\$)" => [JuMP.value(vars.process[process_node, t]) *
-                                               plant.sizes[1].variable_operating_cost[t]
-                                               for t in 1:T],
-            "Storage (tonne)" => [JuMP.value(vars.store[process_node, t])
-                                for t in 1:T],
-            "Storage cost (\$)" => [JuMP.value(vars.store[process_node, t]) *
-                                    plant.storage_cost[t]
-                                    for t in 1:T],
-        )
-        output["Costs"]["Fixed operating (\$)"] += plant_dict["Fixed operating cost (\$)"]
-        output["Costs"]["Variable operating (\$)"] += plant_dict["Variable operating cost (\$)"]
-        output["Costs"]["Opening (\$)"] += plant_dict["Opening cost (\$)"]
-        output["Costs"]["Expansion (\$)"] += plant_dict["Expansion cost (\$)"]
-        output["Costs"]["Storage (\$)"] += plant_dict["Storage cost (\$)"]
-
-        # Inputs
-        for a in process_node.incoming_arcs
-            vals = [JuMP.value(vars.flow[a, t]) for t in 1:T]
-            if sum(vals) <= 1e-3
-                continue
-            end
-            skip_plant = false
-            dict = OrderedDict{Any, Any}(
-                "Amount (tonne)" => vals,
-                "Distance (km)" => a.values["distance"],
-                "Latitude (deg)" => a.source.location.latitude,
-                "Longitude (deg)" => a.source.location.longitude,
-                "Transportation cost (\$)" => a.source.product.transportation_cost .*
-                                              vals .*
-                                              a.values["distance"],
-                "Transportation energy (J)" => vals .*
-                                               a.values["distance"] .*
-                                               a.source.product.transportation_energy,
-                "Emissions (tonne)" => OrderedDict(),
-            )
-            emissions_dict = output["Emissions"]["Transportation (tonne)"]
-            for (em_name, em_values) in a.source.product.transportation_emissions
-                dict["Emissions (tonne)"][em_name] = em_values .*
-                                                     dict["Amount (tonne)"] .*
-                                                     a.values["distance"]
-                if em_name ∉ keys(emissions_dict)
-                    emissions_dict[em_name] = zeros(T)
-                end
-                emissions_dict[em_name] += dict["Emissions (tonne)"][em_name]
-            end
-            if a.source.location isa CollectionCenter
-                plant_name = "Origin"
-                location_name = a.source.location.name
-            else
-                plant_name = a.source.location.plant_name
-                location_name = a.source.location.location_name
-            end
-            
-            if plant_name ∉ keys(plant_dict["Input"])
-                plant_dict["Input"][plant_name] = OrderedDict()
-            end
-            plant_dict["Input"][plant_name][location_name] = dict
-            plant_dict["Total input (tonne)"] += vals
-            output["Costs"]["Transportation (\$)"] += dict["Transportation cost (\$)"]
-            output["Energy"]["Transportation (GJ)"] += dict["Transportation energy (J)"] / 1e9
-        end
-        
-        plant_dict["Energy (GJ)"] = plant_dict["Total input (tonne)"] .* plant.energy
-        output["Energy"]["Plants (GJ)"] += plant_dict["Energy (GJ)"]
-        
-        plant_dict["Emissions (tonne)"] = OrderedDict()
-        emissions_dict = output["Emissions"]["Plants (tonne)"]
-        for (em_name, em_values) in plant.emissions
-            plant_dict["Emissions (tonne)"][em_name] = em_values .* plant_dict["Total input (tonne)"]
-            if em_name ∉ keys(emissions_dict)
-                emissions_dict[em_name] = zeros(T)
-            end
-            emissions_dict[em_name] += plant_dict["Emissions (tonne)"][em_name]
-        end
-
-        # Outputs
-        for shipping_node in plant_to_shipping_nodes[plant]
-            product_name = shipping_node.product.name
-            plant_dict["Total output"][product_name] = zeros(T)
-            plant_dict["Output"]["Send"][product_name] = product_dict = OrderedDict()
-
-            disposal_amount = [JuMP.value(vars.dispose[shipping_node, t]) for t in 1:T]
-            if sum(disposal_amount) > 1e-5
-                skip_plant = false
-                plant_dict["Output"]["Dispose"][product_name] = disposal_dict = OrderedDict()
-                disposal_dict["Amount (tonne)"] = [JuMP.value(model.vars.dispose[shipping_node, t])
-                                                   for t in 1:T]
-                disposal_dict["Cost (\$)"] = [disposal_dict["Amount (tonne)"][t] *
-                                              plant.disposal_cost[shipping_node.product][t]
-                                              for t in 1:T]
