31 Commits

Author SHA1 Message Date
8231f9da32 Implement full recourse; remove feasibility cuts 2022-11-09 11:34:37 -06:00
48ccf0d180 Add stochastic tests 2022-11-09 11:03:39 -06:00
7f475a0914 Merge branch 'master' into feature/stochastic 2022-11-09 10:50:56 -06:00
4b0fc7327c Accelerate tests with Revise.jl 2022-11-09 10:34:18 -06:00
dde0d40282 Fix tests 2022-11-09 10:11:48 -06:00
74606897cd Small fixes 2022-10-24 11:21:58 -05:00
07ca3abb4f Implement stochastic model 2022-09-23 16:06:11 -05:00
da158eb961 Update CHANGELOG 2022-08-26 13:26:15 -05:00
e7eec937cb Update README.md 2022-08-26 13:21:42 -05:00
19bec961bd GH Actions: Update Julia versions 2022-08-26 13:12:20 -05:00
8f52c04702 Fix broken image link 2022-08-26 13:08:22 -05:00
19a34fb5d2 Update dependencies; switch to Documenter.jl 2022-08-26 13:04:47 -05:00
e915a57e58 Allow disposal at collection centers 2022-07-29 10:22:03 -05:00
57b7d09c08 Add disposal cost to product report 2021-10-15 09:39:35 -05:00
a03b9169fd Allow product disposal at collection centers 2021-10-15 09:11:41 -05:00
ee58af73f0 Update sysimage and build scripts 2021-10-15 08:14:04 -05:00
92d30460b9 Update README.md 2021-09-03 18:07:40 -05:00
9ebb2e49f9 Fix validation error on JSONSchema 0.3 2021-08-06 14:56:46 -05:00
505e3a8e1e Update CHANGELOG 2021-07-23 17:42:48 -05:00
d4fa75297f Fix OrderedCollections version 2021-07-23 17:42:29 -05:00
881957d6b5 Implement resolve 2021-07-21 14:53:49 -05:00
86cf7f5bd9 Throw exception for infeasible models 2021-07-21 14:18:10 -05:00
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
099e0fae3a Docs: Minor fixes to what-if analsis section 2021-07-21 14:07:00 -05:00
1b8f392852 Docs: Add description of resolve 2021-07-21 14:07:00 -05:00
7a95aa66f6 Update CHANGELOG 2021-07-21 11:49:54 -05:00
40d28c727a Add products report 2021-07-16 11:25:40 -05:00
a9ac164833 Fix GeoDB download 2021-07-16 10:31:21 -05:00
e244ded51d GH Actions: Add Julia 1.6, remove nightly 2021-07-16 10:18:10 -05:00
7180651cfa Reformat source code 2021-07-16 10:15:41 -05:00
0c9465411f Document GeoDB; remove unused code; minor fixes 2021-07-16 10:13:58 -05:00
55 changed files with 10672 additions and 1032 deletions

View File

@@ -10,7 +10,7 @@ jobs:
runs-on: ${{ matrix.os }} runs-on: ${{ matrix.os }}
strategy: strategy:
matrix: matrix:
version: ['1.3', '1.4', '1.5', 'nightly'] version: ['1.6', '1.7', '1.8']
os: os:
- ubuntu-latest - ubuntu-latest
arch: arch:

1
.gitignore vendored
View File

@@ -12,3 +12,4 @@ Manifest.toml
data data
build build
benchmark benchmark
**/*.log

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@@ -1,28 +1,58 @@
# Version 0.5.0 (Jan 6, 2021) # Changelog
All notable changes to this project will be documented in this file.
- The format is based on [Keep a Changelog][changelog].
- This project adheres to [Semantic Versioning][semver].
- For versions before 1.0, we follow the [Pkg.jl convention][pkjjl]
that `0.a.b` is compatible with `0.a.c`.
[changelog]: https://keepachangelog.com/en/1.0.0/
[semver]: https://semver.org/spec/v2.0.0.html
[pkjjl]: https://pkgdocs.julialang.org/v1/compatibility/#compat-pre-1.0
## [Unreleased]
- Allow product disposal at collection centers
- Implement stochastic optimization
## [0.5.2] -- 2022-08-26
### Changed
- Update to JuMP 1.x
## [0.5.1] -- 2021-07-23
### Added
- Allow user to specify locations as unique identifiers, instead of latitude and longitude (e.g. `us-state:IL` or `2018-us-county:17043`)
- Add what-if scenarios.
- Add products report.
## [0.5.0] -- 2021-01-06
### Added
- Allow plants to store input material for processing in later years - 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) - 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 - Add option to write solution to JSON file in RELOG.solve
- Improve error message when instance is infeasible - Improve error message when instance is infeasible
- Make output file more readable - Make output file more readable
# Version 0.3.2 (Aug 7, 2020) ## [0.3.2] -- 2020-10-07
### Added
- Add "building period" parameter - Add "building period" parameter
# Version 0.3.1 (July 17, 2020) ## [0.3.1] -- 2020-07-17
### Fixed
- Fix expansion cost breakdown - 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) - Track emissions and energy (transportation and plants)
### Changed
- Minor changes to input file format: - Minor changes to input file format:
- Make all dictionary keys lowercase - Make all dictionary keys lowercase
- Rename "outputs (tonne)" to "outputs (tonne/tonne)" - Rename "outputs (tonne)" to "outputs (tonne/tonne)"

View File

@@ -1,28 +1,19 @@
JULIA := julia --color=yes --project=@.
SRC_FILES := $(wildcard src/*.jl test/*.jl)
VERSION := 0.5 VERSION := 0.5
all: docs test
build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
mkdir -p build
$(JULIA) src/sysimage.jl
build/test.log: $(SRC_FILES) build/sysimage.so
cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl
clean: clean:
rm -rf build/* rm -rfv build Manifest.toml test/Manifest.toml deps/formatter/build deps/formatter/Manifest.toml
docs: docs:
mkdocs build -d ../docs/$(VERSION)/ cd docs; julia --project=. make.jl; cd ..
rsync -avP --delete-after docs/build/ ../docs/$(VERSION)/
format: format:
julia -e 'using JuliaFormatter; format(["src", "test"], verbose=true);' cd deps/formatter; ../../juliaw format.jl
test: build/test.log test: test/Manifest.toml
./juliaw test/runtests.jl
test-watch: test/Manifest.toml: test/Project.toml
bash -c "while true; do make test --quiet; sleep 1; done" julia --project=test -e "using Pkg; Pkg.instantiate()"
.PHONY: docs test .PHONY: docs test format

View File

@@ -1,47 +1,45 @@
name = "RELOG" name = "RELOG"
uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008" uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
authors = ["Alinson S Xavier <axavier@anl.gov>"] authors = ["Alinson S Xavier <axavier@anl.gov>"]
version = "0.5.0" version = "0.5.2"
[deps] [deps]
CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205" CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76"
Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6" Downloads = "f43a241f-c20a-4ad4-852c-f6b1247861c6"
GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63" GZip = "92fee26a-97fe-5a0c-ad85-20a5f3185b63"
Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d" Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
HiGHS = "87dc4568-4c63-4d18-b0c0-bb2238e4078b"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JSONSchema = "7d188eb4-7ad8-530c-ae41-71a32a6d4692" JSONSchema = "7d188eb4-7ad8-530c-ae41-71a32a6d4692"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572" JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee" MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7" Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568" ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4" Shapefile = "8e980c4a-a4fe-5da2-b3a7-4b4b0353a2f4"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StochasticPrograms = "8b8459f2-c380-502b-8633-9aed2d6c2b35"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
[compat] [compat]
CRC = "4" CRC = "4"
CSV = "0.7" CSV = "0.10"
Cbc = "0.6" DataFrames = "1"
Clp = "0.8" DataStructures = "0.18"
DataFrames = "0.21"
DataStructures = "0.17"
GZip = "0.5" GZip = "0.5"
Geodesy = "0.5" Geodesy = "1"
JSON = "0.21" JSON = "0.21"
JSONSchema = "0.3" JSONSchema = "1"
JuMP = "0.21" JuMP = "1"
MathOptInterface = "0.9" MathOptInterface = "1"
PackageCompiler = "1" OrderedCollections = "1"
ProgressBars = "0.6" ProgressBars = "1"
Shapefile = "0.7" Shapefile = "0.8"
ZipFile = "0.9" ZipFile = "0.10"
julia = "1" julia = "1"

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@@ -15,7 +15,7 @@
<img src="https://anl-ceeesa.github.io/RELOG/0.5/images/ex_transportation.png" width="1000px"/> <img src="https://anl-ceeesa.github.io/RELOG/0.5/assets/ex_transportation.png" width="1000px"/>
### Documentation ### Documentation
@@ -26,8 +26,10 @@
### Authors ### Authors
* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>> * **Alinson S. Xavier** <<axavier@anl.gov>>
* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>> * **Nwike Iloeje** <<ciloeje@anl.gov>>
* **John Atkins**
* **Kyle Sun**
### License ### License

5
deps/formatter/Project.toml vendored Normal file
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@@ -0,0 +1,5 @@
[deps]
JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899"
[compat]
JuliaFormatter = "0.14.4"

8
deps/formatter/format.jl vendored Normal file
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@@ -0,0 +1,8 @@
using JuliaFormatter
format(
[
"../../src",
"../../test",
],
verbose=true,
)

4
docs/Project.toml Normal file
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@@ -0,0 +1,4 @@
[deps]
Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
RELOG = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"

19
docs/make.jl Normal file
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@@ -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()

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@@ -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);
}

