mirror of https://github.com/ANL-CEEESA/RELOG.git
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GPG Key ID:
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||||
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||||
|
||||
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|
||||
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||||
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||||
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||||
|
||||
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|
||||
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||||
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||||
[[deps.NearestNeighbors]]
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[[deps.Printf]]
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[[deps.Profile]]
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deps = ["Printf"]
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||||
[[deps.REPL]]
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deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
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uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
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||||
[[deps.Random]]
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||||
deps = ["SHA", "Serialization"]
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||||
[[deps.Reexport]]
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git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
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|
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[[deps.Requires]]
|
||||
deps = ["UUIDs"]
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git-tree-sha1 = "838a3a4188e2ded87a4f9f184b4b0d78a1e91cb7"
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||||
version = "1.3.0"
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||||
|
||||
[[deps.Revise]]
|
||||
deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "Requires", "UUIDs", "Unicode"]
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git-tree-sha1 = "12aa2d7593df490c407a3bbd8b86b8b515017f3e"
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uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
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version = "3.5.14"
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|
||||
[[deps.SHA]]
|
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uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
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|
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|
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[[deps.SentinelArrays]]
|
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deps = ["Dates", "Random"]
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git-tree-sha1 = "0e7508ff27ba32f26cd459474ca2ede1bc10991f"
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[[deps.Serialization]]
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[[deps.Sockets]]
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uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
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[[deps.SortingAlgorithms]]
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deps = ["DataStructures"]
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git-tree-sha1 = "66e0a8e672a0bdfca2c3f5937efb8538b9ddc085"
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|
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[[deps.SparseArrays]]
|
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deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
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uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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|
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[[deps.SpecialFunctions]]
|
||||
deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
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git-tree-sha1 = "e2cfc4012a19088254b3950b85c3c1d8882d864d"
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version = "2.3.1"
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|
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[deps.SpecialFunctions.extensions]
|
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SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
|
||||
|
||||
[deps.SpecialFunctions.weakdeps]
|
||||
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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[[deps.StaticArrays]]
|
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deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"]
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git-tree-sha1 = "bf074c045d3d5ffd956fa0a461da38a44685d6b2"
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|
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[deps.StaticArrays.extensions]
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StaticArraysChainRulesCoreExt = "ChainRulesCore"
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StaticArraysStatisticsExt = "Statistics"
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||||
|
||||
[deps.StaticArrays.weakdeps]
|
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ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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[[deps.StaticArraysCore]]
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[[deps.SuiteSparse_jll]]
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[[deps.TOML]]
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||||
|
||||
[[deps.TranscodingStreams]]
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||||
