commit 6b1b62c658b1ad12a2654b27a78527bf2ebef0a1
Author: Alinson S. Xavier <git@axavier.org>
Date:   Wed Mar 20 10:05:49 2024 -0500

    Prototype composition model

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..1269488
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+data
diff --git a/Manifest.toml b/Manifest.toml
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+    [deps.SpecialFunctions.extensions]
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+
+    [deps.SpecialFunctions.weakdeps]
+    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+
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+    TestExt = ["Test", "Random"]
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diff --git a/Project.toml b/Project.toml
new file mode 100644
index 0000000..b0d9bb3
--- /dev/null
+++ b/Project.toml
@@ -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"
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+ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
diff --git a/dist.jl b/dist.jl
new file mode 100644
index 0000000..1008650
--- /dev/null
+++ b/dist.jl
@@ -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
diff --git a/jumpext.jl b/jumpext.jl
new file mode 100644
index 0000000..5ac3ac4
--- /dev/null
+++ b/jumpext.jl
@@ -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[$idx_str]")
+        end
+    end
+end
diff --git a/model-1.jl b/model-1.jl
new file mode 100644
index 0000000..03d94cb
--- /dev/null
+++ b/model-1.jl
@@ -0,0 +1,159 @@
+using JuMP
+using OrderedCollections
+using Gurobi
+using Random
+using Printf
+
+dict = OrderedDict
+
+macro pprint(var)
+    quote
+        println(string($(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()
\ No newline at end of file
diff --git a/model-2.jl b/model-2.jl
new file mode 100644
index 0000000..8c706ce
--- /dev/null
+++ b/model-2.jl
@@ -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()
diff --git a/model-3.jl b/model-3.jl
new file mode 100644
index 0000000..59ad962
--- /dev/null
+++ b/model-3.jl
@@ -0,0 +1,1197 @@
+# 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 CSV
+using DataFrames
+using Gurobi
+using JSON
+using JuMP
+using OrderedCollections
+using Random
+
+include("jumpext.jl")
+include("dist.jl")
+
+dict = OrderedDict
+Time = Int
+Random.seed!(42)
+
+# Structs
+# =========================================================================
+
+Base.@kwdef struct Component
+    name::String
+end
+
+Base.@kwdef struct Product
+    name::String
+    comp::Vector{Component}
+end
+
+Base.@kwdef struct Center
+    name::String
+    latitude::Float64
+    longitude::Float64
+    prod_out::Vector{Product}
+end
+
+Base.@kwdef struct Plant
+    name::String
+    latitude::Float64
+    longitude::Float64
+    prod_out::Vector{Product}
+    prod_in::Product
+end
+
+Base.@kwdef struct Emission
+    name::String
+end
+
+Base.show(
+    io::IO,
+    p::Union{Component,Product,Center,Plant,Emission},
+) = print(io, p.name)
+
+Base.@kwdef struct Instance
+    T::UnitRange{Int}
+    centers::Vector{Center}
+    plants::Vector{Plant}
+    products::Vector{Product}
+    emissions::Vector{Emission}
+    alpha_mix::dict{Tuple{Plant,Product,Component,Component},Float64}
+    alpha_plant_emission::dict{Tuple{Plant,Emission,Time},Float64}
+    alpha_tr_emission::dict{Tuple{Product,Emission,Time},Float64}
+    c_acq::dict{Tuple{Center,Product,Time},Float64}
+    c_center_disp::dict{Tuple{Center,Product,Time},Float64}
+    c_emission::dict{Tuple{Emission,Time},Float64}
+    c_fix::dict{Tuple{Plant,Time},Float64}
+    c_open::dict{Tuple{Plant,Time},Float64}
+    c_plant_disp::dict{Tuple{Plant,Product,Time},Float64}
+    c_store::dict{Tuple{Center,Product,Time},Float64}
+    c_tr::dict{Tuple{Product,Time},Float64}
+    c_var::dict{Tuple{Plant,Time},Float64}
+    m_cap::dict{Plant,Float64}
+    m_center_disp::dict{Tuple{Product,Time},Float64}
+    m_dist::dict{Tuple{Union{Center,Plant},Plant},Float64}
+    m_emission::dict{Tuple{Emission,Time},Float64}
+    m_init::dict{Tuple{Center,Product,Component,Time},Float64}
+    m_plant_disp::dict{Tuple{Plant,Product,Time},Float64}
+    m_store::dict{Tuple{Center,Product,Time},Float64}
+end
+
+
+# Generate
+# ==============================================================================
+
+function generate_data()
+    # Time window
+    T = 1:2
+
+    # Cities
+    cities_a = dict(
+        "Chicago" => [41.881832, -87.623177],
+        "New York City" => [40.712776, -74.005974],
+        "Los Angeles" => [34.052235, -118.243683],
+        "Houston" => [29.760427, -95.369804],
+        "Phoenix" => [33.448376, -112.074036],
+        "Philadelphia" => [39.952583, -75.165222],
+        "San Antonio" => [29.424122, -98.493629],
+        "San Diego" => [32.715736, -117.161087],
+        "Dallas" => [32.776664, -96.796988],
+        "San Jose" => [37.338208, -121.886329],
+    )
+
+    cities_b = dict(
+        "Chicago" => [41.881832, -87.623177],
+        "Phoenix" => [33.448376, -112.074036],
+        "Dallas" => [32.776664, -96.796988],
+    )
+
+    # Components
+    film = Component("Film")
+    paper = Component("Paper")
+    cardboard = Component("Cardboard")
+
+    # Products
+    waste = Product(
+        name="Waste",
+        comp=[film, paper, cardboard],
+    )
+    film_bale = Product(
+        name="Film bale",
+        comp=[film, paper, cardboard],
+    )
+    cardboard_bale = Product(
+        name="Cardboard bale",
+        comp=[paper, cardboard],