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@@ -11,7 +11,7 @@ RELOG accepts as input a JSON file with three sections: `parameters`, `products`
The **parameters** section describes details about the simulation itself. The **parameters** section describes details about the simulation itself.
| Key | Description | Key | Description
|:--------------------------|---------------| |:--------------------------|:---------------|
|`time horizon (years)` | Number of years in the simulation. |`time horizon (years)` | Number of years in the simulation.
|`building period (years)` | List of years in which we are allowed to open new plants. For example, if this parameter is set to `[1,2,3]`, we can only open plants during the first three years. By default, this equals `[1]`; that is, plants can only be opened during the first year. | |`building period (years)` | List of years in which we are allowed to open new plants. For example, if this parameter is set to `[1,2,3]`, we can only open plants during the first three years. By default, this equals `[1]`; that is, plants can only be opened during the first year. |
@@ -31,16 +31,18 @@ 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: 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 | Key | Description
|:--------------------------------------|---------------| |:--------------------------------------|:---------------|
|`transportation cost ($/km/tonne)` | The cost to transport this product. Must be a time series. |`transportation cost ($/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 energy (J/km/tonne)` | The energy required to transport this product. Must be a time series. Optional.
|`transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional. |`transportation emissions (tonne/km/tonne)` | A dictionary mapping the name of each greenhouse gas, produced to transport one tonne of this product along one kilometer, to the amount of gas produced (in tonnes). Must be a time series. Optional.
|`initial amounts` | A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a time series. |`initial amounts` | A dictionary mapping the name of each location to its description (see below). If this product is not initially available, this key may be omitted. Must be a time series.
| `disposal limit (tonne)` | Total amount of product that can be disposed of across all collection centers. If omitted, all product must be processed. This parameter has no effect on product disposal at plants.
| `disposal cost ($/tonne)` | Cost of disposing one tonne of this product at a collection center. If omitted, defaults to zero. This parameter has no effect on product disposal costs at plants.
Each product may have some amount available at the beginning of each time period. In this case, the key `initial amounts` maps to a dictionary with the following keys: Each product may have some amount available at the beginning of each time period. In this case, the key `initial amounts` maps to a dictionary with the following keys:
| Key | Description | Key | Description
|:------------------------|---------------| |:------------------------|:---------------|
| `latitude (deg)` | The latitude of the location. | `latitude (deg)` | The latitude of the location.
| `longitude (deg)` | The longitude of the location. | `longitude (deg)` | The longitude of the location.
| `amount (tonne)` | The amount of the product initially available at the location. Must be a time series. | `amount (tonne)` | The amount of the product initially available at the location. Must be a time series.
@@ -73,7 +75,9 @@ Each product may have some amount available at the beginning of each time period
"transportation emissions (tonne/km/tonne)": { "transportation emissions (tonne/km/tonne)": {
"CO2": [0.052, 0.050], "CO2": [0.052, 0.050],
"CH4": [0.003, 0.002] "CH4": [0.003, 0.002]
} },
"disposal cost ($/tonne)": [-10.0, -12.0],
"disposal limit (tonne)": [1.0, 1.0],
}, },
"P2": { "P2": {
"transportation cost ($/km/tonne)": [0.022, 0.020] "transportation cost ($/km/tonne)": [0.022, 0.020]
@@ -93,7 +97,7 @@ 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: 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 | Key | Description
|:------------------------|---------------| |:------------------------|:---------------|
| `input` | The name of the product that this plant takes as input. Only one input is accepted per plant. | `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. | `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. |`energy (GJ/tonne)` | The energy required to process 1 tonne of the input. Must be a time series. Optional.
@@ -113,14 +117,14 @@ Each type of plant is associated with a set of potential locations where it can
The `storage` dictionary should contain the following keys: The `storage` dictionary should contain the following keys:
| Key | Description | Key | Description
|:------------------------|---------------| |:------------------------|:---------------|
| `cost ($/tonne)` | The cost to store a tonne of input product for one time period. Must be a time series. | `cost ($/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. | `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: The keys in the `disposal` dictionary should be the names of the products. The values are dictionaries with the following keys:
| Key | Description | Key | Description
|:------------------------|---------------| |:------------------------|:---------------|
| `cost ($/tonne)` | The cost to dispose of the product. Must be a time series. | `cost ($/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. | `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 +132,7 @@ 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: The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes). The values are dictionaries with the following keys:
| Key | Description | Key | Description
|:--------------------------------------|---------------| |:--------------------------------------|:---------------|
| `opening cost ($)` | The cost to open a plant of this size. | `opening cost ($)` | The cost to open a plant of this size.
| `fixed operating cost ($)` | The cost to keep the plant open, even if the plant doesn't process anything. Must be a time series. | `fixed operating cost ($)` | The cost to keep the plant open, even if the plant doesn't process anything. Must be a time series.
| `variable operating cost ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series. | `variable operating cost ($/tonne)` | The cost that the plant incurs to process each tonne of input. Must be a time series.
@@ -182,6 +186,38 @@ The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes
} }
``` ```
### Geographic database
Instead of specifying locations using latitudes and longitudes, it is also possible to specify them using unique identifiers, such as the name of a US state, or the county FIPS code. This works anywhere `latitude (deg)` and `longitude (deg)` are expected. For example, instead of:
```json
{
"initial amounts": {
"C1": {
"latitude (deg)": 37.27182,
"longitude (deg)": -119.2704,
"amount (tonne)": [934.56, 934.56]
},
}
}
```
is is possible to write:
```json
{
"initial amounts": {
"C1": {
"location": "us-state:CA",
"amount (tonne)": [934.56, 934.56]
},
}
}
```
Location names follow the format `db:id`, where `db` is the name of the database and `id` is the identifier for a specific location. RELOG currently includes the following databases:
Database | Description | Examples
:--------|:------------|:---------
`us-state`| List of states of the United States. | `us-state:IL` (State of Illinois)
`2018-us-county` | List of United States counties, as of 2018. IDs are 5-digit FIPS codes. | `2018-us-county:17043` (DuPage county in Illinois)
### Current limitations ### Current limitations
* Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation. * Each plant can only be opened exactly once. After open, the plant remains open until the end of the simulation.
@@ -192,4 +228,3 @@ The keys in the `capacities (tonne)` dictionary should be the amounts (in tonnes
## Output Data Format (JSON) ## Output Data Format (JSON)
To be documented. To be documented.

View File

@@ -1,25 +1,29 @@
# RELOG: Reverse Logistics Optimization # 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. **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 ### Table of Contents
* [Usage](usage.md) ```@contents
* [Input and Output Data Formats](format.md) Pages = ["usage.md", "format.md", "reports.md", "model.md"]
* [Simplified Solution Reports](reports.md) Depth = 3
* [Optimization Model](model.md) ```
### Source Code ### Source Code
* [https://github.com/ANL-CEEESA/RELOG](https://github.com/ANL-CEEESA/RELOG) * [https://github.com/ANL-CEEESA/RELOG](https://github.com/ANL-CEEESA/RELOG)
### Authors ### Authors
* **Alinson S. Xavier,** Argonne National Laboratory <<axavier@anl.gov>> * **Alinson S. Xavier,** Argonne National Laboratory <axavier@anl.gov>
* **Nwike Iloeje,** Argonne National Laboratory <<ciloeje@anl.gov>> * **Nwike Iloeje,** Argonne National Laboratory <ciloeje@anl.gov>
### License ### License

View File

@@ -6,53 +6,65 @@ In this page, we describe the precise mathematical optimization model used by RE
### Sets ### Sets
* $L$ - Set of locations holding the original material to be recycled Symbol | Description
* $M$ - Set of materials recovered during the reverse manufacturing process :-------|:------------
* $P$ - Set of potential plants to open $L$ | Set of locations holding the original material to be recycled
* $T = \{ 1, \ldots, t^{max} \} $ - Set of time periods $M$ | 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 ### Constants
**Plants:** #### Plants
* $c^\text{disp}_{pmt}$ - Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ (`$/tonne/km`) Symbol | Description | Unit
* $c^\text{exp}_{pt}$ - 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 (`$`) $c^\text{disp}_{pmt}$ | Cost of disposing one tonne of material $m$ at plant $p$ during time $t$ | \$/tonne/km
* $c^\text{f-base}_{pt}$ - Fixed cost of keeping plant $p$ open during time period $t$ (`$`) $c^\text{exp}_{pt}$ | Cost of adding one tonne of capacity to plant $p$ at time $t$ | \$/tonne
* $c^\text{f-exp}_{pt}$ - Increase in fixed cost for each additional tonne of capacity (`$/tonne`) $c^\text{open}_{pt}$ | Cost of opening plant $p$ at time $t$, at minimum capacity | $
* $c^\text{var}_{pt}$ - Variable cost of processing one tonne of input at plant $p$ at time $t$ (`$/tonne`) $c^\text{f-base}_{pt}$ | Fixed cost of keeping plant $p$ open during time period $t$ | $
* $c^\text{store}_{pt}$ - Cost of storing one tonne of original material at plant $p$ at time $t$ (`$/tonne`) $c^\text{f-exp}_{pt}$ | Increase in fixed cost for each additional tonne of capacity | \$/tonne
* $m^\text{min}_p$ - Minimum capacity of plant $p$ (`tonne`) $c^\text{var}_{pt}$ | Variable cost of processing one tonne of input at plant $p$ at time $t$ | \$/tonne
* $m^\text{max}_p$ - Maximum capacity of plant $p$ (`tonne`) $c^\text{store}_{pt}$ | Cost of storing one tonne of original material at plant $p$ at time $t$ | \$/tonne
* $m^\text{disp}_{pmt}$ - Maximum amount of material $m$ that plant $p$ can dispose of during time $t$ (`tonne`) $m^\text{min}_p$ | Minimum capacity of plant $p$ | tonne
* $m^\text{store}_p$ - Maximum amount of original material that plant $p$ can store for later processing. $m^\text{max}_p$ | 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`) Symbol | Description | Unit
* $m^\text{initial}_{lt}$ - Amount of original material to be recycled at location $l$ during time $t$ (`tonne`) :-------|:------------|:---
$\alpha_{pm}$ | 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`) Symbol | Description | Unit
* $d_{lp}$ - Distance between plant $p$ and location $l$ (`km`) :-------|:------------|:---
$c^\text{tr}_{t}$ | Transportation cost during time $t$ | \$/tonne/km
$d_{lp}$ | Distance between plant $p$ and location $l$ | km
### Decision variables ### 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`) Symbol | Description | Unit
* $w_{pt}$ - 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`) $q_{mpt}$ | Amount of material $m$ recovered by plant $p$ during time $t$ | tonne
* $y_{lpt}$ - Amount of product sent from location $l$ to plant $p$ during time $t$ (`tonne`) $u_{pt}$ | Binary variable that equals 1 if plant $p$ starts operating at time $t$ | Boolean
* $z^{\text{disp}}_{mpt}$ - Amount of material $m$ disposed of by plant $p$ during time $t$ (`tonne`) $w_{pt}$ | Extra capacity (amount above the minimum) added to 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`) $x_{pt}$ | Binary variable that equals 1 if plant $p$ is operational at time $t$ | Boolean
* $z^{\text{proc}}_{mpt}$ - Amount of original material processed by plant $p$ during time period $t$ (`tonne`) $y_{lpt}$ | 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 ### Objective function
RELOG minimizes the overall capital, production and transportation costs: RELOG minimizes the overall capital, production and transportation costs:
```math
\begin{align*} \begin{align*}
\text{minimize} \;\; & \text{minimize} \;\; &
\sum_{t \in T} \sum_{p \in P} \left[ \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} \sum_{t \in T} \sum_{p \in P} \sum_{m \in M} c^{\text{disp}}_{pmt} z_{pmt}
\end{align*} \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 first line, we have (i) opening costs, if plant starts operating at time $t$, (ii) fixed operating costs, if plant is operational, (iii) additional fixed operating costs coming from expansion performed in all previous time periods up to the current one, and finally (iv) the expansion costs during the current time period.
In the second line, we have storage and variable processing costs. In the 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: * All original materials must be sent to a plant:
\begin{align} ```math
\begin{align*}
& \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt} & \sum_{p \in P} y_{lpt} = m^\text{initial}_{lt}
& \forall l \in L, t \in T & \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. * 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} & \sum_{l \in L} y_{lpt} + z^{\text{store}}_{p,t-1}
= z^{\text{proc}}_{pt} + z^{\text{store}}_{p,t} = z^{\text{proc}}_{pt} + z^{\text{store}}_{p,t}
& \forall p \in P, t \in 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 \\ & \forall p \in P \\
& z^{\text{store}}_{p,t^{\max}} = 0 & z^{\text{store}}_{p,t^{\max}} = 0
& \forall p \in P & \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: * 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 & z^{\text{proc}}_{pt} \leq m^\text{min}_p x_p + \sum_{i=1}^t w_p
& \forall p \in P, t \in T & \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: * 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 & z^{\text{store}}_{pt} \leq m^\text{store}_p x_p
& \forall p \in P, t \in T & \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: * 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 & \sum_{i=1}^t w_p \leq m^\text{max}_p x_p
& \forall p \in P, t \in T & \forall p \in P, t \in T
\end{align} \end{align*}
```
* Amount of recovered material is proportional to amount processed: * Amount of recovered material is proportional to amount processed:
\begin{align} ```math
\begin{align*}
& q_{mpt} = \alpha_{pm} z^{\text{proc}}_{pt} & q_{mpt} = \alpha_{pm} z^{\text{proc}}_{pt}
& \forall m \in M, p \in P, t \in T & \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. * 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} & q_{mpt} = z_{mpt}
& \forall m \in M, p \in P, t \in T & \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. * 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} & x_{pt} = x_{p,t-1} + u_{pt}
& \forall p \in P, t \in T \setminus \{1\} \\ & \forall p \in P, t \in T \setminus \{1\} \\
& x_{p,1} = u_{p,1} & x_{p,1} = u_{p,1}
& \forall p \in P & \forall p \in P
\end{align} \end{align*}
```
* Variable bounds: * Variable bounds:
\begin{align} ```math
\begin{align*}
& q_{mpt} \geq 0 & q_{mpt} \geq 0
& \forall m \in M, p \in P, t \in T \\ & \forall m \in M, p \in P, t \in T \\
& u_{pt} \in \{0,1\} & u_{pt} \in \{0,1\}
@@ -162,4 +192,5 @@ In the fourth line, we have the disposal costs.
& p \in P, t \in T \\ & p \in P, t \in T \\
& z^{\text{disp}}_{mpt}, z^{\text{proc}}_{mpt} \geq 0 & z^{\text{disp}}_{mpt}, z^{\text{proc}}_{mpt} \geq 0
& \forall m \in M, p \in P, t \in T & \forall m \in M, p \in P, t \in T
\end{align} \end{align*}
```

View File

@@ -6,15 +6,13 @@ In this page, we also illustrate what types of charts and visualizations can be
## Plants report ## Plants report
Report showing plant costs, capacities, energy expenditure and utilization factors. Report showing plant costs, capacities, energy expenditure and utilization factors. Generated by `RELOG.write_plants_report(solution, filename)`.
Generated by `RELOG.write_plants_report(solution, filename)`. For a concrete example, see [nimh_plants.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plants.csv).
| Column | Description | Column | Description
|:--------------------------------------|---------------| |:--------------------------------------|:---------------|
| `plant type` | Plant type. | `plant type` | Plant type.
| `location name` | Location name. | `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. | `latitude (deg)` | Latitude of the plant.
| `longitude (deg)` | Longitude of the plant. | `longitude (deg)` | Longitude of the plant.
| `capacity (tonne)` | Capacity of the plant at this point in time. | `capacity (tonne)` | Capacity of the plant at this point in time.
@@ -47,7 +45,9 @@ sns.barplot(x="year",
.reset_index()); .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): * Map showing plant locations (in Python):
```python ```python
@@ -67,21 +67,20 @@ points = gp.points_from_xy(data["longitude (deg)"],
gp.GeoDataFrame(data, geometry=points).plot(ax=ax); 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 ## Plant outputs report
Report showing amount of products produced, sent and disposed of by each plant, as well as disposal costs. Report showing amount of products produced, sent and disposed of by each plant, as well as disposal costs. Generated by `RELOG.write_plant_outputs_report(solution, filename)`.
Generated by `RELOG.write_plant_outputs_report(solution, filename)`. For a concrete example, see [nimh_plant_outputs.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plant_outputs.csv).
| Column | Description | Column | Description
|:--------------------------------------|---------------| |:--------------------------------------|:---------------|
| `plant type` | Plant type. | `plant type` | Plant type.
| `location name` | Location name. | `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. | `product name` | Product being produced.
| `amount produced (tonne)` | Amount of product produced this year. | `amount produced (tonne)` | Amount of product produced this year.
| `amount sent (tonne)` | Amount of product produced by this plant and sent to another plant for further processing this year. | `amount 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()); .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 ## Plant emissions report
Report showing amount of emissions produced by each plant. Report showing amount of emissions produced by each plant. Generated by `RELOG.write_plant_emissions_report(solution, filename)`.
Generated by `RELOG.write_plant_emissions_report(solution, filename)`. For a concrete example, see [nimh_plant_emissions.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_plant_emissions.csv).
| Column | Description | Column | Description
|:--------------------------------------|---------------| |:--------------------------------------|:---------------|
| `plant type` | Plant type. | `plant type` | Plant type.
| `location name` | Location name. | `location name` | Location name.
| `year` | Year. | `year` | Year.
@@ -139,17 +138,33 @@ sns.barplot(x="plant type",
.reset_index()); .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.
| `amount disposed (tonne)` | Amount of product disposed of at this collection center.
| `marginal cost ($/tonne)` | Cost to process one additional tonne of this product coming from this collection center.
## Transportation report ## Transportation report
Report showing amount of product sent from initial locations to plants, and from one plant to another. Includes the distance between each pair of locations, amount-distance shipped, transportation costs and energy expenditure. Report showing amount of product sent from initial locations to plants, and from one plant to another. Includes the distance between each pair of locations, amount-distance shipped, transportation costs and energy expenditure. Generated by `RELOG.write_transportation_report(solution, filename)`.
Generated by `RELOG.write_transportation_report(solution, filename)`. For a concrete example, see [nimh_transportation.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_transportation.csv).
| Column | Description | 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 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 location name` | Name of the location where the product is being shipped from.
| `source latitude (deg)` | Latitude of the source location. | `source latitude (deg)` | Latitude of the source location.
@@ -183,7 +198,9 @@ sns.barplot(x="product",
.reset_index()); .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): * Map of transportation lines (in Python):
@@ -226,17 +243,17 @@ gp.GeoDataFrame(data, geometry=points).plot(ax=ax,
markersize=50); markersize=50);
``` ```
<img src="../images/ex_transportation.png" width="1000px"/> ```@raw html
<img src="../assets/ex_transportation.png" width="1000px"/>
```
## Transportation emissions report ## Transportation emissions report
Report showing emissions for each trip between initial locations and plants, and between pairs of plants. Report showing emissions for each trip between initial locations and plants, and between pairs of plants. Generated by `RELOG.write_transportation_emissions_report(solution, filename)`.
Generated by `RELOG.write_transportation_emissions_report(solution, filename)`. For a concrete example, see [nimh_transportation_emissions.csv](https://github.com/ANL-CEEESA/RELOG/blob/master/test/fixtures/nimh_transportation_emissions.csv).
| Column | Description | 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 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 location name` | Name of the location where the product is being shipped from.
| `source latitude (deg)` | Latitude of the source location. | `source latitude (deg)` | Latitude of the source location.
@@ -270,4 +287,6 @@ sns.barplot(x="emission type",
.reset_index()); .reset_index());
``` ```
<img src="../images/ex_transportation_emissions.png" width="500px"/> ```@raw html
<img src="../assets/ex_transportation_emissions.png" width="500px"/>
```