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weakdeps = ["Random", "Test"]
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|
||||
[deps.TranscodingStreams.extensions]
|
||||
TestExt = ["Test", "Random"]
|
||||
|
||||
[[deps.UUIDs]]
|
||||
deps = ["Random", "SHA"]
|
||||
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
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|
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[[deps.Unicode]]
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||||
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|
||||
[[deps.WeakRefStrings]]
|
||||
deps = ["DataAPI", "InlineStrings", "Parsers"]
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|
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[[deps.WorkerUtilities]]
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git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
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[[deps.ZipFile]]
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deps = ["Libdl", "Printf", "Zlib_jll"]
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git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
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[[deps.Zlib_jll]]
|
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deps = ["Libdl"]
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|
||||
[[deps.libblastrampoline_jll]]
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|
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|
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|
||||
[[deps.p7zip_jll]]
|
||||
deps = ["Artifacts", "Libdl"]
|
||||
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
|
||||
version = "17.4.0+0"
|
||||
@ -0,0 +1,13 @@
|
||||
[deps]
|
||||
CRC = "44b605c4-b955-5f2b-9b6d-d2bd01d3d205"
|
||||
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
|
||||
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
|
||||
Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
|
||||
Gurobi = "2e9cd046-0924-5485-92f1-d5272153d98b"
|
||||
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
|
||||
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
|
||||
NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
|
||||
OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
|
||||
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
|
||||
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
|
||||
ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
|
||||
@ -0,0 +1,107 @@
|
||||
# RELOG: Reverse Logistics Optimization
|
||||
# Copyright (C) 2020-2024, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
using Geodesy
|
||||
using NearestNeighbors
|
||||
using DataFrames
|
||||
using CRC
|
||||
using ZipFile
|
||||
using Statistics
|
||||
|
||||
crc32 = crc(CRC_32)
|
||||
|
||||
abstract type DistanceMetric end
|
||||
|
||||
Base.@kwdef mutable struct KnnDrivingDistance <: DistanceMetric
|
||||
tree = nothing
|
||||
ratios = nothing
|
||||
end
|
||||
|
||||
mutable struct EuclideanDistance <: DistanceMetric end
|
||||
|
||||
function _calculate_distance(
|
||||
source_lat,
|
||||
source_lon,
|
||||
dest_lat,
|
||||
dest_lon,
|
||||
::EuclideanDistance,
|
||||
)::Float64
|
||||
x = LLA(source_lat, source_lon, 0.0)
|
||||
y = LLA(dest_lat, dest_lon, 0.0)
|
||||
return round(euclidean_distance(x, y) / 1000.0, digits=3)
|
||||
end
|
||||
|
||||
function _download_file(url, output, expected_crc32)::Nothing
|
||||
if isfile(output)
|
||||
return
|
||||
end
|
||||
mkpath(dirname(output))
|
||||
@info "Downloading: $url"
|
||||
fname = download(url)
|
||||
actual_crc32 = open(crc32, fname)
|
||||
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
|
||||
cp(fname, output)
|
||||
return
|
||||
end
|
||||
|
||||
function _download_zip(url, outputdir, expected_output_file, expected_crc32)::Nothing
|
||||
if isfile(expected_output_file)
|
||||
return
|
||||
end
|
||||
mkpath(outputdir)
|
||||
@info "Downloading: $url"
|
||||
zip_filename = download(url)
|
||||
actual_crc32 = open(crc32, zip_filename)
|
||||
expected_crc32 == actual_crc32 || error("CRC32 mismatch")
|
||||
open(zip_filename) do zip_file
|
||||
zr = ZipFile.Reader(zip_file)
|
||||
for file in zr.files
|
||||
open(joinpath(outputdir, file.name), "w") do output_file
|
||||
write(output_file, read(file))
|
||||
end
|
||||
end
|
||||
end
|
||||
return
|
||||
end
|
||||
|
||||
function _calculate_distance(
|
||||
source_lat,
|
||||
source_lon,
|
||||
dest_lat,
|
||||
dest_lon,
|
||||
metric::KnnDrivingDistance,
|
||||
)::Float64
|
||||
if metric.tree === nothing
|
||||
basedir = joinpath(dirname(@__FILE__), "data")
|
||||
csv_filename = joinpath(basedir, "dist_driving.csv")
|
||||
|
||||
# Download pre-computed driving data
|
||||
if !isfile(csv_filename)
|
||||
_download_zip(
|
||||
"https://axavier.org/RELOG/0.6/data/dist_driving_0b9a6ad6.zip",
|
||||
basedir,
|
||||
csv_filename,
|
||||
0x0b9a6ad6,
|
||||
)
|
||||
end
|
||||
|
||||
# Fit kNN model
|
||||
df = DataFrame(CSV.File(csv_filename, missingstring="NaN"))
|
||||
dropmissing!(df)
|
||||
coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
|
||||
metric.ratios = Matrix(df[!, [:ratio]])
|
||||
metric.tree = KDTree(coords)
|
||||
end
|
||||
|
||||
# Compute Euclidean distance
|
||||
dist_euclidean =
|
||||
_calculate_distance(source_lat, source_lon, dest_lat, dest_lon, EuclideanDistance())
|
||||
|
||||
# Predict ratio
|
||||
idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
|
||||
ratio_pred = mean(metric.ratios[idxs])
|
||||
dist_pred = round(dist_euclidean * ratio_pred, digits=3)
|
||||
isfinite(dist_pred) || error("non-finite distance detected: $dist_pred")