+    )
+    cardboard_sheets = Product(
+        name="Cardboard sheets",
+        comp=[cardboard],
+    )
+    products = [waste, film_bale, cardboard_bale, cardboard_sheets]
+
+    # Centers
+    centers = [
+        Center(
+            name="Collection ($city_name)",
+            latitude=city_lat,
+            longitude=city_lon,
+            prod_out=[waste],
+        )
+        for (city_name, (city_lat, city_lon)) in cities_a
+    ]
+
+    # Plants
+    plants_a = [
+        Plant(
+            name="MRF ($city_name)",
+            latitude=city_lat,
+            longitude=city_lon,
+            prod_in=waste,
+            prod_out=[film_bale, cardboard_bale],
+        )
+        for (city_name, (city_lat, city_lon)) in cities_b
+    ]
+    plants_b = [
+        Plant(
+            name="Paper Mill ($city_name)",
+            latitude=city_lat,
+            longitude=city_lon,
+            prod_in=cardboard_bale,
+            prod_out=[cardboard_sheets],
+        )
+        for (city_name, (city_lat, city_lon)) in cities_b
+    ]
+    plants = [plants_a; plants_b]
+
+    # Emissions
+    emissions = [
+        Emission("CO2"),
+    ]
+
+    alpha_mix = dict(
+        (p, r, cin, cout) => 0.0
+        for p in plants
+        for cin in p.prod_in.comp
+        for r in p.prod_out
+        for cout in r.comp
+    )
+    for p in plants_a
+        alpha_mix[p, film_bale, film, film] = 0.98
+        alpha_mix[p, film_bale, paper, paper] = 0.02
+        alpha_mix[p, film_bale, cardboard, cardboard] = 0.02
+        alpha_mix[p, cardboard_bale, paper, paper] = 0.02
+        alpha_mix[p, cardboard_bale, cardboard, cardboard] = 0.75
+    end
+    for p in plants_b
+        alpha_mix[p, cardboard_sheets, cardboard, cardboard] = 0.95
+    end
+    alpha_plant_emission = dict(
+        (p, s, t) => 0.01
+        for p in plants, s in emissions, t in T
+    )
+    alpha_tr_emission = dict(
+        (r, s, t) => 0.01
+        for r in products, s in emissions, t in T
+    )
+    c_acq = dict(
+        (q, r, t) => 1.0
+        for q in centers
+        for r in q.prod_out, t in T
+    )
+    c_center_disp = dict(
+        (q, r, t) => 0
+        for q in centers
+        for r in q.prod_out
+        for t in T
+    )
+    c_emission = dict(
+        (s, t) => 0.01
+        for s in emissions, t in T
+    )
+    c_fix = dict(
+        (p, t) => 1_000.0
+        for p in plants, t in T
+    )
+    c_open = dict(
+        (p, t) => 10_000.0
+        for p in plants, t in T
+    )
+    c_plant_disp = dict(
+        (p, r, t) => (r == cardboard_sheets ? -100.0 : -10.0)
+        for p in plants
+        for r in p.prod_out, t in T
+    )
+    c_store = dict(
+        (q, r, t) => 1.0
+        for q in centers
+        for r in q.prod_out, t in T
+    )
+    c_tr = dict(
+        (r, t) => 0.05
+        for r in products, t in T
+    )
+    c_var = dict(
+        (p, t) => 1.0
+        for p in plants, t in T
+    )
+    m_cap = dict(
+        p => 50000.0
+        for p in plants
+    )
+    m_center_disp = dict(
+        (r, t) => 0.0
+        for r in products, t in T
+    )
+    metric = KnnDrivingDistance()
+    m_dist = dict(
+        (p, q) => _calculate_distance(
+            p.latitude,
+            p.longitude,
+            q.latitude,
+            q.longitude,
+            metric,
+        )
+        for p in [plants; centers], q in plants
+    )
+    m_emission = dict(
+        (s, t) => 1_000_000
+        for s in emissions, t in T
+    )
+    m_init = dict()
+    for q in centers, r in q.prod_out
+        ratio = dict(
+            c => rand(1:10)
+            for c in r.comp
+        )
+        total = dict(
+            t => rand(1:1000)
+            for t in T
+        )
+        for c in r.comp, t in T
+            m_init[q, r, c, t] = ratio[c] * total[t]
+        end
+    end
+    m_plant_disp = dict(
+        (p, r, t) => 1_000_000
+        for p in plants
+        for r in p.prod_out
+        for t in T
+    )
+    m_store = dict(
+        (q, r, t) => 1_000
+        for q in centers
+        for r in q.prod_out, t in T
+    )
+
+    return Instance(;
+        T,
+        centers,
+        plants,
+        products,
+        emissions,
+        alpha_mix,
+        alpha_plant_emission,
+        alpha_tr_emission,
+        c_acq,
+        c_center_disp,
+        c_emission,
+        c_fix,
+        c_open,
+        c_plant_disp,
+        c_store,
+        c_tr,
+        c_var,
+        m_cap,
+        m_center_disp,
+        m_dist,
+        m_emission,
+        m_init,
+        m_plant_disp,
+        m_store
+    )
+end
+
+# Write
+# ==============================================================================
+
+function write_json(data, filename)
+    json = dict()
+    json["parameters"] = dict(
+        "time horizon (years)" => data.T.stop,
+    )
+    json["products"] = dict(
+        r.name => dict(
+            "components" => [c.name for c in r.comp],
+            "disposal limit (tonne)" => [
+                data.m_center_disp[r, t]
+                for t in data.T
+            ],
+            "transportation cost (\$/km/tonne)" => [
+                data.c_tr[r, t]
+                for t in data.T
+            ],
+            "transportation emissions (tonne/km/tonne)" => dict(
+                s => [
+                    data.alpha_tr_emission[r, s, t]
+                    for t in data.T
+                ]
+                for s in data.emissions
+            )
+        )