-                plant_dict["Total output"][product_name] += disposal_amount
-                output["Costs"]["Disposal (\$)"] += disposal_dict["Cost (\$)"]
-            end
-
-            for a in shipping_node.outgoing_arcs
-                vals = [JuMP.value(vars.flow[a, t]) for t in 1:T]
-                if sum(vals) <= 1e-3
-                    continue
-                end
-                skip_plant = false
-                dict = OrderedDict(
-                    "Amount (tonne)" => vals,
-                    "Distance (km)" => a.values["distance"],
-                    "Latitude (deg)" => a.dest.location.latitude,
-                    "Longitude (deg)" => a.dest.location.longitude,
-                )
-                if a.dest.location.plant_name ∉ keys(product_dict)
-                    product_dict[a.dest.location.plant_name] = OrderedDict()
-                end
-                product_dict[a.dest.location.plant_name][a.dest.location.location_name] = dict
-                plant_dict["Total output"][product_name] += vals
-            end
-        end
-            
-        if !skip_plant
-            if plant.plant_name ∉ keys(output["Plants"])
-                output["Plants"][plant.plant_name] = OrderedDict()
-            end
-            output["Plants"][plant.plant_name][plant.location_name] = plant_dict
-        end
-    end
-
-    output["Costs"]["Total (\$)"] = sum(values(output["Costs"]))
-    return output
-end
--- 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
@@ -1,278 +0,0 @@
-# RELOG: Reverse Logistics Optimization
-# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
-# Released under the modified BSD license. See COPYING.md for more details.
-
-using DataFrames
-using CSV
-
-function plants_report(solution)::DataFrame
-    df = DataFrame()
-    df."plant type" = String[]
-    df."location name" = String[]
-    df."year" = Int[]
-    df."latitude (deg)" = Float64[]
-    df."longitude (deg)" = Float64[]
-    df."capacity (tonne)" = Float64[]
-    df."amount processed (tonne)" = Float64[]
-    df."amount received (tonne)" = Float64[]
-    df."amount in storage (tonne)" = Float64[]
-    df."utilization factor (%)" = Float64[]
-    df."energy (GJ)" = Float64[]
-    df."opening cost (\$)" = Float64[]
-    df."expansion cost (\$)" = Float64[]
-    df."fixed operating cost (\$)" = Float64[]
-    df."variable operating cost (\$)" = Float64[]
-    df."storage cost (\$)" = Float64[]
-    df."total cost (\$)" = Float64[]
-    T = length(solution["Energy"]["Plants (GJ)"])
-    for (plant_name, plant_dict) in solution["Plants"]
-        for (location_name, location_dict) in plant_dict
-            for year in 1:T
-                capacity = round(location_dict["Capacity (tonne)"][year], digits=2)
-                received = round(location_dict["Total input (tonne)"][year], digits=2)
-                processed = round(location_dict["Process (tonne)"][year], digits=2)
-                in_storage = round(location_dict["Storage (tonne)"][year], digits=2)
-                utilization_factor = round(processed / capacity * 100.0, digits=2)
-                energy = round(location_dict["Energy (GJ)"][year], digits=2)
-                latitude = round(location_dict["Latitude (deg)"], digits=6)
-                longitude = round(location_dict["Longitude (deg)"], digits=6)
-                opening_cost = round(location_dict["Opening cost (\$)"][year], digits=2)
-                expansion_cost = round(location_dict["Expansion cost (\$)"][year], digits=2)
-                fixed_cost = round(location_dict["Fixed operating cost (\$)"][year], digits=2)
-                var_cost = round(location_dict["Variable operating cost (\$)"][year], digits=2)
-                storage_cost = round(location_dict["Storage cost (\$)"][year], digits=2)
-                total_cost = round(opening_cost + expansion_cost + fixed_cost +
-                                   var_cost + storage_cost, digits=2)
-                push!(df, [
-                    plant_name,
-                    location_name,
-                    year,
-                    latitude,
-                    longitude,
-                    capacity,
-                    processed,
-                    received,
-                    in_storage,
-                    utilization_factor,
-                    energy,
-                    opening_cost,
-                    expansion_cost,
-                    fixed_cost,
-                    var_cost,
-                    storage_cost,
-                    total_cost,
-                ])
-            end
-        end
-    end
-    return df
-end
-
-function plant_outputs_report(solution)::DataFrame
-    df = DataFrame()
-    df."plant type" = String[]
-    df."location name" = String[]
-    df."year" = Int[]
-    df."product name" = String[]
-    df."amount produced (tonne)" = Float64[]
-    df."amount sent (tonne)" = Float64[]