View File

@@ -3,22 +3,17 @@ Usage
## 1. Installation ## 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 ```julia
(@v1.5) pkg> add https://github.com/ANL-CEEESA/RELOG.git using Pkg
Pkg.add(name="RELOG", version="0.5")
``` ```
After the package and all its dependencies have been installed, please run the RELOG test suite, as shown below, to make sure that the package has been correctly installed: 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 ```julia
(@v1.5) pkg> test RELOG Pkg.test("RELOG")
```
To update the package to a newer version, type `]` to enter the package manager mode, then run:
```text
(@v1.5) pkg> update RELOG
``` ```
## 2. Modeling the problem ## 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). 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: By default, RELOG internally uses [Cbc](https://github.com/coin-or/Cbc), an open-source and freely-available Mixed-Integer Linear Programming solver developed by the [COIN-OR Project](https://www.coin-or.org/). For larger-scale test cases, a commercial solver such as Gurobi, CPLEX or XPRESS is recommended. The following snippet shows how to switch from Cbc to Gurobi, for example:
```julia ```julia
using RELOG, Gurobi, JuMP using RELOG, Gurobi, JuMP
gurobi = optimizer_with_attributes(Gurobi.Optimizer, gurobi = optimizer_with_attributes(
"TimeLimit" => 3600, Gurobi.Optimizer,
"MIPGap" => 0.001) "TimeLimit" => 3600,
"MIPGap" => 0.001,
)
RELOG.solve("instance.json", RELOG.solve(
output="solution.json", "instance.json",
optimizer=gurobi) 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: 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 ```julia
using RELOG using RELOG
solution = RELOG.solve("/home/user/instance.json", solution = RELOG.solve(
heuristic=true) "/home/user/instance.json",
heuristic=true,
)
``` ```

View File

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

View File

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

75
juliaw Executable file
View File

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

View File

@@ -20,6 +20,7 @@ include("model/solve.jl")
include("reports/plant_emissions.jl") include("reports/plant_emissions.jl")
include("reports/plant_outputs.jl") include("reports/plant_outputs.jl")
include("reports/plants.jl") include("reports/plants.jl")
include("reports/products.jl")
include("reports/tr_emissions.jl") include("reports/tr_emissions.jl")
include("reports/tr.jl") include("reports/tr.jl")
include("reports/write.jl") include("reports/write.jl")

View File

@@ -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;
}

View File

@@ -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"]
});