+        for r in data.products
+    )
+    json["centers"] = dict(
+        q.name => dict(
+            "latitude" => q.latitude,
+            "longitude" => q.longitude,
+            "output" => dict(
+                r.name => dict(
+                    "initial amount (tonne)" => [
+                        data.m_init[q, r, c, t]
+                        for c in r.comp, t in data.T
+                    ],
+                    "disposal cost (\$/tonne)" => [
+                        data.c_center_disp[q, r, t]
+                        for t in data.T
+                    ],
+                    "storage cost (\$/tonne)" => [
+                        data.c_store[q, r, t]
+                        for t in data.T
+                    ],
+                    "storage limit (tonne)" => [
+                        data.m_store[q, r, t]
+                        for t in data.T
+                    ],
+                    "acquisition cost (\$/tonne)" => [
+                        data.c_acq[q, r, t]
+                        for t in data.T
+                    ],
+                )
+                for r in q.prod_out
+            )
+        )
+        for q in data.centers
+    )
+    json["plants"] = dict(
+        p.name => dict(
+            "latitude" => p.latitude,
+            "longitude" => p.longitude,
+            "input" => p.prod_in.name,
+            "output" => dict(
+                r.name => dict(
+                    "output matrix" => [
+                        data.alpha_mix[p, r, c_in, c_out]
+                        for c_in in p.prod_in.comp, c_out in r.comp
+                    ],
+                    "disposal limit (tonne)" => [
+                        data.m_plant_disp[p, r, t]
+                        for t in data.T
+                    ],
+                    "disposal cost (\$/tonne)" => [
+                        data.c_plant_disp[p, r, t]
+                        for t in data.T
+                    ],
+                )
+                for r in p.prod_out
+            ),
+            "fixed operating cost (\$)" => [
+                data.c_fix[p, t]
+                for t in data.T
+            ],
+            "variable operating cost (\$/tonne)" => [
+                data.c_var[p, t]
+                for t in data.T
+            ],
+            "opening cost (\$)" => [
+                data.c_open[p, t]
+                for t in data.T
+            ],
+            "capacity (tonne)" => data.m_cap[p],
+            "emissions (tonne/tonne)" => dict(
+                s => [
+                    data.alpha_plant_emission[p, s, t]
+                    for t in data.T
+                ]
+                for s in data.emissions
+            )
+        )
+        for p in data.plants
+    )
+    json["emissions"] = dict(
+        s.name => dict(
+            "penalty (\$/tonne)" => [
+                data.c_emission[s, t]
+                for t in data.T
+            ],
+            "limit (tonne)" => [
+                data.m_emission[s, t]
+                for t in data.T
+            ]
+        )
+        for s in data.emissions
+    )
+    open(filename, "w") do io
+        JSON.print(io, json, 2)
+    end
+end
+
+# Read
+# ==============================================================================
+function read_json(filename)
+    json = JSON.parsefile(filename)
+    T = 1:json["parameters"]["time horizon (years)"]
+    centers = []
+    components_by_name = dict()
+    emissions = []
+    emissions_by_name = dict()
+    plants = []
+    products = []
+    products_by_name = dict()
+    alpha_mix = dict()
+    alpha_plant_emission = dict()
+    alpha_tr_emission = dict()
+    c_acq = dict()
+    c_center_disp = dict()
+    c_emission = dict()
+    c_fix = dict()
+    c_open = dict()
+    c_plant_disp = dict()
+    c_store = dict()
+    c_tr = dict()
+    c_var = dict()
+    m_cap = dict()
+    m_center_disp = dict()
+    m_emission = dict()
+    m_init = dict()
+    m_plant_disp = dict()
+    m_store = dict()
+
+    for (emission_name, emission_data) in json["emissions"]
+        s = Emission(emission_name)
+        emissions_by_name[emission_name] = s
+        push!(emissions, s)
+        for t in T
+            c_emission[s, t] = emission_data["penalty (\$/tonne)"][t]
+            m_emission[s, t] = emission_data["limit (tonne)"][t]
+        end
+    end
+
+    for (name, prod_data) in json["products"]
+        comp = []
+        for (comp_name) in prod_data["components"]
+            if comp_name ∉ keys(components_by_name)
+                components_by_name[comp_name] = Component(comp_name)
+            end
+            push!(comp, components_by_name[comp_name])
+        end
+        r = Product(name, comp)
+        products_by_name[name] = r
+        push!(products, r)
+        for t in T
+            m_center_disp[r, t] = prod_data["disposal limit (tonne)"][t]
+            c_tr[r, t] = prod_data["transportation cost (\$/km/tonne)"][t]
+        end
+        for (s_name, s_data) in prod_data["transportation emissions (tonne/km/tonne)"]
+            s = emissions_by_name[s_name]
+            for t in T
+                alpha_tr_emission[r, s, t] = s_data[t]
+            end
+        end
+    end
+
+    for (name, center_data) in json["centers"]
+        latitude = center_data["latitude"]
+        longitude = center_data["longitude"]
+        prod_out = [products_by_name[r] for r in keys(center_data["output"])]
+        q = Center(; name, latitude, longitude, prod_out)