-    df."amount disposed (tonne)" = Float64[]
-    df."disposal cost (\$)" = Float64[]
-    T = length(solution["Energy"]["Plants (GJ)"])
-    for (plant_name, plant_dict) in solution["Plants"]
-        for (location_name, location_dict) in plant_dict
-            for (product_name, amount_produced) in location_dict["Total output"]
-                send_dict = location_dict["Output"]["Send"]
-                disposal_dict = location_dict["Output"]["Dispose"]
-                
-                sent = zeros(T)
-                if product_name in keys(send_dict)
-                    for (dst_plant_name, dst_plant_dict) in send_dict[product_name]
-                        for (dst_location_name, dst_location_dict) in dst_plant_dict
-                            sent += dst_location_dict["Amount (tonne)"]
-                        end
-                    end
-                end
-                sent = round.(sent, digits=2)
-                
-                disposal_amount = zeros(T)
-                disposal_cost = zeros(T)
-                if product_name in keys(disposal_dict)
-                    disposal_amount += disposal_dict[product_name]["Amount (tonne)"]
-                    disposal_cost += disposal_dict[product_name]["Cost (\$)"]
-                end
-                disposal_amount = round.(disposal_amount, digits=2)
-                disposal_cost = round.(disposal_cost, digits=2)
-                
-                for year in 1:T
-                    push!(df, [
-                        plant_name,
-                        location_name,
-                        year,
-                        product_name,
-                        round(amount_produced[year], digits=2),
-                        sent[year],
-                        disposal_amount[year],
-                        disposal_cost[year],
-                    ])
-                end
-            end
-        end
-    end
-    return df
-end
-
-
-function plant_emissions_report(solution)::DataFrame
-    df = DataFrame()
-    df."plant type" = String[]
-    df."location name" = String[]
-    df."year" = Int[]
-    df."emission type" = String[]
-    df."emission amount (tonne)" = Float64[]
-    T = length(solution["Energy"]["Plants (GJ)"])
-    for (plant_name, plant_dict) in solution["Plants"]
-        for (location_name, location_dict) in plant_dict
-            for (emission_name, emission_amount) in location_dict["Emissions (tonne)"]
-                for year in 1:T
-                    push!(df, [
-                        plant_name,
-                        location_name,
-                        year,
-                        emission_name,
-                        round(emission_amount[year], digits=2),
-                    ])
-                end
-            end
-        end
-    end
-    return df
-end
-
-
-function transportation_report(solution)::DataFrame
-    df = DataFrame()
-    df."source type" = String[]
-    df."source location name" = String[]
-    df."source latitude (deg)" = Float64[]
-    df."source longitude (deg)" = Float64[]
-    df."destination type" = String[]
-    df."destination location name" = String[]
-    df."destination latitude (deg)" = Float64[]
-    df."destination longitude (deg)" = Float64[]
-    df."product" = String[]
-    df."year" = Int[]
-    df."distance (km)" = Float64[]
-    df."amount (tonne)" = Float64[]
-    df."amount-distance (tonne-km)" = Float64[]
-    df."transportation cost (\$)" = Float64[]    
-    df."transportation energy (GJ)" = Float64[]    
-    
-    T = length(solution["Energy"]["Plants (GJ)"])
-    for (dst_plant_name, dst_plant_dict) in solution["Plants"]
-        for (dst_location_name, dst_location_dict) in dst_plant_dict
-            for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
-                for (src_location_name, src_location_dict) in src_plant_dict
-                    for year in 1:T
-                        push!(df, [
-                            src_plant_name,
-                            src_location_name,
-                            round(src_location_dict["Latitude (deg)"], digits=6),
-                            round(src_location_dict["Longitude (deg)"], digits=6),
-                            dst_plant_name,
-                            dst_location_name,
-                            round(dst_location_dict["Latitude (deg)"], digits=6),
-                            round(dst_location_dict["Longitude (deg)"], digits=6),
-                            dst_location_dict["Input product"],
-                            year,
-                            round(src_location_dict["Distance (km)"], digits=2),
-                            round(src_location_dict["Amount (tonne)"][year], digits=2),
-                            round(src_location_dict["Amount (tonne)"][year] *
-                                      src_location_dict["Distance (km)"],
-                                  digits=2),