|
||||
return dist_pred
|
||||
end
|
||||
@ -0,0 +1,48 @@
|
||||
# This file extends some JuMP functions so that decision variables can be safely
|
||||
# replaced by (constant) floating point numbers.
|
||||
|
||||
using Printf
|
||||
using JuMP
|
||||
using OrderedCollections
|
||||
|
||||
import JuMP: value, fix, set_name
|
||||
|
||||
function value(x::Float64)
|
||||
return x
|
||||
end
|
||||
|
||||
function fix(x::Float64, v::Float64; force)
|
||||
return abs(x - v) < 1e-6 || error("Value mismatch: $x != $v")
|
||||
end
|
||||
|
||||
function set_name(x::Number, n::String)
|
||||
# nop
|
||||
end
|
||||
|
||||
function _init(model::JuMP.Model, key::Symbol)::OrderedDict
|
||||
if !(key in keys(object_dictionary(model)))
|
||||
model[key] = OrderedDict()
|
||||
end
|
||||
return model[key]
|
||||
end
|
||||
|
||||
function _set_names!(model::JuMP.Model)
|
||||
@info "Setting variable and constraint names..."
|
||||
time_varnames = @elapsed begin
|
||||
_set_names!(object_dictionary(model))
|
||||
end
|
||||
@info @sprintf("Set names in %.2f seconds", time_varnames)
|
||||
end
|
||||
|
||||
function _set_names!(dict::Dict)
|
||||
for name in keys(dict)
|
||||
dict[name] isa AbstractDict || continue
|
||||
for idx in keys(dict[name])
|
||||
if dict[name][idx] isa AffExpr
|
||||
continue
|
||||
end
|
||||
idx_str = join(map(string, idx), ",")
|
||||
set_name(dict[name][idx], "$name[$idx_str]")
|
||||
end
|
||||
end
|
||||
end
|
||||
@ -0,0 +1,159 @@
|
||||
using JuMP
|
||||
using OrderedCollections
|
||||
using Gurobi
|
||||
using Random
|
||||
using Printf
|
||||
|
||||
dict = OrderedDict
|
||||
|
||||
macro pprint(var)
|
||||
quote
|
||||
println(string($(QuoteNode(var))))
|
||||
v = round.($(esc(var)), digits=2)
|
||||
display(v)
|
||||
println()
|
||||
end
|
||||
end
|
||||
|
||||
macro pprint_jump(var)
|
||||
quote
|
||||
println(string($(QuoteNode(var))))
|
||||
v = round.(value.($(esc(var))), digits=2)
|
||||
display(v)
|
||||
println()
|
||||
end
|
||||
end
|
||||
|
||||
function model1()
|
||||
Random.seed!(42)
|
||||
|
||||
model = Model(
|
||||
optimizer_with_attributes(
|
||||
Gurobi.Optimizer,
|
||||
"NonConvex" => 2,
|
||||
),
|
||||
)
|
||||
|
||||
# Data
|
||||
# -------------------------------------------------------------------------
|
||||
n_plants = 5
|
||||
n_components = 2
|
||||
plants = 1:n_plants
|
||||
components = 1:n_components
|
||||
initial_amount = [rand(1:1000) for _ in plants, _ in components]
|
||||
revenue = [rand(1:1000) for _ in plants]
|
||||
tr_cost = [
|
||||
rand(1:50)
|
||||
for _ in plants, _ in plants
|
||||
]
|
||||
|
||||
@show plants
|
||||
@show components
|
||||
@show initial_amount
|
||||
@show revenue
|
||||
|
||||
# Decision variables
|
||||
# -------------------------------------------------------------------------
|
||||
@variable(model, y_total[plants, plants], lower_bound = 0)
|
||||
@variable(model, y[plants, plants, components], lower_bound = 0)
|
||||
@variable(model, z_disp_total[plants], lower_bound = 0)
|
||||
@variable(model, z_disp[plants, components], lower_bound = 0)
|
||||
@variable(model, z_avail_total[plants])
|
||||
@variable(model, z_avail[plants, components])
|
||||
@variable(model, alpha[plants, components])
|
||||
|
||||
# Objective
|
||||
# -------------------------------------------------------------------------
|
||||
@objective(
|
||||
model,