+        push!(centers, q)
+        for r in prod_out, t in T
+            c_acq[q, r, t] = center_data["output"][r.name]["acquisition cost (\$/tonne)"][t]
+            c_center_disp[q, r, t] = center_data["output"][r.name]["disposal cost (\$/tonne)"][t]
+            c_store[q, r, t] = center_data["output"][r.name]["storage cost (\$/tonne)"][t]
+            m_store[q, r, t] = center_data["output"][r.name]["storage limit (tonne)"][t]
+            for (c_idx, c) in enumerate(r.comp)
+                m_init[q, r, c, t] = center_data["output"][r.name]["initial amount (tonne)"][t][c_idx]
+            end
+        end
+    end
+
+    for (plant_name, plant_data) in json["plants"]
+        latitude = plant_data["latitude"]
+        longitude = plant_data["longitude"]
+        prod_in = products_by_name[plant_data["input"]]
+        prod_out = [products_by_name[r] for r in keys(plant_data["output"])]
+        p = Plant(plant_name, latitude, longitude, prod_out, prod_in)
+        push!(plants, p)
+        m_cap[p] = plant_data["capacity (tonne)"]
+        for t in T
+            c_fix[p, t] = plant_data["fixed operating cost (\$)"][t]
+            c_var[p, t] = plant_data["variable operating cost (\$/tonne)"][t]
+            c_open[p, t] = plant_data["opening cost (\$)"][t]
+        end
+        for r in prod_out,
+            (cin_idx, c_in) in enumerate(prod_in.comp),
+            (cout_idx, c_out) in enumerate(r.comp)
+
+            alpha_mix[p, r, c_in, c_out] =
+                plant_data["output"][r.name]["output matrix"][cout_idx][cin_idx]
+        end
+        for r in prod_out, t in T
+            c_plant_disp[p, r, t] = plant_data["output"][r.name]["disposal cost (\$/tonne)"][t]
+            m_plant_disp[p, r, t] = plant_data["output"][r.name]["disposal limit (tonne)"][t]
+        end
+        for (s_name, s_data) in plant_data["emissions (tonne/tonne)"]
+            s = emissions_by_name[s_name]
+            for t in T
+                alpha_plant_emission[p, s, t] = s_data[t]
+            end
+        end
+    end
+
+    metric = KnnDrivingDistance()
+    m_dist = dict(
+        (p, q) => _calculate_distance(
+            p.latitude,
+            p.longitude,
+            q.latitude,
+            q.longitude,
+            metric,
+        )
+        for p in [plants; centers], q in plants
+    )
+
+    return Instance(;
+        T,
+        centers,
+        plants,
+        products,
+        emissions,
+        alpha_mix,
+        alpha_plant_emission,
+        alpha_tr_emission,
+        c_acq,
+        c_center_disp,
+        c_emission,
+        c_fix,
+        c_open,
+        c_plant_disp,
+        c_store,
+        c_tr,
+        c_var,
+        m_cap,
+        m_center_disp,
+        m_dist,
+        m_emission,
+        m_init,
+        m_plant_disp,
+        m_store
+    )
+end
+
+
+# Run
+# ==============================================================================
+
+function generate_json()
+    @info "Generating data"
+    data = generate_data()
+
+    @info "Writing JSON file"
+    write_json(data, "output-3/case.json")
+end
+
+function solve()
+    @info "Reading JSON file"
+    data = read_json("output-3/case.json")
+
+    T = data.T
+    centers = data.centers
+    plants = data.plants
+    products = data.products
+    emissions = data.emissions
+
+    model = Model(Gurobi.Optimizer)
+
+    # Graph
+    # -------------------------------------------------------------------------
+    E = []
+    E_in = dict(src => [] for src in plants)
+    E_out = dict(src => [] for src in plants ∪ centers)
+    function push_edge!(src, dst, r)
+        push!(E, (src, dst, r))
+        push!(E_out[src], (dst, r))
+        push!(E_in[dst], (src, r))
+    end
+    for r in products
+        # Plant to plant
+        for p1 in plants
+            r ∈ p1.prod_out || continue
+            for p2 in plants
+                p1 != p2 || continue
+                r == p2.prod_in || continue
+                push_edge!(p1, p2, r)
+            end
+        end
+        # Center to plant
+        for q in centers
+            r ∈ q.prod_out || continue
+            for p in plants
+                r == p.prod_in || continue
+                push_edge!(q, p, r)
+            end
+        end
+    end
+
+    # Decision variables
+    # -------------------------------------------------------------------------
+    y = _init(model, :y)
+    for (q, p, r) in E, c in r.comp, t in T
+        y[q, p, r, c, t] = @variable(model, lower_bound = 0)
+    end
+
+    y_total = _init(model, :y_total)
+    for (q, p, r) in E, t in T
+        y_total[q, p, r, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_center_disp = _init(model, :z_center_disp)
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        z_center_disp[q, r, c, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_center_disp_total = _init(model, :z_center_disp_total)
+    for q in centers, r in q.prod_out, t in T
+        z_center_disp_total[q, r, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_store = _init(model, :z_store)
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        if t == T.stop
+            z_store[q, r, c, t] = 0.0
+        else
+            z_store[q, r, c, t] = @variable(model, lower_bound = 0)
+        end
+    end
+
+    z_store_total = _init(model, :z_store_total)
+    for q in centers, r in q.prod_out
+        z_store_total[q, r, 0] = 0.0
+        for t in T
+            if t == T.stop