-                            round(src_location_dict["Transportation cost (\$)"][year], digits=2),
-                            round(src_location_dict["Transportation energy (J)"][year] / 1e9, digits=2),
-                        ])
-                    end
-                end
-            end
-        end
-    end
-    return df
-end
-
-
-function transportation_emissions_report(solution)::DataFrame
-    df = DataFrame()
-    df."source type" = String[]
-    df."source location name" = String[]
-    df."source latitude (deg)" = Float64[]
-    df."source longitude (deg)" = Float64[]
-    df."destination type" = String[]
-    df."destination location name" = String[]
-    df."destination latitude (deg)" = Float64[]
-    df."destination longitude (deg)" = Float64[]
-    df."product" = String[]
-    df."year" = Int[]
-    df."distance (km)" = Float64[]
-    df."shipped amount (tonne)" = Float64[]
-    df."shipped amount-distance (tonne-km)" = Float64[]
-    df."emission type" = String[]    
-    df."emission amount (tonne)" = Float64[]    
-    
-    T = length(solution["Energy"]["Plants (GJ)"])
-    for (dst_plant_name, dst_plant_dict) in solution["Plants"]
-        for (dst_location_name, dst_location_dict) in dst_plant_dict
-            for (src_plant_name, src_plant_dict) in dst_location_dict["Input"]
-                for (src_location_name, src_location_dict) in src_plant_dict
-                    for (emission_name, emission_amount) in src_location_dict["Emissions (tonne)"]
-                        for year in 1:T
-                            push!(df, [
-                                src_plant_name,
-                                src_location_name,
-                                round(src_location_dict["Latitude (deg)"], digits=6),
-                                round(src_location_dict["Longitude (deg)"], digits=6),
-                                dst_plant_name,
-                                dst_location_name,
-                                round(dst_location_dict["Latitude (deg)"], digits=6),
-                                round(dst_location_dict["Longitude (deg)"], digits=6),
-                                dst_location_dict["Input product"],
-                                year,
-                                round(src_location_dict["Distance (km)"], digits=2),
-                                round(src_location_dict["Amount (tonne)"][year], digits=2),
-                                round(src_location_dict["Amount (tonne)"][year] *
-                                          src_location_dict["Distance (km)"],
-                                      digits=2),
-                                emission_name,
-                                round(emission_amount[year], digits=2),
-                            ])
-                        end
-                    end
-                end
-            end
-        end
-    end
-    return df
-end
-
-function write(solution::AbstractDict, filename::AbstractString)
-    @info "Writing solution: $filename"
-    open(filename, "w") do file
-        JSON.print(file, solution, 2)
-    end
-end
-
-write_plants_report(solution, filename) =
-    CSV.write(filename, plants_report(solution))
-
-write_plant_outputs_report(solution, filename) =
-    CSV.write(filename, plant_outputs_report(solution))
-
-write_plant_emissions_report(solution, filename) =
-    CSV.write(filename, plant_emissions_report(solution))
-
-write_transportation_report(solution, filename) =
-    CSV.write(filename, transportation_report(solution))
-
-write_transportation_emissions_report(solution, filename) =
-    CSV.write(filename, transportation_emissions_report(solution))
--- 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: $filename"
+    open(filename, "w") do file
+        JSON.print(file, solution, 2)
+    end
+end
--- a/src/schemas/input.json
+++ b/src/schemas/input.json
@@ -12,7 +12,12 @@
        "Parameters": {
            "type": "object",
            "properties": {
-                "time horizon (years)": { "type": "number" }
+                "time horizon (years)": {
+                    "type": "number"
+                },
+                "distance metric": {
+                    "type": "string"
+                }
            },
            "required": [
                "time horizon (years)"
@@ -23,17 +28,27 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "input": { "type": "string" },
+                    "input": {
+                        "type": "string"
+                    },
                    "outputs (tonne/tonne)": {
                        "type": "object",
-                        "additionalProperties": { "type": "number" }
+                        "additionalProperties": {
+                            "type": "number"
+                        }
+                    },
+                    "energy (GJ/tonne)": {
+                        "$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": {