|
||||
Max,
|
||||
sum(
|
||||
z_disp_total[p] * revenue[p]
|
||||
for p in plants
|
||||
)
|
||||
-
|
||||
sum(
|
||||
y_total[p, q] * tr_cost[p, q]
|
||||
for p in plants, q in plants
|
||||
)
|
||||
)
|
||||
|
||||
# Constraints
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
# Definition of total sent
|
||||
@constraint(
|
||||
model,
|
||||
eq_y_total_def[p in plants, q in plants],
|
||||
y_total[p, q] == sum(y[p, q, c] for c in components)
|
||||
)
|
||||
|
||||
# Definition of total disposed
|
||||
@constraint(
|
||||
model,
|
||||
eq_z_disp_total_def[p in plants], z_disp_total[p] == sum(z_disp[p, c] for c in components)
|
||||
)
|
||||
|
||||
# Definition of available amount
|
||||
@constraint(
|
||||
model,
|
||||
eq_z_avail_total[p in plants],
|
||||
z_avail_total[p] == sum(z_avail[p, c] for c in components)
|
||||
)
|
||||
|
||||
# Definition of available component
|
||||
@constraint(
|
||||
model,
|
||||
eq_z_avail[p in plants, c in components],
|
||||
z_avail[p, c] == initial_amount[p, c] + sum(y[q, p, c] for q in plants)
|
||||
)
|
||||
|
||||
# Mass balance
|
||||
@constraint(
|
||||
model,
|
||||
eq_balance[p in plants],
|
||||
z_avail_total[p] == z_disp_total[p] + sum(y_total[p, q] for q in plants)
|
||||
)
|
||||
|
||||
# Available proportion
|
||||
@constraint(
|
||||
model,
|
||||
eq_alpha_avail[p in plants, c in components],
|
||||
z_avail[p, c] == alpha[p, c] * z_avail_total[p]
|
||||
)
|
||||
|
||||
# Sending proportion
|
||||
@constraint(
|
||||
model,
|
||||
eq_alpha_send[p in plants, q in plants, c in components],
|
||||
y[p, q, c] == alpha[p, c] * y_total[p, q]
|
||||
)
|
||||
|
||||
# Disposal proportion
|
||||
@constraint(
|
||||
model,
|
||||
eq_alpha_disp[p in plants, c in components],
|
||||
z_disp[p, c] == alpha[p, c] * z_disp_total[p]
|
||||
)
|
||||
|
||||
# Run
|
||||
# -------------------------------------------------------------------------
|
||||
print(model)
|
||||
optimize!(model)
|
||||
|
||||
# Print solution
|
||||
# -------------------------------------------------------------------------
|
||||
@pprint initial_amount
|
||||
@pprint revenue
|
||||
@pprint tr_cost
|
||||
|
||||
@pprint_jump y_total
|
||||
@pprint_jump y
|
||||
@pprint_jump z_disp_total
|
||||
@pprint_jump z_disp
|
||||
@pprint_jump z_avail_total
|
||||
@pprint_jump z_avail
|
||||
@pprint_jump alpha
|
||||
end
|
||||
|
||||
model1()
|
||||
@ -0,0 +1,282 @@
|
||||
using JuMP
|
||||
using OrderedCollections
|
||||
using Gurobi
|
||||
using Random
|
||||
using Printf
|
||||
using DataFrames
|
||||
using CSV
|
||||
|
||||
dict = OrderedDict
|
||||
|
||||
function model2()
|
||||
Random.seed!(42)
|
||||
|
||||
model = Model(
|
||||
optimizer_with_attributes(
|
||||
Gurobi.Optimizer,
|
||||
),
|
||||
)
|
||||
|
||||
# Data
|
||||
# -------------------------------------------------------------------------
|
||||
components = ["film", "paper", "cardboard"]
|
||||
centers = [
|
||||
"Chicago",
|
||||
"New York City",
|
||||
"Los Angeles",
|
||||
"Houston",
|
||||
"Phoenix",
|
||||
"Philadelphia",
|
||||
"San Antonio",
|
||||
"San Diego",
|
||||
"Dallas",
|
||||
"San Jose",
|
||||
]
|
||||
plants = [
|
||||
"Chicago",
|
||||
"Phoenix",
|
||||
"Dallas",
|
||||
]
|
||||
products = [
|
||||
"film bale",
|
||||
"cardboard bale"
|
||||
]
|
||||
initial_amount = dict(
|
||||
(q, c) => rand(1:1000)
|
||||