+                z_store_total[q, r, t] = 0.0
+            else
+                z_store_total[q, r, t] = @variable(model, lower_bound = 0)
+            end
+        end
+    end
+
+    x_open = _init(model, :x_open)
+    for p in plants
+        x_open[p, 0] = 0
+        for t in T
+            x_open[p, t] = @variable(model, binary = true)
+        end
+    end
+
+    x_send = _init(model, :x_send)
+    for p in plants, (q, r) in E_out[p], t in T
+        x_send[p, q, r, t] = @variable(model, binary = true)
+    end
+
+    x_disp = _init(model, :x_disp)
+    for p in plants, r in p.prod_out, t in T
+        x_disp[p, r, t] = @variable(model, binary = true)
+    end
+
+    z_prod = _init(model, :z_prod)
+    for p in plants, r in p.prod_out, c in r.comp, t in T
+        z_prod[p, r, c, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_plant_disp = _init(model, :z_plant_disp)
+    for p in plants, r in p.prod_out, c in r.comp, t in T
+        z_plant_disp[p, r, c, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_tr_emissions = _init(model, :z_tr_emissions)
+    for (q, p, r) in E, s in emissions, t in T
+        z_tr_emissions[q, p, r, s, t] = @variable(model, lower_bound = 0)
+    end
+
+    z_plant_emissions = _init(model, :z_plant_emissions)
+    for p in plants, s in emissions, t in T
+        z_plant_emissions[p, s, t] = @variable(model, lower_bound = 0)
+    end
+
+    # Objective function
+    # -------------------------------------------------------------------------
+    obj = AffExpr()
+
+    # Center disposal
+    for q in centers, r in q.prod_out, t in T
+        add_to_expression!(
+            obj,
+            data.c_center_disp[q, r, t],
+            z_center_disp_total[q, r, t]
+        )
+    end
+
+    # Center acquisition
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        add_to_expression!(
+            obj,
+            data.m_init[q, r, c, t] * data.c_acq[q, r, t]
+        )
+    end
+
+    # Center storage
+    for q in centers, r in q.prod_out, t in T
+        add_to_expression!(
+            obj,
+            data.c_store[q, r, t],
+            z_store_total[q, r, t],
+        )
+    end
+
+    # Transportation
+    for (q, p, r) in E, c in r.comp, t in T
+        add_to_expression!(
+            obj,
+            data.m_dist[q, p] * data.c_tr[r, t],
+            y[q, p, r, c, t]
+        )
+    end
+
+    # Transportation emissions
+    for (q, p, r) in E, s in emissions, t in T
+        add_to_expression!(
+            obj,
+            data.c_emission[s, t],
+            z_tr_emissions[q, p, r, s, t]
+        )
+    end
+
+    # Fixed cost
+    for p in plants, t in T
+        add_to_expression!(
+            obj,
+            data.c_fix[p, t],
+            x_open[p, t]
+        )
+    end
+
+    # Opening cost
+    for p in plants, t in T
+        add_to_expression!(
+            obj,
+            data.c_open[p, t],
+            x_open[p, t] - x_open[p, t-1]
+        )
+    end
+
+    # Variable operating cost
+    for (q, p, r) in E, c in r.comp, t in T
+        add_to_expression!(
+            obj,
+            data.c_var[p, t],
+            y[q, p, r, c, t]
+        )
+    end
+
+    # Plant disposal
+    for p in plants, r in p.prod_out, c in r.comp, t in T
+        add_to_expression!(
+            obj,
+            data.c_plant_disp[p, r, t],
+            z_plant_disp[p, r, c, t]
+        )
+    end
+
+    # Plant emissions
+    for p in plants, s in emissions, t in T
+        add_to_expression!(
+            obj,
+            data.c_emission[s, t],
+            z_plant_emissions[p, s, t]
+        )
+    end
+
+    @objective(model, Min, obj)
+
+    # Constraints
+    # -------------------------------------------------------------------------
+    eq_balance = _init(model, :eq_balance)
+    for q in centers, r in q.prod_out, t in T
+        eq_balance[q, r, t] = @constraint(
+            model,
+            sum(
+                y_total[q, p, r2, t]
+                for (p, r2) in E_out[q] if r == r2
+            ) + z_store_total[q, r, t] ==
+            sum(data.m_init[q, r, c, t] for c in r.comp) +
+            z_store_total[q, r, t-1]
+        )
+    end
+
+    ratio(q, r, c, t) = (
+        data.m_init[q, r, c, t] /
+        sum(data.m_init[q, r, d, t] for d in r.comp)
+    )
+
+    eq_y_total = _init(model, :eq_y_total)
+    for (q, p, r) in E, t in T
+        eq_y_total[q, p, r, t] = @constraint(
+            model,
+            y_total[q, p, r, t] == sum(y[q, p, r, c, t] for c in r.comp)
+        )
+    end
+
+    eq_split_y = _init(model, :eq_split_y)
+    for (q, p, r) in E, c in r.comp, t in T
+        if q ∉ centers
+            continue
+        end
+        eq_split_y[q, p, r, c, t] = @constraint(
+            model,
+            y[q, p, r, c, t] == ratio(q, r, c, t) * y_total[q, p, r, t]
+        )
+    end
+
+    eq_split_center_disp = _init(model, :eq_split_center_disp)
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        eq_split_center_disp[q, r, c, t] = @constraint(
+            model,
+            z_center_disp[q, r, c, t] == ratio(q, r, c, t) * z_center_disp_total[q, r, t]
+        )
+    end
+
+    eq_split_store = _init(model, :eq_split_store)
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        eq_split_store[q, r, c, t] = @constraint(
+            model,
+            z_store[q, r, c, t] == ratio(q, r, c, t) * z_store_total[q, r, t]
+        )
+    end
+
+    eq_center_disposal = _init(model, :eq_center_disposal)