+                        "$ref": "#/definitions/PlantLocation"
+                    }
                },
                "required": [
                    "input",
@@ -46,15 +61,26 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "latitude (deg)": { "type": "number" },
-                    "longitude (deg)": { "type": "number" },
+                    "location": {
+                        "type": "string"
+                    },
+                    "latitude (deg)": {
+                        "type": "number"
+                    },
+                    "longitude (deg)": {
+                        "type": "number"
+                    },
                    "disposal": {
                        "type": "object",
                        "additionalProperties": {
                            "type": "object",
                            "properties": {
-                                "cost ($/tonne)": { "$ref": "#/definitions/TimeSeries" },
-                                "limit (tonne)": { "$ref": "#/definitions/TimeSeries" }
+                                "cost ($/tonne)": {
+                                    "$ref": "#/definitions/TimeSeries"
+                                },
+                                "limit (tonne)": {
+                                    "$ref": "#/definitions/TimeSeries"
+                                }
                            },
                            "required": [
                                "cost ($/tonne)"
@@ -64,22 +90,32 @@
                    "storage": {
                        "type": "object",
                        "properties": {
-                            "cost ($/tonne)": { "$ref": "#/definitions/TimeSeries" },
-                            "limit (tonne)": { "type": "number" }
+                            "cost ($/tonne)": {
+                                "$ref": "#/definitions/TimeSeries"
+                            },
+                            "limit (tonne)": {
+                                "type": "number"
+                            }
                        },
                        "required": [
                            "cost ($/tonne)",
                            "limit (tonne)"
                        ]
-                    },                    
+                    },
                    "capacities (tonne)": {
                        "type": "object",
                        "additionalProperties": {
                            "type": "object",
                            "properties": {
-                                "variable operating cost ($/tonne)": { "$ref": "#/definitions/TimeSeries" },
-                                "fixed operating cost ($)": { "$ref": "#/definitions/TimeSeries" },
-                                "opening cost ($)": { "$ref": "#/definitions/TimeSeries" }
+                                "variable operating cost ($/tonne)": {
+                                    "$ref": "#/definitions/TimeSeries"
+                                },
+                                "fixed operating cost ($)": {
+                                    "$ref": "#/definitions/TimeSeries"
+                                },
+                                "opening cost ($)": {
+                                    "$ref": "#/definitions/TimeSeries"
+                                }
                            },
                            "required": [
                                "variable operating cost ($/tonne)",
@@ -87,11 +123,9 @@
                                "opening cost ($)"
                            ]
                        }
-                    }                    
+                    }
                },
                "required": [
-                    "latitude (deg)",
-                    "longitude (deg)",
                    "capacities (tonne)"
                ]
            }
@@ -101,13 +135,20 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "latitude (deg)": { "type": "number" },
-                    "longitude (deg)": { "type": "number" },
-                    "amount (tonne)": { "$ref": "#/definitions/TimeSeries" }
+                    "location": {
+                        "type": "string"
+                    },
+                    "latitude (deg)": {
+                        "type": "number"
+                    },
+                    "longitude (deg)": {
+                        "type": "number"
+                    },
+                    "amount (tonne)": {
+                        "$ref": "#/definitions/TimeSeries"
+                    }
                },
                "required": [
-                    "latitude (deg)",
-                    "longitude (deg)",
                    "amount (tonne)"
                ]
            }
@@ -117,25 +158,39 @@
            "additionalProperties": {
                "type": "object",
                "properties": {