for q in centers, c in components
|
||||
)
|
||||
cost_tr = dict(
|
||||
(q, p) => rand(1:10)
|
||||
for q in centers, p in plants
|
||||
)
|
||||
cost_open = dict(
|
||||
p => rand(5000:10000)
|
||||
for p in plants
|
||||
)
|
||||
cost_var = dict(
|
||||
p => rand(5:10)
|
||||
for p in plants
|
||||
)
|
||||
revenue = dict(
|
||||
(p, m) => rand(10:20)
|
||||
for p in plants, m in products
|
||||
)
|
||||
alpha = dict(
|
||||
"film bale" => dict(
|
||||
"film" => dict(
|
||||
"film" => 0.98,
|
||||
"paper" => 0,
|
||||
"cardboard" => 0,
|
||||
),
|
||||
"paper" => dict(
|
||||
"film" => 0,
|
||||
"paper" => 0.02,
|
||||
"cardboard" => 0,
|
||||
),
|
||||
"cardboard" => dict(
|
||||
"film" => 0,
|
||||
"paper" => 0,
|
||||
"cardboard" => 0.02,
|
||||
),
|
||||
),
|
||||
"cardboard bale" => dict(
|
||||
"film" => dict(
|
||||
"film" => 0.0,
|
||||
"paper" => 0.0,
|
||||
"cardboard" => 0.0,
|
||||
),
|
||||
"paper" => dict(
|
||||
"film" => 0.0,
|
||||
"paper" => 0.02,
|
||||
"cardboard" => 0.0,
|
||||
),
|
||||
"cardboard" => dict(
|
||||
"film" => 0.0,
|
||||
"paper" => 0.0,
|
||||
"cardboard" => 0.75,
|
||||
),
|
||||
),
|
||||
)
|
||||
capacity = dict(
|
||||
p => rand(10000:50000)
|
||||
for p in plants
|
||||
)
|
||||
|
||||
# Variables
|
||||
# -------------------------------------------------------------------------
|
||||
@variable(model, y[centers, plants, components] >= 0)
|
||||
@variable(model, y_total[centers, plants])
|
||||
@variable(model, x[plants], Bin)
|
||||
@variable(model, z_avail[plants, components])
|
||||
@variable(model, z_prod[plants, products, components])
|
||||
|
||||
|
||||
# Objective
|
||||
# -------------------------------------------------------------------------
|
||||
@objective(
|
||||
model,
|
||||
Min,
|
||||
|
||||
# Transportation cost
|
||||
+ sum(
|
||||
cost_tr[q, p] * y[q, p, c]
|
||||
for p in plants, q in centers, c in components
|
||||
)
|
||||
|
||||
# Opening cost
|
||||
+ sum(
|
||||
cost_open[p] * x[p]
|
||||
for p in plants
|
||||
)
|
||||
|
||||
# Variable operating cost
|
||||
+ sum(
|
||||
cost_var[p] * y[q,p,c]
|
||||
for q in centers, p in plants, c in components
|
||||
)
|
||||
|
||||
# Revenue
|
||||
+ sum(
|
||||
revenue[p,m] * z_prod[p,m,c]
|
||||
for p in plants, m in products, c in components
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# Constraints
|
||||
# -------------------------------------------------------------------------
|
||||
|
||||
# Flow balance at centers:
|
||||
@constraint(
|
||||
model,
|
||||
eq_flow_balance[q in centers, c in components],
|
||||
sum(y[q,p,c] for p in plants) == initial_amount[q, c]
|
||||
)
|
||||
|
||||
# Total flow:
|
||||
@constraint(
|
||||
model,
|
||||
eq_total_flow[q in centers, p in plants],
|
||||
y_total[q,p] == sum(y[q,p,c] for c in components)
|
||||
)
|
||||
|
||||
# Center balance mix:
|
||||
@constraint(
|
||||
model,
|
||||
eq_mix[q in centers, p in plants, c in components],
|
||||
y[q,p,c] == initial_amount[q,c] / sum(initial_amount[q,d] for d in components) * y_total[q,p]
|
||||
)
|
||||
|
||||
# Plant capacity
|
||||
@constraint(
|
||||
model,
|
||||
eq_capacity[p in plants],
|
||||
sum(y_total[q,p] for q in centers) <= capacity[p] * x[p]
|
||||
)
|
||||
|
||||
# Amount available
|
||||
@constraint(
|
||||
model,
|
||||
eq_z_avail[p in plants, c in components],
|
||||
z_avail[p,c] == sum(y[q,p,c] for q in centers)