View File

@@ -7,7 +7,7 @@ using Geodesy
function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64 function calculate_distance(source_lat, source_lon, dest_lat, dest_lon)::Float64
x = LLA(source_lat, source_lon, 0.0) x = LLA(source_lat, source_lon, 0.0)
y = LLA(dest_lat, dest_lon, 0.0) y = LLA(dest_lat, dest_lon, 0.0)
return round(distance(x, y) / 1000.0, digits = 2) return round(euclidean_distance(x, y) / 1000.0, digits = 2)
end end
function build_graph(instance::Instance)::Graph function build_graph(instance::Instance)::Graph
@@ -17,6 +17,9 @@ function build_graph(instance::Instance)::Graph
plant_shipping_nodes = ShippingNode[] plant_shipping_nodes = ShippingNode[]
collection_shipping_nodes = ShippingNode[] collection_shipping_nodes = ShippingNode[]
name_to_process_node_map = Dict{Tuple{AbstractString,AbstractString},ProcessNode}()
collection_center_to_node = Dict()
process_nodes_by_input_product = process_nodes_by_input_product =
Dict(product => ProcessNode[] for product in instance.products) Dict(product => ProcessNode[] for product in instance.products)
shipping_nodes_by_plant = Dict(plant => [] for plant in instance.plants) shipping_nodes_by_plant = Dict(plant => [] for plant in instance.plants)
@@ -25,6 +28,7 @@ function build_graph(instance::Instance)::Graph
for center in instance.collection_centers for center in instance.collection_centers
node = ShippingNode(next_index, center, center.product, [], []) node = ShippingNode(next_index, center, center.product, [], [])
next_index += 1 next_index += 1
collection_center_to_node[center] = node
push!(collection_shipping_nodes, node) push!(collection_shipping_nodes, node)
end end
@@ -35,6 +39,8 @@ function build_graph(instance::Instance)::Graph
push!(process_nodes, pn) push!(process_nodes, pn)
push!(process_nodes_by_input_product[plant.input], 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) for product in keys(plant.output)
sn = ShippingNode(next_index, plant, product, [], []) sn = ShippingNode(next_index, plant, product, [], [])
next_index += 1 next_index += 1
@@ -53,7 +59,7 @@ function build_graph(instance::Instance)::Graph
dest.location.longitude, dest.location.longitude,
) )
values = Dict("distance" => distance) values = Dict("distance" => distance)
arc = Arc(source, dest, values) arc = Arc(length(arcs) + 1, source, dest, values)
push!(source.outgoing_arcs, arc) push!(source.outgoing_arcs, arc)
push!(dest.incoming_arcs, arc) push!(dest.incoming_arcs, arc)
push!(arcs, arc) push!(arcs, arc)
@@ -66,12 +72,32 @@ function build_graph(instance::Instance)::Graph
for dest in shipping_nodes_by_plant[plant] for dest in shipping_nodes_by_plant[plant]
weight = plant.output[dest.product] weight = plant.output[dest.product]
values = Dict("weight" => weight) values = Dict("weight" => weight)
arc = Arc(source, dest, values) arc = Arc(length(arcs) + 1, source, dest, values)
push!(source.outgoing_arcs, arc) push!(source.outgoing_arcs, arc)
push!(dest.incoming_arcs, arc) push!(dest.incoming_arcs, arc)
push!(arcs, arc) push!(arcs, arc)
end end
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,
collection_center_to_node,
)
end
function print_graph_stats(instance::Instance, graph::Graph)::Nothing
@info @sprintf(" %12d time periods", instance.time)
@info @sprintf(" %12d process nodes", length(graph.process_nodes))
@info @sprintf(" %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
@info @sprintf(
" %12d shipping nodes (collection)",
length(graph.collection_shipping_nodes)
)
@info @sprintf(" %12d arcs", length(graph.arcs))
return
end end

View File

@@ -7,6 +7,7 @@ using Geodesy
abstract type Node end abstract type Node end
mutable struct Arc mutable struct Arc
index::Int
source::Node source::Node
dest::Node dest::Node
values::Dict{String,Float64} values::Dict{String,Float64}
@@ -32,4 +33,14 @@ mutable struct Graph
plant_shipping_nodes::Vector{ShippingNode} plant_shipping_nodes::Vector{ShippingNode}
collection_shipping_nodes::Vector{ShippingNode} collection_shipping_nodes::Vector{ShippingNode}
arcs::Vector{Arc} arcs::Vector{Arc}
name_to_process_node_map::Dict{Tuple{AbstractString,AbstractString},ProcessNode}
collection_center_to_node::Dict{CollectionCenter,ShippingNode}
end
function Base.show(io::IO, instance::Graph)
print(io, "RELOG graph with ")
print(io, "$(length(instance.process_nodes)) process nodes, ")
print(io, "$(length(instance.plant_shipping_nodes)) plant shipping nodes, ")
print(io, "$(length(instance.collection_shipping_nodes)) collection shipping nodes, ")
print(io, "$(length(instance.arcs)) arcs")
end end

View File

@@ -29,6 +29,8 @@ function _compress(instance::Instance)::Instance
for (emission_name, emission_value) in p.transportation_emissions for (emission_name, emission_value) in p.transportation_emissions
p.transportation_emissions[emission_name] = [mean(emission_value)] p.transportation_emissions[emission_name] = [mean(emission_value)]
end end
p.disposal_limit = [maximum(p.disposal_limit) * T]
p.disposal_cost = [mean(p.disposal_cost)]
end end
# Compress collection centers # Compress collection centers
@@ -58,3 +60,42 @@ function _compress(instance::Instance)::Instance
return compressed return compressed
end end
function _slice(instance::Instance, T::UnitRange)::Instance
sliced = deepcopy(instance)
sliced.time = length(T)
for p in sliced.products
p.transportation_cost = p.transportation_cost[T]
p.transportation_energy = p.transportation_energy[T]
for (emission_name, emission_value) in p.transportation_emissions
p.transportation_emissions[emission_name] = emission_value[T]
end
p.disposal_limit = p.disposal_limit[T]
p.disposal_cost = p.disposal_cost[T]
end
for c in sliced.collection_centers
c.amount = c.amount[T]
end
for plant in sliced.plants
plant.energy = plant.energy[T]
for (emission_name, emission_value) in plant.emissions
plant.emissions[emission_name] = emission_value[T]
end
for s in plant.sizes
s.variable_operating_cost = s.variable_operating_cost[T]
s.opening_cost = s.opening_cost[T]
s.fixed_operating_cost = s.fixed_operating_cost[T]
end
for (prod_name, disp_limit) in plant.disposal_limit
plant.disposal_limit[prod_name] = disp_limit[T]
end
for (prod_name, disp_cost) in plant.disposal_cost
plant.disposal_cost[prod_name] = disp_cost[T]
end
end
return sliced
end

View File

@@ -42,7 +42,7 @@ function centroid(geom::Shapefile.Polygon)::GeoPoint
return GeoPoint(round(y_center, digits = 5), round(x_center, digits = 5)) return GeoPoint(round(y_center, digits = 5), round(x_center, digits = 5))
end end
function _download(url, output, expected_crc32)::Nothing function _download_file(url, output, expected_crc32)::Nothing
if isfile(output) if isfile(output)
return return
end end
@@ -55,13 +55,13 @@ function _download(url, output, expected_crc32)::Nothing
return return
end end
function _download_zip(url, outputdir, expected_crc32)::Nothing function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
if isdir(outputdir) if isfile(expected_output_file)
return return
end end
mkpath(outputdir) mkpath(outputdir)
@info "Downloading: $url" @info "Downloading: $url"
zip_filename = _download(url) zip_filename = download(url)
actual_crc32 = open(crc32, zip_filename) actual_crc32 = open(crc32, zip_filename)
expected_crc32 == actual_crc32 || error("CRC32 mismatch") expected_crc32 == actual_crc32 || error("CRC32 mismatch")
open(zip_filename) do zip_file open(zip_filename) do zip_file
@@ -91,8 +91,8 @@ function _geodb_load_gov_census(;
csv_filename = "$basedir/locations.csv" csv_filename = "$basedir/locations.csv"
if !isfile(csv_filename) if !isfile(csv_filename)
# Download required files # Download required files
_download(population_url, "$basedir/population.csv", population_crc32) _download_zip(shp_url, basedir, joinpath(basedir, shp_filename), shp_crc32)
_download_zip(shp_url, basedir, shp_crc32) _download_file(population_url, "$basedir/population.csv", population_crc32)
# Read shapefile # Read shapefile
@info "Processing: $shp_filename" @info "Processing: $shp_filename"
@@ -140,12 +140,15 @@ end
# 2018 US counties # 2018 US counties
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
function _extract_cols_2018_us_county(table::Shapefile.Table, i::Int)::OrderedDict{String,Any} function _extract_cols_2018_us_county(
table::Shapefile.Table,
i::Int,
)::OrderedDict{String,Any}
return OrderedDict( return OrderedDict(
"id" => table.STATEFP[i] * table.COUNTYFP[i], "id" => table.STATEFP[i] * table.COUNTYFP[i],
"statefp" => table.STATEFP[i], "statefp" => table.STATEFP[i],
"countyfp" => table.COUNTYFP[i], "countyfp" => table.COUNTYFP[i],
"name" => table.NAME[i] "name" => table.NAME[i],
) )
end end
@@ -168,25 +171,6 @@ function _geodb_load_2018_us_county()::Dict{String,GeoRegion}
) )
end end
# # 2018 US ZIP codes
# # -----------------------------------------------------------------------------
# function _extract_cols_2018_us_zcta(table::Shapefile.Table, i::Int)::OrderedDict{String,Any}
# return OrderedDict("id" => table.ZCTA5CE10[i])
# end
# function _geodb_load_2018_us_zcta()::Dict{String,GeoRegion}
# return _geodb_load_gov_census(
# db_name = "2018-us-zcta",
# extract_cols = _extract_cols_2018_us_zcta,
# shp_crc32 = 0x6391f5fc,
# shp_filename = "cb_2018_us_zcta510_500k.shp",
# shp_url = "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_zcta510_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",
# )
# end
# US States # US States
# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------
function _extract_cols_us_state(table::Shapefile.Table, i::Int)::OrderedDict{String,Any} function _extract_cols_us_state(table::Shapefile.Table, i::Int)::OrderedDict{String,Any}
@@ -218,7 +202,6 @@ end
function geodb_load(db_name::AbstractString)::Dict{String,GeoRegion} function geodb_load(db_name::AbstractString)::Dict{String,GeoRegion}
db_name == "2018-us-county" && return _geodb_load_2018_us_county() db_name == "2018-us-county" && return _geodb_load_2018_us_county()
db_name == "2018-us-zcta" && return _geodb_load_2018_us_zcta()
db_name == "us-state" && return _geodb_load_us_state() db_name == "us-state" && return _geodb_load_us_state()
error("Unknown database: $db_name") error("Unknown database: $db_name")
end end

View File

@@ -37,6 +37,8 @@ function parse(json)::Instance
cost = product_dict["transportation cost (\$/km/tonne)"] cost = product_dict["transportation cost (\$/km/tonne)"]
energy = zeros(T) energy = zeros(T)
emissions = Dict() emissions = Dict()
disposal_limit = zeros(T)
disposal_cost = zeros(T)
if "transportation energy (J/km/tonne)" in keys(product_dict) if "transportation energy (J/km/tonne)" in keys(product_dict)
energy = product_dict["transportation energy (J/km/tonne)"] energy = product_dict["transportation energy (J/km/tonne)"]
@@ -46,7 +48,25 @@ function parse(json)::Instance
emissions = product_dict["transportation emissions (tonne/km/tonne)"] emissions = product_dict["transportation emissions (tonne/km/tonne)"]
end end
product = Product(product_name, cost, energy, emissions) if "disposal limit (tonne)" in keys(product_dict)
disposal_limit = product_dict["disposal limit (tonne)"]
end
if "disposal cost (\$/tonne)" in keys(product_dict)
disposal_cost = product_dict["disposal cost (\$/tonne)"]
end
prod_centers = []
product = Product(
product_name,
cost,
energy,
emissions,
disposal_limit,
disposal_cost,
prod_centers,
)
push!(products, product) push!(products, product)
prod_name_to_product[product_name] = product prod_name_to_product[product_name] = product
@@ -66,6 +86,7 @@ function parse(json)::Instance
product, product,
center_dict["amount (tonne)"], center_dict["amount (tonne)"],
) )
push!(prod_centers, center)
push!(collection_centers, center) push!(collection_centers, center)
end end
end end
@@ -100,6 +121,13 @@ function parse(json)::Instance
disposal_limit = Dict(p => [0.0 for t = 1:T] for p in keys(output)) disposal_limit = Dict(p => [0.0 for t = 1:T] for p in keys(output))
disposal_cost = Dict(p => [0.0 for t = 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 # Disposal
if "disposal" in keys(location_dict) if "disposal" in keys(location_dict)
for (product_name, disposal_dict) in location_dict["disposal"] for (product_name, disposal_dict) in location_dict["disposal"]