-                    "transportation cost ($/km/tonne)": { "$ref": "#/definitions/TimeSeries" },
-                    "transportation energy (J/km/tonne)": { "$ref": "#/definitions/TimeSeries" },
+                    "transportation cost ($/km/tonne)": {
+                        "$ref": "#/definitions/TimeSeries"
+                    },
+                    "transportation energy (J/km/tonne)": {
+                        "$ref": "#/definitions/TimeSeries"
+                    },
                    "transportation emissions (tonne/km/tonne)": {
                        "type": "object",
-                        "additionalProperties": { "$ref": "#/definitions/TimeSeries" }
+                        "additionalProperties": {
+                            "$ref": "#/definitions/TimeSeries"
+                        }
                    },
-                    "initial amounts": { "$ref": "#/definitions/InitialAmount" }
+                    "initial amounts": {
+                        "$ref": "#/definitions/InitialAmount"
+                    }
                },
                "required": [
                    "transportation cost ($/km/tonne)"
                ]
-            }            
+            }
        }
    },
    "type": "object",
    "properties": {
-        "parameters": { "$ref": "#/definitions/Parameters" },
-        "plants": { "$ref": "#/definitions/Plant" },
-        "products": { "$ref": "#/definitions/Product" }
+        "parameters": {
+            "$ref": "#/definitions/Parameters"
+        },
+        "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,
-       :Clp,
-       :Geodesy,
-       :JSON,
-       :JSONSchema,
-       :JuMP,
-       :MathOptInterface,
-       :ProgressBars]
+pkg = [:Cbc, :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/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"] == 1695.364
+
+    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/dist_test.jl
+++ b/test/graph/dist_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 "KnnDrivingDistance" begin
+    # Euclidean distance between Chicago and Indianapolis
+    @test RELOG._calculate_distance(
+        41.866,
+        -87.656,
+        39.764,
+        -86.148,
+        RELOG.EuclideanDistance(),
+    ) == 265.818
+
+    # Approximate driving distance between Chicago and Indianapolis
+    @test RELOG._calculate_distance(
+        41.866,
+        -87.656,
+        39.764,
+        -86.148,
+        RELOG.KnnDrivingDistance(),
+    ) == 316.43
+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
@@ -1,127 +0,0 @@
-# Copyright (C) 2020 Argonne National Laboratory
-# Written by Alinson Santos Xavier <axavier@anl.gov>
-
-using RELOG
-
-@testset "Instance" begin
-    @testset "load" begin
-        basedir = dirname(@__FILE__)
-        instance = RELOG.parsefile("$basedir/../instances/s1.json")
-        
-        centers = instance.collection_centers
-        plants = instance.plants
-        products = instance.products
-        location_name_to_plant = Dict(p.location_name => p for p in plants)
-        product_name_to_product = Dict(p.name => p for p in products)
-        
-        @test length(centers) == 10
-        @test centers[1].name == "C1"
-        @test centers[1].latitude == 7
-        @test centers[1].latitude == 7
-        @test centers[1].longitude == 7
-        @test centers[1].amount == [934.56, 934.56]
-        @test centers[1].product.name == "P1"
-        
-        @test length(plants) == 6
-
-        plant = location_name_to_plant["L1"]
-        @test plant.plant_name == "F1"
-        @test plant.location_name == "L1"
-        @test plant.input.name == "P1"
-        @test plant.latitude == 0
-        @test plant.longitude == 0
-        
-        @test length(plant.sizes) == 2
-        @test plant.sizes[1].capacity == 250
-        @test plant.sizes[1].opening_cost == [500, 500]
-        @test plant.sizes[1].fixed_operating_cost == [30, 30]
-        @test plant.sizes[1].variable_operating_cost == [30, 30]
-        @test plant.sizes[2].capacity == 1000
-        @test plant.sizes[2].opening_cost == [1250, 1250]
-        @test plant.sizes[2].fixed_operating_cost == [30, 30]
-        @test plant.sizes[2].variable_operating_cost == [30, 30]
-        
-        p2 = product_name_to_product["P2"]
-        p3 = product_name_to_product["P3"]
-        @test length(plant.output) == 2
-        @test plant.output[p2] == 0.2
-        @test plant.output[p3] == 0.5
-        @test plant.disposal_limit[p2] == [1, 1]
-        @test plant.disposal_limit[p3] == [1, 1]
-        @test plant.disposal_cost[p2] == [-10, -10]
-        @test plant.disposal_cost[p3] == [-10, -10]
-        
-        plant = location_name_to_plant["L3"]
-        @test plant.location_name == "L3"
-        @test plant.input.name == "P2"
-        @test plant.latitude == 25
-        @test plant.longitude == 65
-        
-        @test length(plant.sizes) == 2
-        @test plant.sizes[1].capacity == 1000.0