|
||||
)
|
||||
|
||||
# Amount produced
|
||||
@constraint(
|
||||
model,
|
||||
eq_z_prod[p in plants, m in products, c in components],
|
||||
z_prod[p,m,c] ==
|
||||
sum(
|
||||
alpha[m][c][d] *
|
||||
z_avail[p,c]
|
||||
for d in components
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
# Run
|
||||
# -------------------------------------------------------------------------
|
||||
print(model)
|
||||
optimize!(model)
|
||||
|
||||
# Report: Transportation
|
||||
# -------------------------------------------------------------------------
|
||||
df = DataFrame()
|
||||
df."center" = String[]
|
||||
df."plant" = String[]
|
||||
df."component" = String[]
|
||||
df."amount sent (tonne)" = Float64[]
|
||||
df."transportation cost (\$)" = Float64[]
|
||||
df."variable operating cost (\$)" = Float64[]
|
||||
for q in centers, p in plants, c in components
|
||||
if value(y[q, p, c]) ≈ 0
|
||||
continue
|
||||
end
|
||||
push!(
|
||||
df,
|
||||
[
|
||||
q,
|
||||
p,
|
||||
c,
|
||||
value(y[q, p, c]),
|
||||
cost_tr[q, p] * value(y[q, p, c]),
|
||||
cost_var[p] * value(y[q,p,c])
|
||||
]
|
||||
)
|
||||
end
|
||||
CSV.write("output-2/tr.csv", df)
|
||||
|
||||
|
||||
# Report: Plant
|
||||
# -------------------------------------------------------------------------
|
||||
df = DataFrame()
|
||||
df."plant" = String[]
|
||||
df."is open?" = Float64[]
|
||||
df."capacity (tonne)" = Float64[]
|
||||
df."utilization (tonne)" = Float64[]
|
||||
df."opening cost (\$)" = Float64[]
|
||||
for p in plants
|
||||
if value(x[p]) ≈ 0
|
||||
continue
|
||||
end
|
||||
push!(
|
||||
df,
|
||||
[
|
||||
p,
|
||||
value(x[p]),
|
||||
capacity[p],
|
||||
sum(value(y_total[q,p]) for q in centers),
|
||||
cost_open[p] * value(x[p])
|
||||
]
|
||||
)
|
||||
end
|
||||
CSV.write("output-2/plant.csv", df)
|
||||
|
||||
# Report: Plant Outputs
|
||||
# -------------------------------------------------------------------------
|
||||
df = DataFrame()
|
||||
df."plant" = String[]
|
||||
df."product" = String[]
|
||||
df."component" = String[]
|
||||
df."amount produced (tonne)" = Float64[]
|
||||
df."revenue (\$)" = Float64[]
|
||||
for p in plants, m in products, c in components
|
||||
if value(z_prod[p, m, c]) ≈ 0
|
||||
continue
|
||||
end
|
||||
push!(
|
||||
df,
|
||||
[
|
||||
p,
|
||||
m,
|
||||
c,
|
||||
value(z_prod[p, m, c]),
|
||||
revenue[p,m] * value(z_prod[p,m,c])
|
||||
]
|
||||
)
|
||||
end
|
||||
CSV.write("output-2/plant-outputs.csv", df)
|
||||
|
||||
end
|
||||
|
||||
model2()
|
||||
File diff suppressed because it is too large
Load Diff
|
|
|
@ -0,0 +1,815 @@
|
||||
{
|
||||
"parameters": {
|
||||
"time horizon (years)": 2
|
||||
},
|
||||
"products": {
|
||||
"Waste": {
|
||||
"components": [
|
||||
"Film",
|
||||
"Paper",
|
||||
"Cardboard"
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"transportation cost ($/km/tonne)": [
|
||||
0.05,
|
||||
0.05
|
||||
],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Film bale": {
|
||||
"components": [
|
||||
"Film",
|
||||
"Paper",
|
||||
"Cardboard"
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"transportation cost ($/km/tonne)": [
|
||||
0.05,
|
||||
0.05
|
||||
],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Cardboard bale": {
|
||||
"components": [
|
||||
"Paper",
|
||||
"Cardboard"
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"transportation cost ($/km/tonne)": [