View File

@@ -13,6 +13,9 @@ mutable struct Product
transportation_cost::Vector{Float64} transportation_cost::Vector{Float64}
transportation_energy::Vector{Float64} transportation_energy::Vector{Float64}
transportation_emissions::Dict{String,Vector{Float64}} transportation_emissions::Dict{String,Vector{Float64}}
disposal_limit::Vector{Float64}
disposal_cost::Vector{Float64}
collection_centers::Vector
end end
mutable struct CollectionCenter mutable struct CollectionCenter

View File

@@ -12,14 +12,10 @@ function validate(json, schema)
result = JSONSchema.validate(json, schema) result = JSONSchema.validate(json, schema)
if result !== nothing if result !== nothing
if result isa JSONSchema.SingleIssue if result isa JSONSchema.SingleIssue
path = join(result.path, "") msg = "$(result.reason) in $(result.path)"
if length(path) == 0
path = "root"
end
msg = "$(result.msg) in $(path)"
else else
msg = convert(String, result) msg = convert(String, result)
end end
throw(msg) throw("Error parsing input file: $(msg)")
end end
end end

View File

@@ -2,63 +2,346 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures using JuMP, LinearAlgebra, Geodesy, ProgressBars, Printf, DataStructures, StochasticPrograms
function build_model(
function build_model(instance::Instance, graph::Graph, optimizer)::JuMP.Model instance::Instance,
model = Model(optimizer) graph::Graph,
model[:instance] = instance optimizer,
model[:graph] = graph )
create_vars!(model) return build_model(
create_objective_function!(model) instance,
create_shipping_node_constraints!(model) [graph],
create_process_node_constraints!(model) [1.0],
return model optimizer=optimizer,
method=:ef,
)
end end
function build_model(
instance::Instance,
graphs::Vector{Graph},
probs::Vector{Float64};
optimizer,
method=:ef,
tol=0.1,
)
T = instance.time
function create_vars!(model::JuMP.Model) @stochastic_model model begin
graph, T = model[:graph], model[:instance].time # Stage 1: Build plants
model[:flow] = # =====================================================================
Dict((a, t) => @variable(model, lower_bound = 0) for a in graph.arcs, t = 1:T) @stage 1 begin
model[:dispose] = Dict( pn = graphs[1].process_nodes
(n, t) => @variable( PN = length(pn)
model,
lower_bound = 0, # Var: open_plant
upper_bound = n.location.disposal_limit[n.product][t] @decision(
) for n in values(graph.plant_shipping_nodes), t = 1:T model,
) open_plant[n in 1:PN, t in 1:T],
model[:store] = Dict( binary = true,
(n, t) => )
@variable(model, lower_bound = 0, upper_bound = n.location.storage_limit)
for n in values(graph.process_nodes), t = 1:T # Var: is_open
) @decision(
model[:process] = Dict( model,
(n, t) => @variable(model, lower_bound = 0) for is_open[n in 1:PN, t in 1:T],
n in values(graph.process_nodes), t = 1:T binary = true,
) )
model[:open_plant] = Dict(
(n, t) => @variable(model, binary = true) for n in values(graph.process_nodes), # Objective function
t = 1:T @objective(
) model,
model[:is_open] = Dict( Min,
(n, t) => @variable(model, binary = true) for n in values(graph.process_nodes),
t = 1:T # Opening, fixed operating costs
) sum(
model[:capacity] = Dict( pn[n].location.sizes[1].opening_cost[t] * open_plant[n, t] +
(n, t) => @variable( pn[n].location.sizes[1].fixed_operating_cost[t] * is_open[n, t]
model, for n in 1:PN
lower_bound = 0, for t in 1:T
upper_bound = n.location.sizes[2].capacity ),
) for n in values(graph.process_nodes), t = 1:T )
)
model[:expansion] = Dict( for t = 1:T, n in 1:PN
(n, t) => @variable( # Plant is currently open if it was already open in the previous time period or
model, # if it was built just now
lower_bound = 0, if t > 1
upper_bound = n.location.sizes[2].capacity - n.location.sizes[1].capacity @constraint(
) for n in values(graph.process_nodes), t = 1:T model,
) is_open[n, t] == is_open[n, t-1] + open_plant[n, t]
)
else
@constraint(model, is_open[n, t] == open_plant[n, t])
end
# Plant can only be opened during building period
if t instance.building_period
@constraint(model, open_plant[n, t] == 0)
end
end
end
# Stage 2: Flows, disposal, capacity & storage
# =====================================================================
@stage 2 begin
@uncertain graph
pn = graph.process_nodes
psn = graph.plant_shipping_nodes
csn = graph.collection_shipping_nodes
arcs = graph.arcs
A = length(arcs)
PN = length(pn)
CSN = length(csn)
PSN = length(psn)
# Var: flow
@recourse(
model,
flow[a in 1:A, t in 1:T],
lower_bound = 0,
)
# Var: plant_dispose
@recourse(
model,
plant_dispose[n in 1:PSN, t in 1:T],
lower_bound = 0,
upper_bound = psn[n].location.disposal_limit[psn[n].product][t],
)
# Var: collection_dispose
@recourse(
model,
collection_dispose[n in 1:CSN, t in 1:T],
lower_bound = 0,
upper_bound = graph.collection_shipping_nodes[n].location.amount[t],
)
# Var: collection_shortfall
@recourse(
model,
collection_shortfall[n in 1:CSN, t in 1:T],
lower_bound = 0,
)
# Var: store
@recourse(
model,
store[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = pn[n].location.storage_limit,
)
# Var: process
@recourse(
model,
process[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
)
# Var: capacity
@recourse(
model,
capacity[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = pn[n].location.sizes[2].capacity,
)
# Var: expansion
@recourse(
model,
expansion[
n in 1:PN,
t in 1:T,
],
lower_bound = 0,
upper_bound = (
pn[n].location.sizes[2].capacity -
pn[n].location.sizes[1].capacity
),
)
# Objective function
@objective(
model,
Min,
sum(
# Transportation costs
pn[n].location.input.transportation_cost[t] *
a.values["distance"] *
flow[a.index,t]
for n in 1:PN
for a in pn[n].incoming_arcs
for t in 1:T
) + sum(
# Fixed operating costs (expansion)
slope_fix_oper_cost(pn[n].location, t) * expansion[n, t] +
# Processing costs
pn[n].location.sizes[1].variable_operating_cost[t] * process[n, t] +
# Storage costs
pn[n].location.storage_cost[t] * store[n, t] +
# Expansion costs
(
t < T ? (
(
slope_open(pn[n].location, t) -
slope_open(pn[n].location, t + 1)
) * expansion[n, t]
) : slope_open(pn[n].location, t) * expansion[n, t]
)
for n in 1:PN
for t in 1:T
) + sum(
# Disposal costs (plants)
psn[n].location.disposal_cost[psn[n].product][t] * plant_dispose[n, t]
for n in 1:PSN
for t in 1:T
) + sum(
# Disposal costs (collection centers)
csn[n].location.product.disposal_cost[t] * collection_dispose[n, t]
for n in 1:CSN
for t in 1:T
) + sum(
# Collection shortfall
1e4 * collection_shortfall[n, t]
for n in 1:CSN
for t in 1:T
)
)
# Process node constraints
for t = 1:T, n in 1:PN
node = pn[n]
# Output amount is implied by amount processed
for arc in node.outgoing_arcs
@constraint(
model,
flow[arc.index, t] == arc.values["weight"] * process[n, t]
)
end
# If plant is closed, capacity is zero
@constraint(
model,
capacity[n, t] <= node.location.sizes[2].capacity * is_open[n, t]
)
# If plant is open, capacity is greater than base
@constraint(
model,
capacity[n, t] >= node.location.sizes[1].capacity * is_open[n, t]
)
# Capacity is linked to expansion
@constraint(
model,
capacity[n, t] <=
node.location.sizes[1].capacity + expansion[n, t]
)
# Can only process up to capacity
@constraint(model, process[n, t] <= capacity[n, t])
if t > 1
# Plant capacity can only increase over time
@constraint(model, capacity[n, t] >= capacity[n, t-1])
@constraint(model, expansion[n, t] >= expansion[n, t-1])
end
# Amount received equals amount processed plus stored
store_in = 0
if t > 1
store_in = store[n, t-1]
end
if t == T
@constraint(model, store[n, t] == 0)
end
@constraint(
model,
sum(
flow[arc.index, t]
for arc in node.incoming_arcs
) + store_in == store[n, t] + process[n, t]
)
end
# Material flow at collection shipping nodes
@constraint(
model,
eq_balance_centers[
n in 1:CSN,
t in 1:T,
],
sum(
flow[arc.index, t]
for arc in csn[n].outgoing_arcs
) == csn[n].location.amount[t] - collection_dispose[n, t] - collection_shortfall[n, t]
)
# Material flow at plant shipping nodes
@constraint(
model,
eq_balance_plant[
n in 1:PSN,
t in 1:T,
],
sum(flow[a.index, t] for a in psn[n].incoming_arcs) ==
sum(flow[a.index, t] for a in psn[n].outgoing_arcs) +
plant_dispose[n, t]
)
# Enforce product disposal limit at collection centers
for t in 1:T, prod in instance.products
if isempty(prod.collection_centers)
continue
end
@constraint(
model,
sum(
collection_dispose[n, t]
for n in 1:CSN
if csn[n].product.name == prod.name
) <= prod.disposal_limit[t]
)
end
end
end
ξ = [
@scenario graph = graphs[i] probability = probs[i]
for i in 1:length(graphs)
]
if method == :ef
sp = instantiate(model, ξ; optimizer=optimizer)
elseif method == :lshaped
sp = instantiate(model, ξ; optimizer=LShaped.Optimizer)
set_optimizer_attribute(sp, MasterOptimizer(), optimizer)
set_optimizer_attribute(sp, SubProblemOptimizer(), optimizer)
set_optimizer_attribute(sp, RelativeTolerance(), tol)
else
error("unknown method: $method")
end
return sp
end end
@@ -79,172 +362,3 @@ function slope_fix_oper_cost(plant, t)
(plant.sizes[2].capacity - plant.sizes[1].capacity) (plant.sizes[2].capacity - plant.sizes[1].capacity)
end end
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