-        @test plant.sizes[1].opening_cost == [3000, 3000]
-        @test plant.sizes[1].fixed_operating_cost == [50, 50]
-        @test plant.sizes[1].variable_operating_cost == [50, 50]
-        @test plant.sizes[1] == plant.sizes[2]
-        
-        p4 = product_name_to_product["P4"]
-        @test plant.output[p3] == 0.05
-        @test plant.output[p4] == 0.8
-        @test plant.disposal_limit[p3] == [1e8, 1e8]
-        @test plant.disposal_limit[p4] == [0, 0]
-    end
-    
-    @testset "validate timeseries" begin
-        @test_throws String RELOG.parsefile("fixtures/s1-wrong-length.json")
-    end
-    
-    @testset "compress" begin
-        basedir = dirname(@__FILE__)
-        instance = RELOG.parsefile("$basedir/../instances/s1.json")
-        compressed = RELOG._compress(instance)
-        
-        product_name_to_product = Dict(p.name => p for p in compressed.products)
-        location_name_to_facility = Dict()
-        for p in compressed.plants
-            location_name_to_facility[p.location_name] = p
-        end
-        for c in compressed.collection_centers
-            location_name_to_facility[c.name] = c
-        end
-        
-        p1 = product_name_to_product["P1"]
-        p2 = product_name_to_product["P2"]
-        p3 = product_name_to_product["P3"]
-        c1 = location_name_to_facility["C1"]
-        l1 = location_name_to_facility["L1"]
-
-        @test compressed.time == 1
-        @test compressed.building_period == [1]
-
-        @test p1.name == "P1"
-        @test p1.transportation_cost ≈ [0.015]
-        @test p1.transportation_energy ≈ [0.115]
-        @test p1.transportation_emissions["CO2"] ≈ [0.051]
-        @test p1.transportation_emissions["CH4"] ≈ [0.0025]
-        
-        @test c1.name == "C1"
-        @test c1.amount ≈ [1869.12]
-        
-        @test l1.plant_name == "F1"
-        @test l1.location_name == "L1"
-        @test l1.energy ≈ [0.115]
-        @test l1.emissions["CO2"] ≈ [0.051]
-        @test l1.emissions["CH4"] ≈ [0.0025]
-        @test l1.sizes[1].opening_cost ≈ [500]
-        @test l1.sizes[2].opening_cost ≈ [1250]
-        @test l1.sizes[1].fixed_operating_cost ≈ [60]
-        @test l1.sizes[2].fixed_operating_cost ≈ [60]
-        @test l1.sizes[1].variable_operating_cost ≈ [30]
-        @test l1.sizes[2].variable_operating_cost ≈ [30]
-        @test l1.disposal_limit[p2] ≈ [2.0]
-        @test l1.disposal_limit[p3] ≈ [2.0]
-        @test l1.disposal_cost[p2] ≈ [-10.0]
-        @test l1.disposal_cost[p3] ≈ [-10.0]        
-    end
-end
-
--- 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
@@ -1,100 +0,0 @@
-# Copyright (C) 2020 Argonne National Laboratory
-# Written by Alinson Santos Xavier <axavier@anl.gov>
-
-using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
-
-@testset "Model" begin
-    @testset "build" begin
-        basedir = dirname(@__FILE__)
-        instance = RELOG.parsefile("$basedir/../instances/s1.json")
-        graph = RELOG.build_graph(instance)
-        model = RELOG.build_model(instance, graph, Cbc.Optimizer)
-        set_optimizer_attribute(model.mip, "logLevel", 0)
-
-        process_node_by_location_name = Dict(n.location.location_name => n
-                                             for n in graph.process_nodes)
-
-        shipping_node_by_location_and_product_names = Dict((n.location.location_name, n.product.name) => n
-                                                           for n in graph.plant_shipping_nodes)
-        
-        @test length(model.vars.flow) == 76
-        @test length(model.vars.dispose) == 16
-        @test length(model.vars.open_plant) == 12
-        @test length(model.vars.capacity) == 12
-        @test length(model.vars.expansion) == 12
-
-        l1 = process_node_by_location_name["L1"]
-        v = model.vars.capacity[l1, 1]
-        @test lower_bound(v) == 0.0
-        @test upper_bound(v) == 1000.0
-        
-        v = model.vars.expansion[l1, 1]
-        @test lower_bound(v) == 0.0
-        @test upper_bound(v) == 750.0
-        
-        v = model.vars.dispose[shipping_node_by_location_and_product_names["L1", "P2"], 1]
-        @test lower_bound(v) == 0.0
-        @test upper_bound(v) == 1.0
-        
-        # dest = FileFormats.Model(format = FileFormats.FORMAT_LP)
-        # MOI.copy_to(dest, model.mip)
-        # MOI.write_to_file(dest, "model.lp")
-    end
-
-    @testset "solve (exact)" begin
-        solution_filename_a = tempname()
-        solution_filename_b = tempname()
-        solution = RELOG.solve("$(pwd())/../instances/s1.json",
-                               output=solution_filename_a)
-
-        @test isfile(solution_filename_a)
-                               
-        RELOG.write(solution, solution_filename_b)
-        @test isfile(solution_filename_b)
-        
-        @test "Costs" in keys(solution)
-        @test "Fixed operating (\$)" in keys(solution["Costs"])