+    for r in products, t in T
+        centers_r = [q for q in centers if r ∈ q.prod_out]
+        if isempty(centers_r)
+            continue
+        end
+        eq_center_disposal[r, t] = @constraint(
+            model,
+            sum(z_center_disp_total[q, r, t] for q in centers_r) <= data.m_center_disp[r, t]
+        )
+    end
+
+    eq_center_storage = _init(model, :eq_center_storage)
+    for q in centers, r in q.prod_out, t in T
+        eq_center_storage[q, r, t] = @constraint(
+            model,
+            z_store_total[q, r, t] <= data.m_store[q, r, t]
+        )
+    end
+
+    eq_tr_emissions = _init(model, :eq_tr_emissions)
+    for (q, p, r) in E, s in emissions, t in T
+        eq_tr_emissions[q, p, r, s, t] = @constraint(
+            model,
+            z_tr_emissions[q, p, r, s, t] ==
+            data.m_dist[q, p] * data.alpha_tr_emission[r, s, t] * y_total[q, p, r, t]
+        )
+    end
+
+    eq_plant_capacity = _init(model, :eq_plant_capacity)
+    for p in plants, t in T
+        eq_plant_capacity[p, t] = @constraint(
+            model,
+            sum(y_total[q, p, r, t] for (q, r) in E_in[p]) <=
+            data.m_cap[p] * x_open[p, t]
+        )
+    end
+
+    eq_plant_prod = _init(model, :eq_plant_prod)
+    for p in plants, r_out in p.prod_out, c_out in r_out.comp, t in T
+        eq_plant_prod[p, r_out, c_out, t] = @constraint(
+            model,
+            z_prod[p, r_out, c_out, t] ==
+            sum(
+                data.alpha_mix[p, r_out, c_in, c_out] * y[q, p, r_in, c_in, t]
+                for (q, r_in) in E_in[p]
+                for c_in in r_in.comp
+            )
+        )
+    end
+
+    eq_plant_disp = _init(model, :eq_plant_disp)
+    for p in plants, r in p.prod_out, t in T
+        eq_plant_disp[p, r, t] = @constraint(
+            model,
+            sum(
+                z_plant_disp[p, r, c, t]
+                for c in r.comp
+            ) <= data.m_plant_disp[p, r, t] * x_disp[p, r, t]
+        )
+    end
+
+    eq_plant_emissions = _init(model, :eq_plant_emissions)
+    for p in plants, s in emissions, t in T
+        eq_plant_emissions[p, s, t] = @constraint(
+            model,
+            z_plant_emissions[p, s, t] ==
+            sum(
+                y_total[q, p, r, t]
+                for (q, r,) in E_in[p]
+            ) * data.alpha_plant_emission[p, s, t]
+        )
+    end
+
+    eq_emissions_limit = _init(model, :eq_emissions_limit)
+    for s in emissions, t in T
+        eq_emissions_limit[s, t] = @constraint(
+            model,
+            sum(z_plant_emissions[p, s, t] for p in plants)
+            +
+            sum(
+                z_tr_emissions[q, p, r, s, t]
+                for (q, p, r) in E
+            ) <= data.m_emission[s, t]
+        )
+    end
+
+    eq_plant_remains_open = _init(model, :eq_plant_remains_open)
+    for p in plants, t in T
+        eq_plant_remains_open[p, t] = @constraint(
+            model,
+            x_open[p, t] >= x_open[p, t-1]
+        )
+    end
+
+    eq_plant_single_dest = _init(model, :eq_plant_single_dest)
+    for p in plants, r in p.prod_out, t in T
+        eq_plant_single_dest[p, r, t] = @constraint(
+            model,
+            sum(
+                x_send[p, q, r, t]
+                for (q, r2) in E_out[p]
+                if r == r2
+            ) + x_disp[p, r, t] <= 1
+        )
+    end
+
+    eq_plant_send_limit = _init(model, :eq_plant_send_limit)
+    for p in plants, (q, r) in E_out[p], t in T
+        eq_plant_send_limit[p, q, r, t] = @constraint(
+            model,
+            y_total[p, q, r, t] <= data.m_cap[q] * x_send[p, q, r, t]
+        )
+    end
+
+    eq_plant_balance = _init(model, :eq_plant_balance)
+    for p in plants, r in p.prod_out, c in r.comp, t in T
+        eq_plant_balance[p, r, c, t] = @constraint(
+            model,
+            z_prod[p, r, c, t] ==
+            z_plant_disp[p, r, c, t] +
+            sum(
+                y[p, q, r, c, t]
+                for (q, r2) in E_out[p]
+                if r == r2
+            )
+        )
+    end
+
+    # Optimize
+    # -------------------------------------------------------------------------
+    _set_names!(model)
+    optimize!(model)
+
+    # Report: Transportation
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."source" = String[]
+    df."destination" = String[]
+    df."product" = String[]
+    df."component" = String[]
+    df."time" = Int[]
+    df."distance (km)" = Float64[]
+    df."amount sent (tonne)" = Float64[]
+    df."transportation cost (\$)" = Float64[]
+    df."variable operating cost (\$)" = Float64[]
+    for (q, p, r) in E, c in r.comp, t in T
+        if value(y[q, p, r, c, t]) ≈ 0
+            continue
+        end
+        push!(
+            df,
+            [
+                q.name,
+                p.name,
+                r.name,
+                c.name,
+                t,
+                data.m_dist[q, p],
+                value(y[q, p, r, c, t]),
+                data.m_dist[q, p] * data.c_tr[r, t] * value(y[q, p, r, c, t]),
+                data.c_var[p, t] * value(y[q, p, r, c, t])
+            ]
+        )
+    end
+    CSV.write("output-3/transp.csv", df)
+
+    # Report: Centers
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."center" = String[]
+    df."product" = String[]
+    df."component" = String[]
+    df."time" = Int[]
+    df."amount available (tonne)" = Float64[]