|
||||
0.05,
|
||||
0.05
|
||||
],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Cardboard sheets": {
|
||||
"components": [
|
||||
"Cardboard"
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"transportation cost ($/km/tonne)": [
|
||||
0.05,
|
||||
0.05
|
||||
],
|
||||
"transportation emissions (tonne/km/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"centers": {
|
||||
"Collection (Chicago)": {
|
||||
"latitude": 41.881832,
|
||||
"longitude": -87.623177,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
716.0,
|
||||
2864.0,
|
||||
1074.0
|
||||
],
|
||||
[
|
||||
1394.0,
|
||||
5576.0,
|
||||
2091.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (New York City)": {
|
||||
"latitude": 40.712776,
|
||||
"longitude": -74.005974,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
990.0,
|
||||
891.0,
|
||||
297.0
|
||||
],
|
||||
[
|
||||
6450.0,
|
||||
5805.0,
|
||||
1935.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (Los Angeles)": {
|
||||
"latitude": 34.052235,
|
||||
"longitude": -118.243683,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
4160.0,
|
||||
3640.0,
|
||||
5200.0
|
||||
],
|
||||
[
|
||||
5704.0,
|
||||
4991.0,
|
||||
7130.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (Houston)": {
|
||||
"latitude": 29.760427,
|
||||
"longitude": -95.369804,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
2668.0,
|
||||
4669.0,
|
||||
6670.0
|
||||
],
|
||||
[
|
||||
1852.0,
|
||||
3241.0,
|
||||
4630.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (Phoenix)": {
|
||||
"latitude": 33.448376,
|
||||
"longitude": -112.074036,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
3635.0,
|
||||
6543.0,
|
||||
5089.0
|
||||
],
|
||||
[
|
||||
2275.0,
|
||||
4095.0,
|
||||
3185.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (Philadelphia)": {
|
||||
"latitude": 39.952583,
|
||||
"longitude": -75.165222,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
220.0,
|
||||
880.0,
|
||||
880.0
|
||||
],
|
||||
[
|
||||
962.0,
|
||||
3848.0,
|
||||
3848.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (San Antonio)": {
|
||||
"latitude": 29.424122,
|
||||
"longitude": -98.493629,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
288.0,
|
||||
252.0,
|
||||
324.0
|
||||
],
|
||||
[
|
||||
4240.0,
|
||||
3710.0,
|
||||
4770.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (San Diego)": {
|
||||
"latitude": 32.715736,
|
||||
"longitude": -117.161087,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
6690.0,
|
||||
1338.0,
|
||||
4014.0
|
||||
],
|
||||
[
|
||||
8460.0,
|
||||
1692.0,
|
||||
5076.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (Dallas)": {
|
||||
"latitude": 32.776664,
|
||||
"longitude": -96.796988,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
5096.0,
|
||||
728.0,
|
||||
5824.0
|
||||
],
|
||||
[
|
||||
4354.0,
|
||||
622.0,
|
||||
4976.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"Collection (San Jose)": {
|
||||
"latitude": 37.338208,
|
||||
"longitude": -121.886329,
|
||||
"output": {
|
||||
"Waste": {
|
||||
"initial amount (tonne)": [
|
||||
[
|
||||
1255.0,
|
||||
2510.0,
|
||||
251.0
|
||||
],
|
||||
[
|
||||
4650.0,
|
||||
9300.0,
|
||||
930.0
|
||||
]
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