View File

@@ -2,12 +2,33 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures using JuMP, LinearAlgebra, Geodesy, ProgressBars, Printf, DataStructures
function get_solution(
instance,
graph,
model,
scenario_index::Int=1;
marginal_costs=false,
)
value(x) = StochasticPrograms.value(x, scenario_index)
ivalue(x) = StochasticPrograms.value(x)
shadow_price(x) = StochasticPrograms.shadow_price(x, scenario_index)
function get_solution(model::JuMP.Model; marginal_costs = true)
graph, instance = model[:graph], model[:instance]
T = instance.time T = instance.time
pn = graph.process_nodes
psn = graph.plant_shipping_nodes
csn = graph.collection_shipping_nodes
arcs = graph.arcs
A = length(arcs)
PN = length(pn)
CSN = length(csn)
PSN = length(psn)
flow = model[2, :flow]
output = OrderedDict( output = OrderedDict(
"Plants" => OrderedDict(), "Plants" => OrderedDict(),
"Products" => OrderedDict(), "Products" => OrderedDict(),
@@ -29,34 +50,52 @@ function get_solution(model::JuMP.Model; marginal_costs = true)
), ),
) )
plant_to_process_node = OrderedDict(n.location => n for n in graph.process_nodes) pn = graph.process_nodes
plant_to_shipping_nodes = OrderedDict() psn = graph.plant_shipping_nodes
for p in instance.plants
plant_to_shipping_nodes[p] = [] plant_to_process_node_index = OrderedDict(
for a in plant_to_process_node[p].outgoing_arcs pn[n].location => n
push!(plant_to_shipping_nodes[p], a.dest) for n in 1:length(pn)
end )
plant_to_shipping_node_indices = OrderedDict(p => [] for p in instance.plants)
for n in 1:length(psn)
push!(plant_to_shipping_node_indices[psn[n].location], n)
end end
# Products # Products
if marginal_costs for n in 1:CSN
for n in graph.collection_shipping_nodes node = csn[n]
location_dict = OrderedDict{Any,Any}( location_dict = OrderedDict{Any,Any}(
"Marginal cost (\$/tonne)" => [ "Latitude (deg)" => node.location.latitude,
round(abs(JuMP.shadow_price(model[:eq_balance][n, t])), digits = 2) for t = 1:T "Longitude (deg)" => node.location.longitude,
], "Amount (tonne)" => node.location.amount,
) "Dispose (tonne)" => [
if n.product.name keys(output["Products"]) value(model[2, :collection_dispose][n, t])
output["Products"][n.product.name] = OrderedDict() for t = 1:T
end ],
output["Products"][n.product.name][n.location.name] = location_dict "Disposal cost (\$)" => [
value(model[2, :collection_dispose][n, t]) *
node.location.product.disposal_cost[t]
for t = 1:T
]
)
if marginal_costs
location_dict["Marginal cost (\$/tonne)"] = [
round(abs(shadow_price(model[2, :eq_balance_centers][n, t])), digits=2) for t = 1:T
]
end end
if node.product.name keys(output["Products"])
output["Products"][node.product.name] = OrderedDict()
end
output["Products"][node.product.name][node.location.name] = location_dict
end end
# Plants # Plants
for plant in instance.plants for plant in instance.plants
skip_plant = true skip_plant = true
process_node = plant_to_process_node[plant] n = plant_to_process_node_index[plant]
process_node = pn[n]
plant_dict = OrderedDict{Any,Any}( plant_dict = OrderedDict{Any,Any}(
"Input" => OrderedDict(), "Input" => OrderedDict(),
"Output" => "Output" =>
@@ -67,39 +106,39 @@ function get_solution(model::JuMP.Model; marginal_costs = true)
"Latitude (deg)" => plant.latitude, "Latitude (deg)" => plant.latitude,
"Longitude (deg)" => plant.longitude, "Longitude (deg)" => plant.longitude,
"Capacity (tonne)" => "Capacity (tonne)" =>
[JuMP.value(model[:capacity][process_node, t]) for t = 1:T], [value(model[2, :capacity][n, t]) for t = 1:T],
"Opening cost (\$)" => [ "Opening cost (\$)" => [
JuMP.value(model[:open_plant][process_node, t]) * ivalue(model[1, :open_plant][n, t]) *
plant.sizes[1].opening_cost[t] for t = 1:T plant.sizes[1].opening_cost[t] for t = 1:T
], ],
"Fixed operating cost (\$)" => [ "Fixed operating cost (\$)" => [
JuMP.value(model[:is_open][process_node, t]) * ivalue(model[1, :is_open][n, t]) *
plant.sizes[1].fixed_operating_cost[t] + plant.sizes[1].fixed_operating_cost[t] +
JuMP.value(model[:expansion][process_node, t]) * value(model[2, :expansion][n, t]) *
slope_fix_oper_cost(plant, t) for t = 1:T slope_fix_oper_cost(plant, t) for t = 1:T
], ],
"Expansion cost (\$)" => [ "Expansion cost (\$)" => [
( (
if t == 1 if t == 1
slope_open(plant, t) * JuMP.value(model[:expansion][process_node, t]) slope_open(plant, t) * value(model[2, :expansion][n, t])
else else
slope_open(plant, t) * ( slope_open(plant, t) * (
JuMP.value(model[:expansion][process_node, t]) - value(model[2, :expansion][n, t]) -
JuMP.value(model[:expansion][process_node, t-1]) value(model[2, :expansion][n, t-1])
) )
end end
) for t = 1:T ) for t = 1:T
], ],
"Process (tonne)" => "Process (tonne)" =>
[JuMP.value(model[:process][process_node, t]) for t = 1:T], [value(model[2, :process][n, t]) for t = 1:T],
"Variable operating cost (\$)" => [ "Variable operating cost (\$)" => [
JuMP.value(model[:process][process_node, t]) * value(model[2, :process][n, t]) *
plant.sizes[1].variable_operating_cost[t] for t = 1:T plant.sizes[1].variable_operating_cost[t] for t = 1:T
], ],
"Storage (tonne)" => "Storage (tonne)" =>
[JuMP.value(model[:store][process_node, t]) for t = 1:T], [value(model[2, :store][n, t]) for t = 1:T],
"Storage cost (\$)" => [ "Storage cost (\$)" => [
JuMP.value(model[:store][process_node, t]) * plant.storage_cost[t] value(model[2, :store][n, t]) * plant.storage_cost[t]
for t = 1:T for t = 1:T
], ],
) )
@@ -112,7 +151,7 @@ function get_solution(model::JuMP.Model; marginal_costs = true)
# Inputs # Inputs
for a in process_node.incoming_arcs for a in process_node.incoming_arcs
vals = [JuMP.value(model[:flow][a, t]) for t = 1:T] vals = [value(flow[a.index, t]) for t = 1:T]
if sum(vals) <= 1e-3 if sum(vals) <= 1e-3
continue continue
end end
@@ -170,18 +209,20 @@ function get_solution(model::JuMP.Model; marginal_costs = true)
end end
# Outputs # Outputs
for shipping_node in plant_to_shipping_nodes[plant] for n2 in plant_to_shipping_node_indices[plant]
shipping_node = psn[n2]
product_name = shipping_node.product.name product_name = shipping_node.product.name
plant_dict["Total output"][product_name] = zeros(T) plant_dict["Total output"][product_name] = zeros(T)
plant_dict["Output"]["Send"][product_name] = product_dict = OrderedDict() plant_dict["Output"]["Send"][product_name] = product_dict = OrderedDict()
disposal_amount = [JuMP.value(model[:dispose][shipping_node, t]) for t = 1:T] disposal_amount =
[value(model[2, :plant_dispose][n2, t]) for t = 1:T]
if sum(disposal_amount) > 1e-5 if sum(disposal_amount) > 1e-5
skip_plant = false skip_plant = false
plant_dict["Output"]["Dispose"][product_name] = plant_dict["Output"]["Dispose"][product_name] =
disposal_dict = OrderedDict() disposal_dict = OrderedDict()
disposal_dict["Amount (tonne)"] = disposal_dict["Amount (tonne)"] =
[JuMP.value(model[:dispose][shipping_node, t]) for t = 1:T] [value(model[2, :plant_dispose][n2, t]) for t = 1:T]
disposal_dict["Cost (\$)"] = [ disposal_dict["Cost (\$)"] = [
disposal_dict["Amount (tonne)"][t] * disposal_dict["Amount (tonne)"][t] *
plant.disposal_cost[shipping_node.product][t] for t = 1:T plant.disposal_cost[shipping_node.product][t] for t = 1:T
@@ -191,7 +232,7 @@ function get_solution(model::JuMP.Model; marginal_costs = true)
end end
for a in shipping_node.outgoing_arcs for a in shipping_node.outgoing_arcs
vals = [JuMP.value(model[:flow][a, t]) for t = 1:T] vals = [value(flow[a.index, t]) for t = 1:T]
if sum(vals) <= 1e-3 if sum(vals) <= 1e-3
continue continue
end end

View File

@@ -2,26 +2,18 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures using JuMP, LinearAlgebra, Geodesy, HiGHS, ProgressBars, Printf, DataStructures
default_milp_optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0) function _get_default_milp_optimizer()
default_lp_optimizer = optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0) return optimizer_with_attributes(HiGHS.Optimizer)
end
function solve( function _get_default_lp_optimizer()
instance::Instance; return optimizer_with_attributes(HiGHS.Optimizer)
optimizer = nothing, end
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..." function _print_graph_stats(instance::Instance, graph::Graph)::Nothing
graph = RELOG.build_graph(instance)
@info @sprintf(" %12d time periods", instance.time) @info @sprintf(" %12d time periods", instance.time)
@info @sprintf(" %12d process nodes", length(graph.process_nodes)) @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 (plant)", length(graph.plant_shipping_nodes))
@@ -30,49 +22,100 @@ function solve(
length(graph.collection_shipping_nodes) length(graph.collection_shipping_nodes)
) )
@info @sprintf(" %12d arcs", length(graph.arcs)) @info @sprintf(" %12d arcs", length(graph.arcs))
return
end
@info "Building optimization model..." function solve_stochastic(;
model = RELOG.build_model(instance, graph, milp_optimizer) scenarios::Vector{String},
probs::Vector{Float64},
optimizer,
method=:ef,
tol=0.1,
)
@info "Reading instance files..."
instances = [parsefile(sc) for sc in scenarios]
@info "Optimizing MILP..." @info "Building graphs..."
JuMP.optimize!(model) graphs = [build_graph(inst) for inst in instances]
@info "Building stochastic model..."
sp = RELOG.build_model(instances[1], graphs, probs; optimizer, method, tol)
@info "Optimizing stochastic model..."
optimize!(sp)
@info "Extracting solution..."
solutions = [
get_solution(instances[i], graphs[i], sp, i)
for i in 1:length(instances)
]
return solutions
end
function solve(
instance::Instance;
optimizer=HiGHS.Optimizer,
marginal_costs=true,
return_model=false
)
@info "Building graph..."
graph = RELOG.build_graph(instance)
_print_graph_stats(instance, graph)
@info "Building model..."
model = RELOG.build_model(instance, [graph], [1.0]; optimizer)
@info "Optimizing model..."
optimize!(model)
if !has_values(model) if !has_values(model)
@warn "No solution available" error("No solution available")
return OrderedDict()
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 end
@info "Extracting solution..." @info "Extracting solution..."
solution = get_solution(model, marginal_costs = marginal_costs) solution = get_solution(instance, graph, model, 1)
if output != nothing if marginal_costs
write(solution, output) @info "Re-optimizing with integer variables fixed..."
open_plant_vals = value.(model[1, :open_plant])
is_open_vals = value.(model[1, :is_open])
for n in 1:length(graph.process_nodes), t in 1:instance.time
unset_binary(model[1, :open_plant][n, t])
unset_binary(model[1, :is_open][n, t])
fix(
model[1, :open_plant][n, t],
open_plant_vals[n, t]
)
fix(
model[1, :is_open][n, t],
is_open_vals[n, t]
)
end
optimize!(model)
if has_values(model)
@info "Extracting solution..."
solution = get_solution(instance, graph, model, 1, marginal_costs=true)
else
@warn "Error computing marginal costs. Ignoring."
end
end end
return solution if return_model
return solution, model
else
return solution
end
end end
function solve(filename::AbstractString; heuristic = false, kwargs...) function solve(filename::AbstractString; heuristic=false, kwargs...)
@info "Reading $filename..." @info "Reading $filename..."
instance = RELOG.parsefile(filename) instance = RELOG.parsefile(filename)
if heuristic && instance.time > 1 if heuristic && instance.time > 1
@info "Solving single-period version..." @info "Solving single-period version..."
compressed = _compress(instance) compressed = _compress(instance)
csol = solve(compressed; output = nothing, marginal_costs = false, kwargs...) csol, model = solve(compressed; marginal_costs=false, return_model=true, kwargs...)
@info "Filtering candidate locations..." @info "Filtering candidate locations..."
selected_pairs = [] selected_pairs = []
for (plant_name, plant_dict) in csol["Plants"] for (plant_name, plant_dict) in csol["Plants"]