-        @test "Transportation (\$)" in keys(solution["Costs"])
-        @test "Variable operating (\$)" in keys(solution["Costs"])
-        @test "Total (\$)" in keys(solution["Costs"])
-
-        @test "Plants" in keys(solution)
-        @test "F1" in keys(solution["Plants"])
-        @test "F2" in keys(solution["Plants"])
-        @test "F3" in keys(solution["Plants"])
-        @test "F4" in keys(solution["Plants"])
-    end
-    
-    
-    @testset "solve (heuristic)" begin
-        # Should not crash
-        solution = RELOG.solve("$(pwd())/../instances/s1.json", heuristic=true)
-    end
-
-    @testset "infeasible solve" begin
-        json = JSON.parsefile("$(pwd())/../instances/s1.json")
-        for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
-            location_dict["amount (tonne)"] *= 1000
-        end
-        RELOG.solve(RELOG.parse(json))
-    end
-    
-    @testset "storage" begin
-        basedir = dirname(@__FILE__)
-        filename = "$basedir/fixtures/storage.json"
-        instance = RELOG.parsefile(filename)
-        @test instance.plants[1].storage_limit == 50.0
-        @test instance.plants[1].storage_cost == [2.0, 1.5, 1.0]
-        
-        solution = RELOG.solve(filename)
-        plant_dict = solution["Plants"]["mega plant"]["Chicago"]
-        @test plant_dict["Variable operating cost (\$)"] == [500.0, 0.0, 100.0]
-        @test plant_dict["Process (tonne)"] == [50.0, 0.0, 50.0]
-        @test plant_dict["Storage (tonne)"] == [50.0, 50.0, 0.0]
-        @test plant_dict["Storage cost (\$)"] == [100.0, 75.0, 0.0]
-        
-        @test solution["Costs"]["Variable operating (\$)"] == [500.0, 0.0, 100.0]
-        @test solution["Costs"]["Storage (\$)"] == [100.0, 75.0, 0.0]
-        @test solution["Costs"]["Total (\$)"] == [600.0, 75.0, 100.0]
-    end
-end
--- a/test/reports_test.jl
+++ b/test/reports_test.jl
@@ -4,38 +4,16 @@

 using RELOG, JSON, GZip

-load_json_gz(filename) = JSON.parse(GZip.gzopen(filename))
-
-# function check(func, expected_csv_filename::String)
-#     solution = load_json_gz("fixtures/nimh_solution.json.gz")
-#     actual_csv_filename = tempname()
-#     func(solution, actual_csv_filename)
-#     @test isfile(actual_csv_filename)
-#     if readlines(actual_csv_filename) != readlines(expected_csv_filename)
-#         out_filename = replace(expected_csv_filename, ".csv" => "_actual.csv")
-#         @error "$func: Unexpected CSV contents: $out_filename"
-#         write(out_filename, read(actual_csv_filename))
-#         @test false
-#     end
-# end
-
@testset "Reports" begin
-#     @testset "from fixture" begin
-#         check(RELOG.write_plants_report, "fixtures/nimh_plants.csv")
-#         check(RELOG.write_plant_outputs_report, "fixtures/nimh_plant_outputs.csv")
-#         check(RELOG.write_plant_emissions_report, "fixtures/nimh_plant_emissions.csv")
-#         check(RELOG.write_transportation_report, "fixtures/nimh_transportation.csv")
-#         check(RELOG.write_transportation_emissions_report, "fixtures/nimh_transportation_emissions.csv")
-#     end
-    
    @testset "from solve" begin
        solution = RELOG.solve("$(pwd())/../instances/s1.json")
        tmp_filename = tempname()
        # The following should not crash
-        RELOG.write_plants_report(solution, tmp_filename)
-        RELOG.write_plant_outputs_report(solution, tmp_filename)
        RELOG.write_plant_emissions_report(solution, tmp_filename)
-        RELOG.write_transportation_report(solution, tmp_filename)
+        RELOG.write_plant_outputs_report(solution, tmp_filename)
+        RELOG.write_plants_report(solution, tmp_filename)
+        RELOG.write_products_report(solution, tmp_filename)
        RELOG.write_transportation_emissions_report(solution, tmp_filename)
+        RELOG.write_transportation_report(solution, tmp_filename)
    end
 end
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -4,8 +4,19 @@
 using Test

@testset "RELOG" begin
-    include("instance_test.jl")
-    include("graph_test.jl")
-    include("model_test.jl")
+    @testset "Instance" begin
+        include("instance/compress_test.jl")
+        include("instance/geodb_test.jl")
+        include("instance/parse_test.jl")
+    end
+    @testset "Graph" begin
+        include("graph/build_test.jl")
+        include("graph/dist_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
+end
Author	SHA1	Message	Date
Alinson S. Xavier	51ff8eb130	Restrict NearestNeighbors version; remove debug statement	2022-12-15 11:03:44 -06:00
Alinson S. Xavier	9191474df8	Bump version to 0.6	2022-12-15 10:36:44 -06:00
Alinson S. Xavier	841fbf16fb	Make distance metric configurable; fix building period bug	2022-12-15 10:26:10 -06:00
Alinson S. Xavier	48bd3c403f	Switch from Euclidean to approximate driving distance	2022-12-15 09:49:38 -06:00
Alinson S. Xavier	23b3b33146	Update README.md	2022-10-28 14:05:53 -05:00
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