+    df."amount sent (tonne)" = Float64[]
+    df."amount stored (tonne)" = Float64[]
+    df."amount disposed (tonne)" = Float64[]
+    df."acquisition cost (\$)" = Float64[]
+    df."storage cost (\$)" = Float64[]
+    df."disposal cost (\$)" = Float64[]
+    for q in centers, r in q.prod_out, c in r.comp, t in T
+        push!(
+            df,
+            [
+                q.name,
+                r.name,
+                c.name,
+                t,
+                data.m_init[q, r, c, t],
+                sum(
+                    value(y[q, p, r, c, t])
+                    for (p, r2) in E_out[q]
+                    if r == r2
+                ),
+                value(z_store[q, r, c, t]),
+                value(z_center_disp[q, r, c, t]),
+                data.m_init[q, r, c, t] * data.c_acq[q, r, t],
+                data.c_store[q, r, t] * value(z_store[q, r, c, t]),
+                data.c_center_disp[q, r, t] * value(z_center_disp[q, r, c, t]),
+            ]
+        )
+    end
+    CSV.write("output-3/centers.csv", df)
+
+    # Report: Plants
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."plant" = String[]
+    df."time" = Int[]
+    df."is open?" = Float64[]
+    df."opening cost (\$)" = Float64[]
+    df."fixed operating cost (\$)" = Float64[]
+    for p in plants, t in T
+        push!(
+            df,
+            [
+                p.name,
+                t,
+                value(x_open[p, t]),
+                data.c_open[p, t] * (value(x_open[p, t]) - value(x_open[p, t-1])),
+                data.c_fix[p, t] * value(x_open[p, t]),
+            ]
+        )
+    end
+    CSV.write("output-3/plants.csv", df)
+
+    # Report: Plant Outputs
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."plant" = String[]
+    df."product" = String[]
+    df."component" = String[]
+    df."time" = Int[]
+    df."amount produced (tonne)" = Float64[]
+    df."amount disposed (tonne)" = Float64[]
+    df."amount sent (tonne)" = Float64[]
+    df."disposal cost (\$)" = Float64[]
+    for p in plants, r in p.prod_out, c in r.comp, t in T
+        if value(z_prod[p, r, c, t]) ≈ 0
+            continue
+        end
+        push!(
+            df,
+            [
+                p.name,
+                r.name,
+                c.name,
+                t,
+                value(z_prod[p, r, c, t]),
+                value(z_plant_disp[p, r, c, t]),
+                sum(
+                    value(y[p, q, r, c, t])
+                    for (q, r2) in E_out[p]
+                    if r == r2;
+                    init=0.0
+                ),
+                data.c_plant_disp[p, r, t] * value(z_plant_disp[p, r, c, t]),
+            ]
+        )
+    end
+    CSV.write("output-3/plant-outputs.csv", df)
+
+    # Report: Plant Emissions
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."plant" = String[]
+    df."emission" = String[]
+    df."time" = Int[]
+    df."amount emitted (tonne)" = Float64[]
+    df."emission cost (\$)" = Float64[]
+    for p in plants, s in emissions, t in T
+        push!(
+            df,
+            [
+                p.name,
+                s.name,
+                t,
+                value(z_plant_emissions[p, s, t]),
+                data.c_emission[s, t] * value(z_plant_emissions[p, s, t]),
+            ]
+        )
+    end
+    CSV.write("output-3/plant-emissions.csv", df)
+
+    # Report: Transportation Emissions
+    # -------------------------------------------------------------------------
+    df = DataFrame()
+    df."source" = String[]
+    df."destination" = String[]
+    df."product" = String[]
+    df."emission" = String[]
+    df."time" = Int[]
+    df."distance (km)" = Float64[]
+    df."amount sent (tonne)" = Float64[]
+    df."amount emitted (tonne)" = Float64[]
+    df."emission cost (\$)" = Float64[]
+    for (q, p, r) in E, s in emissions, t in T
+        if value(y_total[q, p, r, t]) ≈ 0
+            continue
+        end
+        push!(
+            df,
+            [
+                q.name,
+                p.name,
+                r.name,
+                s.name,
+                t,
+                data.m_dist[q, p],
+                value(y_total[q, p, r, t]),
+                value(z_tr_emissions[q, p, r, s, t]),
+                data.c_emission[s, t] * value(z_tr_emissions[q, p, r, s, t]),
+            ]
+        )
+    end
+    CSV.write("output-3/transp-emissions.csv", df)
+end
+
diff --git a/output-2/plant-outputs.csv b/output-2/plant-outputs.csv
new file mode 100644
index 0000000..39e3bea
--- /dev/null
+++ b/output-2/plant-outputs.csv
@@ -0,0 +1,11 @@
+plant,product,component,amount produced (tonne),revenue ($)
+Phoenix,film bale,film,2896.88,28968.800000000003
+Phoenix,film bale,paper,40.800000000000004,408.00000000000006
+Phoenix,film bale,cardboard,53.88,538.8000000000001
+Phoenix,cardboard bale,paper,40.800000000000004,530.4000000000001
+Phoenix,cardboard bale,cardboard,2020.5,26266.5
+Dallas,film bale,film,2144.24,25730.879999999997
+Dallas,film bale,paper,37.08,444.96
+Dallas,film bale,cardboard,31.86,382.32
+Dallas,cardboard bale,paper,37.08,667.4399999999999
+Dallas,cardboard bale,cardboard,1194.75,21505.5
diff --git a/output-2/plant.csv b/output-2/plant.csv
new file mode 100644
index 0000000..9bbcd1c
--- /dev/null
+++ b/output-2/plant.csv
@@ -0,0 +1,3 @@
+plant,is open?,capacity (tonne),utilization (tonne),opening cost ($)
+Phoenix,1.0,41670.0,7690.0,6405.0
+Dallas,1.0,12957.0,5635.0,6824.0