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

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

View File

@@ -169,6 +169,12 @@
}, },
"initial amounts": { "initial amounts": {
"$ref": "#/definitions/InitialAmount" "$ref": "#/definitions/InitialAmount"
},
"disposal limit (tonne)": {
"$ref": "#/definitions/TimeSeries"
},
"disposal cost ($/tonne)": {
"$ref": "#/definitions/TimeSeries"
} }
}, },
"required": [ "required": [

View File

@@ -1,15 +1,30 @@
using PackageCompiler using PackageCompiler
using TOML
using Logging
using Cbc Logging.disable_logging(Logging.Info)
using Clp
using Geodesy
using JSON
using JSONSchema
using JuMP
using MathOptInterface
using ProgressBars
pkg = [:Cbc, :Clp, :Geodesy, :JSON, :JSONSchema, :JuMP, :MathOptInterface, :ProgressBars] mkpath("build")
@info "Building system image..." printstyled("Generating precompilation statements...\n", color = :light_green)
create_sysimage(pkg, sysimage_path = "build/sysimage.so") run(`julia --project=. --trace-compile=build/precompile.jl $ARGS`)
printstyled("Finding dependencies...\n", color = :light_green)
project = TOML.parsefile("Project.toml")
manifest = TOML.parsefile("Manifest.toml")
deps = Symbol[]
for dep in keys(project["deps"])
if "path" in keys(manifest[dep][1])
printstyled(" skip $(dep)\n", color = :light_black)
else
println(" add $(dep)")
push!(deps, Symbol(dep))
end
end
printstyled("Building system image...\n", color = :light_green)
create_sysimage(
deps,
precompile_statements_file = "build/precompile.jl",
sysimage_path = "build/sysimage.so",
)

File diff suppressed because it is too large Load Diff

File diff suppressed because it is too large Load Diff

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

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

View File

@@ -202,8 +202,7 @@
} }
}, },
"L2": { "L2": {
"latitude (deg)": 0.5, "location": "2018-us-county:17043",
"longitude (deg)": 0.5,
"capacities (tonne)": { "capacities (tonne)": {
"0.0": { "0.0": {
"opening cost ($)": [ "opening cost ($)": [

View File

@@ -3,37 +3,38 @@
using RELOG using RELOG
@testset "build_graph" begin function graph_build_test()
basedir = dirname(@__FILE__) @testset "build_graph" begin
instance = RELOG.parsefile("$basedir/../../instances/s1.json") instance = RELOG.parsefile(fixture("instances/s1.json"))
graph = RELOG.build_graph(instance) graph = RELOG.build_graph(instance)
process_node_by_location_name = process_node_by_location_name =
Dict(n.location.location_name => n for n in graph.process_nodes) Dict(n.location.location_name => n for n in graph.process_nodes)
@test length(graph.plant_shipping_nodes) == 8 @test length(graph.plant_shipping_nodes) == 8
@test length(graph.collection_shipping_nodes) == 10 @test length(graph.collection_shipping_nodes) == 10
@test length(graph.process_nodes) == 6 @test length(graph.process_nodes) == 6
node = graph.collection_shipping_nodes[1] node = graph.collection_shipping_nodes[1]
@test node.location.name == "C1" @test node.location.name == "C1"
@test length(node.incoming_arcs) == 0 @test length(node.incoming_arcs) == 0
@test length(node.outgoing_arcs) == 2 @test length(node.outgoing_arcs) == 2
@test node.outgoing_arcs[1].source.location.name == "C1" @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.plant_name == "F1"
@test node.outgoing_arcs[1].dest.location.location_name == "L1" @test node.outgoing_arcs[1].dest.location.location_name == "L1"
@test node.outgoing_arcs[1].values["distance"] == 1095.62 @test node.outgoing_arcs[1].values["distance"] == 1095.62
node = process_node_by_location_name["L1"] node = process_node_by_location_name["L1"]
@test node.location.plant_name == "F1" @test node.location.plant_name == "F1"
@test node.location.location_name == "L1" @test node.location.location_name == "L1"
@test length(node.incoming_arcs) == 10 @test length(node.incoming_arcs) == 10
@test length(node.outgoing_arcs) == 2 @test length(node.outgoing_arcs) == 2
node = process_node_by_location_name["L3"] node = process_node_by_location_name["L3"]
@test node.location.plant_name == "F2" @test node.location.plant_name == "F2"
@test node.location.location_name == "L3" @test node.location.location_name == "L3"
@test length(node.incoming_arcs) == 2 @test length(node.incoming_arcs) == 2
@test length(node.outgoing_arcs) == 2 @test length(node.outgoing_arcs) == 2
@test length(graph.arcs) == 38 @test length(graph.arcs) == 38
end
end end

View File

@@ -3,51 +3,52 @@
using RELOG using RELOG
@testset "compress" begin function compress_test()
basedir = dirname(@__FILE__) @testset "compress" begin
instance = RELOG.parsefile("$basedir/../../instances/s1.json") instance = RELOG.parsefile(fixture("instances/s1.json"))
compressed = RELOG._compress(instance) compressed = RELOG._compress(instance)
product_name_to_product = Dict(p.name => p for p in compressed.products) product_name_to_product = Dict(p.name => p for p in compressed.products)
location_name_to_facility = Dict() location_name_to_facility = Dict()
for p in compressed.plants for p in compressed.plants
location_name_to_facility[p.location_name] = p 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
for c in compressed.collection_centers
location_name_to_facility[c.name] = c
end
p1 = product_name_to_product["P1"]
p2 = product_name_to_product["P2"]
p3 = product_name_to_product["P3"]
c1 = location_name_to_facility["C1"]
l1 = location_name_to_facility["L1"]
@test compressed.time == 1
@test compressed.building_period == [1]
@test p1.name == "P1"
@test p1.transportation_cost [0.015]
@test p1.transportation_energy [0.115]
@test p1.transportation_emissions["CO2"] [0.051]
@test p1.transportation_emissions["CH4"] [0.0025]
@test c1.name == "C1"
@test c1.amount [1869.12]
@test l1.plant_name == "F1"
@test l1.location_name == "L1"
@test l1.energy [0.115]
@test l1.emissions["CO2"] [0.051]
@test l1.emissions["CH4"] [0.0025]
@test l1.sizes[1].opening_cost [500]
@test l1.sizes[2].opening_cost [1250]
@test l1.sizes[1].fixed_operating_cost [60]
@test l1.sizes[2].fixed_operating_cost [60]
@test l1.sizes[1].variable_operating_cost [30]
@test l1.sizes[2].variable_operating_cost [30]
@test l1.disposal_limit[p2] [2.0]
@test l1.disposal_limit[p3] [2.0]
@test l1.disposal_cost[p2] [-10.0]
@test l1.disposal_cost[p3] [-10.0]
end end

View File

@@ -4,22 +4,24 @@
using RELOG using RELOG
@testset "geodb_query (2018-us-county)" begin function geodb_test()
region = RELOG.geodb_query("2018-us-county:17043") @testset "geodb_query (2018-us-county)" begin
@test region.centroid.lat == 41.83956 region = RELOG.geodb_query("2018-us-county:17043")
@test region.centroid.lon == -88.08857 @test region.centroid.lat == 41.83956
@test region.population == 922_921 @test region.centroid.lon == -88.08857
end @test region.population == 922_921
end
# @testset "geodb_query (2018-us-zcta)" begin # @testset "geodb_query (2018-us-zcta)" begin
# region = RELOG.geodb_query("2018-us-zcta:60439") # region = RELOG.geodb_query("2018-us-zcta:60439")
# @test region.centroid.lat == 41.68241 # @test region.centroid.lat == 41.68241
# @test region.centroid.lon == -87.98954 # @test region.centroid.lon == -87.98954
# end # end
@testset "geodb_query (us-state)" begin @testset "geodb_query (us-state)" begin
region = RELOG.geodb_query("us-state:IL") region = RELOG.geodb_query("us-state:IL")
@test region.centroid.lat == 39.73939 @test region.centroid.lat == 39.73939
@test region.centroid.lon == -89.50414 @test region.centroid.lon == -89.50414
@test region.population == 12_671_821 @test region.population == 12_671_821
end
end end