"storage cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"storage limit (tonne)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"acquisition cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
"plants": {
|
||||
"MRF (Chicago)": {
|
||||
"latitude": 41.881832,
|
||||
"longitude": -87.623177,
|
||||
"input": "Waste",
|
||||
"output": {
|
||||
"Film bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.98,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.02
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
},
|
||||
"Cardboard bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.75
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"MRF (Phoenix)": {
|
||||
"latitude": 33.448376,
|
||||
"longitude": -112.074036,
|
||||
"input": "Waste",
|
||||
"output": {
|
||||
"Film bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.98,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.02
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
},
|
||||
"Cardboard bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.75
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"MRF (Dallas)": {
|
||||
"latitude": 32.776664,
|
||||
"longitude": -96.796988,
|
||||
"input": "Waste",
|
||||
"output": {
|
||||
"Film bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.98,
|
||||
0.0,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.02
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
},
|
||||
"Cardboard bale": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.02,
|
||||
0.0
|
||||
],
|
||||
[
|
||||
0.0,
|
||||
0.0,
|
||||
0.75
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-10.0,
|
||||
-10.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Paper Mill (Chicago)": {
|
||||
"latitude": 41.881832,
|
||||
"longitude": -87.623177,
|
||||
"input": "Cardboard bale",
|
||||
"output": {
|
||||
"Cardboard sheets": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.95
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-100.0,
|
||||
-100.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Paper Mill (Phoenix)": {
|
||||
"latitude": 33.448376,
|
||||
"longitude": -112.074036,
|
||||
"input": "Cardboard bale",
|
||||
"output": {
|
||||
"Cardboard sheets": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.95
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-100.0,
|
||||
-100.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
},
|
||||
"Paper Mill (Dallas)": {
|
||||
"latitude": 32.776664,
|
||||
"longitude": -96.796988,
|
||||
"input": "Cardboard bale",
|
||||
"output": {
|
||||
"Cardboard sheets": {
|
||||
"output matrix": [
|
||||
[
|
||||
0.0,
|
||||
0.95
|
||||
]
|
||||
],
|
||||
"disposal limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
],
|
||||
"disposal cost ($/tonne)": [
|
||||
-100.0,
|
||||
-100.0
|
||||
]
|
||||
}
|
||||
},
|
||||
"fixed operating cost ($)": [
|
||||
1000.0,
|
||||
1000.0
|
||||
],
|
||||
"variable operating cost ($/tonne)": [
|
||||
1.0,
|
||||
1.0
|
||||
],
|
||||
"opening cost ($)": [
|
||||
10000.0,
|
||||
10000.0
|
||||
],
|
||||
"capacity (tonne)": 50000.0,
|
||||
"emissions (tonne/tonne)": {
|
||||
"CO2": [
|
||||
0.01,
|
||||
0.01
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"emissions": {
|
||||
"CO2": {
|
||||
"penalty ($/tonne)": [
|
||||
0.01,
|
||||
0.01
|
||||
],
|
||||
"limit (tonne)": [
|
||||
1.0e6,
|
||||
1.0e6
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|
|
|
Loading…
Reference in new issue