View File

@@ -3,84 +3,90 @@
using RELOG using RELOG
@testset "parse" begin function parse_test()
basedir = dirname(@__FILE__) @testset "parse" begin
instance = RELOG.parsefile("$basedir/../../instances/s1.json") instance = RELOG.parsefile(fixture("instances/s1.json"))
centers = instance.collection_centers centers = instance.collection_centers
plants = instance.plants plants = instance.plants
products = instance.products products = instance.products
location_name_to_plant = Dict(p.location_name => p for p in plants) 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) product_name_to_product = Dict(p.name => p for p in products)
@test length(centers) == 10 @test length(centers) == 10
@test centers[1].name == "C1" @test centers[1].name == "C1"
@test centers[1].latitude == 7 @test centers[1].latitude == 7
@test centers[1].latitude == 7 @test centers[1].latitude == 7
@test centers[1].longitude == 7 @test centers[1].longitude == 7
@test centers[1].amount == [934.56, 934.56] @test centers[1].amount == [934.56, 934.56]
@test centers[1].product.name == "P1" @test centers[1].product.name == "P1"
@test length(plants) == 6 @test length(plants) == 6
plant = location_name_to_plant["L1"] plant = location_name_to_plant["L1"]
@test plant.plant_name == "F1" @test plant.plant_name == "F1"
@test plant.location_name == "L1" @test plant.location_name == "L1"
@test plant.input.name == "P1" @test plant.input.name == "P1"
@test plant.latitude == 0 @test plant.latitude == 0
@test plant.longitude == 0 @test plant.longitude == 0
@test length(plant.sizes) == 2 @test length(plant.sizes) == 2
@test plant.sizes[1].capacity == 250 @test plant.sizes[1].capacity == 250
@test plant.sizes[1].opening_cost == [500, 500] @test plant.sizes[1].opening_cost == [500, 500]
@test plant.sizes[1].fixed_operating_cost == [30, 30] @test plant.sizes[1].fixed_operating_cost == [30, 30]
@test plant.sizes[1].variable_operating_cost == [30, 30] @test plant.sizes[1].variable_operating_cost == [30, 30]
@test plant.sizes[2].capacity == 1000 @test plant.sizes[2].capacity == 1000
@test plant.sizes[2].opening_cost == [1250, 1250] @test plant.sizes[2].opening_cost == [1250, 1250]
@test plant.sizes[2].fixed_operating_cost == [30, 30] @test plant.sizes[2].fixed_operating_cost == [30, 30]
@test plant.sizes[2].variable_operating_cost == [30, 30] @test plant.sizes[2].variable_operating_cost == [30, 30]
p2 = product_name_to_product["P2"] p1 = product_name_to_product["P1"]
p3 = product_name_to_product["P3"] @test p1.disposal_limit == [1.0, 1.0]
@test length(plant.output) == 2 @test p1.disposal_cost == [-1000.0, -1000.0]
@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"] p2 = product_name_to_product["P2"]
@test plant.location_name == "L3" @test p2.disposal_limit == [0.0, 0.0]
@test plant.input.name == "P2" @test p2.disposal_cost == [0.0, 0.0]
@test plant.latitude == 25
@test plant.longitude == 65
@test length(plant.sizes) == 2 p3 = product_name_to_product["P3"]
@test plant.sizes[1].capacity == 1000.0 @test length(plant.output) == 2
@test plant.sizes[1].opening_cost == [3000, 3000] @test plant.output[p2] == 0.2
@test plant.sizes[1].fixed_operating_cost == [50, 50] @test plant.output[p3] == 0.5
@test plant.sizes[1].variable_operating_cost == [50, 50] @test plant.disposal_limit[p2] == [1, 1]
@test plant.sizes[1] == plant.sizes[2] @test plant.disposal_limit[p3] == [1, 1]
@test plant.disposal_cost[p2] == [-10, -10]
@test plant.disposal_cost[p3] == [-10, -10]
p4 = product_name_to_product["P4"] plant = location_name_to_plant["L3"]
@test plant.output[p3] == 0.05 @test plant.location_name == "L3"
@test plant.output[p4] == 0.8 @test plant.input.name == "P2"
@test plant.disposal_limit[p3] == [1e8, 1e8] @test plant.latitude == 25
@test plant.disposal_limit[p4] == [0, 0] @test plant.longitude == 65
@test length(plant.sizes) == 2
@test plant.sizes[1].capacity == 1000.0
@test plant.sizes[1].opening_cost == [3000, 3000]
@test plant.sizes[1].fixed_operating_cost == [50, 50]
@test plant.sizes[1].variable_operating_cost == [50, 50]
@test plant.sizes[1] == plant.sizes[2]
p4 = product_name_to_product["P4"]
@test plant.output[p3] == 0.05
@test plant.output[p4] == 0.8
@test plant.disposal_limit[p3] == [1e8, 1e8]
@test plant.disposal_limit[p4] == [0, 0]
end
@testset "parse (geodb)" begin
instance = RELOG.parsefile(fixture("instances/s2.json"))
centers = instance.collection_centers
@test centers[1].name == "C1"
@test centers[1].latitude == 41.83956
@test centers[1].longitude == -88.08857
end
# @testset "parse (invalid)" begin
# @test_throws ErrorException RELOG.parsefile(fixture("s1-wrong-length.json"))
# end
end end
@testset "parse (geodb)" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../../instances/s2.json")
centers = instance.collection_centers
@test centers[1].name == "C1"
@test centers[1].latitude == 41.83956
@test centers[1].longitude == -88.08857
end
# @testset "parse (invalid)" begin
# basedir = dirname(@__FILE__)
# @test_throws ErrorException RELOG.parsefile("$basedir/../fixtures/s1-wrong-length.json")
# end

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

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@@ -1,61 +1,85 @@
# Copyright (C) 2020 Argonne National Laboratory # Copyright (C) 2020 Argonne National Laboratory
# Written by Alinson Santos Xavier <axavier@anl.gov> # Written by Alinson Santos Xavier <axavier@anl.gov>
using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats using RELOG, JuMP, Printf, JSON, MathOptInterface.FileFormats
basedir = dirname(@__FILE__) basedir = dirname(@__FILE__)
@testset "solve (exact)" begin function model_solve_test()
solution_filename_a = tempname() @testset "solve (exact)" begin
solution_filename_b = tempname() solution = RELOG.solve(fixture("instances/s1.json"))
solution = RELOG.solve("$basedir/../../instances/s1.json", output = solution_filename_a)
@test isfile(solution_filename_a) solution_filename = tempname()
RELOG.write(solution, solution_filename)
@test isfile(solution_filename)
RELOG.write(solution, solution_filename_b) @test "Costs" in keys(solution)
@test isfile(solution_filename_b) @test "Fixed operating (\$)" in keys(solution["Costs"])
@test "Transportation (\$)" in keys(solution["Costs"])
@test "Variable operating (\$)" in keys(solution["Costs"])
@test "Total (\$)" in keys(solution["Costs"])
@test "Costs" in keys(solution) @test "Plants" in keys(solution)
@test "Fixed operating (\$)" in keys(solution["Costs"]) @test "F1" in keys(solution["Plants"])
@test "Transportation (\$)" in keys(solution["Costs"]) @test "F2" in keys(solution["Plants"])
@test "Variable operating (\$)" in keys(solution["Costs"]) @test "F3" in keys(solution["Plants"])
@test "Total (\$)" in keys(solution["Costs"]) @test "F4" in keys(solution["Plants"])
@test "Plants" in keys(solution) @test "Products" in keys(solution)
@test "F1" in keys(solution["Plants"]) @test "P1" in keys(solution["Products"])
@test "F2" in keys(solution["Plants"]) @test "C1" in keys(solution["Products"]["P1"])
@test "F3" in keys(solution["Plants"]) @test "Dispose (tonne)" in keys(solution["Products"]["P1"]["C1"])
@test "F4" in keys(solution["Plants"])
end
@testset "solve (heuristic)" begin total_disposal =
# Should not crash sum([loc["Dispose (tonne)"] for loc in values(solution["Products"]["P1"])])
solution = RELOG.solve("$basedir/../../instances/s1.json", heuristic = true) @test total_disposal == [1.0, 1.0]
end end
@testset "solve (infeasible)" begin @testset "solve (heuristic)" begin
json = JSON.parsefile("$basedir/../../instances/s1.json") # Should not crash
for (location_name, location_dict) in json["products"]["P1"]["initial amounts"] solution = RELOG.solve(fixture("instances/s1.json"), heuristic = true)
location_dict["amount (tonne)"] *= 1000 end
# @testset "solve (infeasible)" begin
# json = JSON.parsefile(fixture("instances/s1.json"))
# for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
# location_dict["amount (tonne)"] *= 1000
# end
# @test_throws ErrorException("No solution available") RELOG.solve(RELOG.parse(json))
# end
@testset "solve (with storage)" begin
basedir = dirname(@__FILE__)
filename = "$basedir/../fixtures/storage.json"
instance = RELOG.parsefile(filename)
@test instance.plants[1].storage_limit == 50.0
@test instance.plants[1].storage_cost == [2.0, 1.5, 1.0]
solution = RELOG.solve(filename)
plant_dict = solution["Plants"]["mega plant"]["Chicago"]
@test plant_dict["Variable operating cost (\$)"] == [500.0, 0.0, 100.0]
@test plant_dict["Process (tonne)"] == [50.0, 0.0, 50.0]
@test plant_dict["Storage (tonne)"] == [50.0, 50.0, 0.0]
@test plant_dict["Storage cost (\$)"] == [100.0, 75.0, 0.0]
@test solution["Costs"]["Variable operating (\$)"] == [500.0, 0.0, 100.0]
@test solution["Costs"]["Storage (\$)"] == [100.0, 75.0, 0.0]
@test solution["Costs"]["Total (\$)"] == [600.0, 75.0, 100.0]
end
@testset "solve (stochastic)" begin
# Should not crash
solutions = RELOG.solve_stochastic(
scenarios=[
fixture("instances/case3_p010_s1.00.json"),
fixture("instances/case3_p010_s1.25.json"),
],
probs=[0.5, 0.5],
optimizer=optimizer_with_attributes(
HiGHS.Optimizer,
"log_to_console" => false,
),
method=:lshaped,
)
end end
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 end

View File

@@ -4,15 +4,20 @@
using RELOG, JSON, GZip using RELOG, JSON, GZip
@testset "Reports" begin basedir = @__DIR__
@testset "from solve" begin
solution = RELOG.solve("$(pwd())/../instances/s1.json") function reports_test()
tmp_filename = tempname() @testset "Reports" begin
# The following should not crash @testset "from solve" begin
RELOG.write_plants_report(solution, tmp_filename) solution = RELOG.solve(fixture("instances/s1.json"))
RELOG.write_plant_outputs_report(solution, tmp_filename) tmp_filename = tempname()
RELOG.write_plant_emissions_report(solution, tmp_filename) # The following should not crash
RELOG.write_transportation_report(solution, tmp_filename) RELOG.write_plant_emissions_report(solution, tmp_filename)
RELOG.write_transportation_emissions_report(solution, tmp_filename) RELOG.write_plant_outputs_report(solution, tmp_filename)
RELOG.write_plants_report(solution, tmp_filename)
RELOG.write_products_report(solution, tmp_filename)
RELOG.write_transportation_emissions_report(solution, tmp_filename)
RELOG.write_transportation_report(solution, tmp_filename)
end
end end
end end

View File

@@ -2,19 +2,46 @@
# Written by Alinson Santos Xavier <axavier@anl.gov> # Written by Alinson Santos Xavier <axavier@anl.gov>
using Test using Test
using RELOG
using Revise
@testset "RELOG" begin includet("instance/compress_test.jl")
@testset "Instance" begin includet("instance/geodb_test.jl")
include("instance/compress_test.jl") includet("instance/parse_test.jl")
include("instance/geodb_test.jl") includet("graph/build_test.jl")
include("instance/parse_test.jl") includet("model/build_test.jl")
includet("model/solve_test.jl")
includet("reports_test.jl")
function fixture(path)
for candidate in [
"fixtures/$path",
"test/fixtures/$path"
]
if isfile(candidate)
return candidate
end
end end
@testset "Graph" begin error("Fixture not found: $path")
include("graph/build_test.jl")
end
@testset "Model" begin
include("model/build_test.jl")
include("model/solve_test.jl")
end
include("reports_test.jl")
end end
function runtests()
@testset "RELOG" begin
@testset "Instance" begin
compress_test()
geodb_test()
parse_test()
end
@testset "Graph" begin
graph_build_test()
end
@testset "Model" begin
model_build_test()
model_solve_test()
end
reports_test()
end
return
end
runtests()