diff --git a/output-2/tr.csv b/output-2/tr.csv
new file mode 100644
index 0000000..d3a9cd0
--- /dev/null
+++ b/output-2/tr.csv
@@ -0,0 +1,31 @@
+center,plant,component,amount sent (tonne),transportation cost ($),variable operating cost ($)
+Chicago,Dallas,film,630.0,1890.0,5670.0
+Chicago,Dallas,paper,662.0000000000001,1986.0000000000005,5958.000000000001
+Chicago,Dallas,cardboard,81.0,243.0,729.0
+New York City,Phoenix,film,451.00000000000006,2255.0000000000005,4059.0000000000005
+New York City,Phoenix,paper,640.0000000000001,3200.0000000000005,5760.000000000001
+New York City,Phoenix,cardboard,516.0000000000001,2580.0000000000005,4644.000000000001
+Los Angeles,Dallas,film,478.0,2868.0,4302.0
+Los Angeles,Dallas,paper,342.99999999999994,2057.9999999999995,3086.9999999999995
+Los Angeles,Dallas,cardboard,197.0,1182.0,1773.0
+Houston,Phoenix,film,703.9999999999998,1407.9999999999995,6335.999999999998
+Houston,Phoenix,paper,268.0,536.0,2412.0
+Houston,Phoenix,cardboard,600.9999999999999,1201.9999999999998,5408.999999999999
+Phoenix,Phoenix,film,674.0,2022.0,6066.0
+Phoenix,Phoenix,paper,515.9999999999999,1547.9999999999995,4643.999999999999
+Phoenix,Phoenix,cardboard,398.0,1194.0,3582.0
+Philadelphia,Dallas,film,165.99999999999997,331.99999999999994,1493.9999999999998
+Philadelphia,Dallas,paper,91.0,182.0,819.0
+Philadelphia,Dallas,cardboard,315.99999999999994,631.9999999999999,2843.9999999999995
+San Antonio,Dallas,film,614.0000000000001,614.0000000000001,5526.000000000001
+San Antonio,Dallas,paper,273.0,273.0,2457.0
+San Antonio,Dallas,cardboard,435.00000000000006,435.00000000000006,3915.0000000000005
+San Diego,Phoenix,film,669.0000000000001,5352.000000000001,6021.000000000001
+San Diego,Phoenix,paper,192.0,1536.0,1728.0
+San Diego,Phoenix,cardboard,859.0000000000001,6872.000000000001,7731.000000000001
+Dallas,Phoenix,film,458.00000000000006,3664.0000000000005,4122.000000000001
+Dallas,Phoenix,paper,424.0000000000001,3392.000000000001,3816.000000000001
+Dallas,Phoenix,cardboard,320.0,2560.0,2880.0
+San Jose,Dallas,film,300.0,900.0,2700.0
+San Jose,Dallas,paper,484.99999999999994,1454.9999999999998,4364.999999999999
+San Jose,Dallas,cardboard,563.9999999999999,1691.9999999999995,5075.999999999999
diff --git a/output-3/case.json b/output-3/case.json
new file mode 100644
index 0000000..ccf74ae
--- /dev/null
+++ b/output-3/case.json
@@ -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
+      ]
+    }
+  }
+}
diff --git a/output-3/centers.csv b/output-3/centers.csv
new file mode 100644
index 0000000..a6dc7b6
--- /dev/null
+++ b/output-3/centers.csv
@@ -0,0 +1,61 @@
+center,product,component,time,amount available (tonne),amount sent (tonne),amount stored (tonne),amount disposed (tonne),acquisition cost ($),storage cost ($),disposal cost ($)
+Collection (San Antonio),Waste,Film,1,288.0,288.0,0.0,0.0,288.0,0.0,0.0
+Collection (San Antonio),Waste,Film,2,4240.0,4240.0,0.0,0.0,4240.0,0.0,0.0
+Collection (San Antonio),Waste,Paper,1,252.0,252.00000000000003,0.0,0.0,252.0,0.0,0.0
+Collection (San Antonio),Waste,Paper,2,3710.0,3710.0000000000005,0.0,0.0,3710.0,0.0,0.0
+Collection (San Antonio),Waste,Cardboard,1,324.0,324.0,0.0,0.0,324.0,0.0,0.0
+Collection (San Antonio),Waste,Cardboard,2,4770.0,4770.0,0.0,0.0,4770.0,0.0,0.0
+Collection (San Jose),Waste,Film,1,1255.0,1255.0,0.0,0.0,1255.0,0.0,0.0
+Collection (San Jose),Waste,Film,2,4650.0,4650.0,0.0,0.0,4650.0,0.0,0.0
+Collection (San Jose),Waste,Paper,1,2510.0,2510.0,0.0,0.0,2510.0,0.0,0.0
+Collection (San Jose),Waste,Paper,2,9300.0,9300.0,0.0,0.0,9300.0,0.0,0.0
+Collection (San Jose),Waste,Cardboard,1,251.0,251.0,0.0,0.0,251.0,0.0,0.0
+Collection (San Jose),Waste,Cardboard,2,930.0,930.0,0.0,0.0,930.0,0.0,0.0
+Collection (New York City),Waste,Film,1,990.0,990.0,0.0,0.0,990.0,0.0,0.0
+Collection (New York City),Waste,Film,2,6450.0,6450.0,0.0,0.0,6450.0,0.0,0.0
+Collection (New York City),Waste,Paper,1,891.0,891.0,0.0,0.0,891.0,0.0,0.0
+Collection (New York City),Waste,Paper,2,5805.0,5805.0,0.0,0.0,5805.0,0.0,0.0
+Collection (New York City),Waste,Cardboard,1,297.0,297.0,0.0,0.0,297.0,0.0,0.0
+Collection (New York City),Waste,Cardboard,2,1935.0,1934.9999999999998,0.0,0.0,1935.0,0.0,0.0
+Collection (Los Angeles),Waste,Film,1,4160.0,4160.0,0.0,0.0,4160.0,0.0,0.0
+Collection (Los Angeles),Waste,Film,2,5704.0,5704.0,0.0,0.0,5704.0,0.0,0.0
+Collection (Los Angeles),Waste,Paper,1,3640.0,3640.0000000000005,0.0,0.0,3640.0,0.0,0.0
+Collection (Los Angeles),Waste,Paper,2,4991.0,4991.000000000001,0.0,0.0,4991.0,0.0,0.0
+Collection (Los Angeles),Waste,Cardboard,1,5200.0,5200.0,0.0,0.0,5200.0,0.0,0.0
+Collection (Los Angeles),Waste,Cardboard,2,7130.0,7130.0,0.0,0.0,7130.0,0.0,0.0
+Collection (Chicago),Waste,Film,1,716.0,716.0,0.0,0.0,716.0,0.0,0.0
+Collection (Chicago),Waste,Film,2,1394.0,1394.0,0.0,0.0,1394.0,0.0,0.0
+Collection (Chicago),Waste,Paper,1,2864.0,2864.0,0.0,0.0,2864.0,0.0,0.0
+Collection (Chicago),Waste,Paper,2,5576.0,5576.0,0.0,0.0,5576.0,0.0,0.0
+Collection (Chicago),Waste,Cardboard,1,1074.0,1074.0,0.0,0.0,1074.0,0.0,0.0
+Collection (Chicago),Waste,Cardboard,2,2091.0,2091.0,0.0,0.0,2091.0,0.0,0.0
+Collection (Dallas),Waste,Film,1,5096.0,5096.0,0.0,0.0,5096.0,0.0,0.0
+Collection (Dallas),Waste,Film,2,4354.0,4354.0,0.0,0.0,4354.0,0.0,0.0
+Collection (Dallas),Waste,Paper,1,728.0,728.0,0.0,0.0,728.0,0.0,0.0
+Collection (Dallas),Waste,Paper,2,622.0,622.0,0.0,0.0,622.0,0.0,0.0
+Collection (Dallas),Waste,Cardboard,1,5824.0,5824.0,0.0,0.0,5824.0,0.0,0.0
+Collection (Dallas),Waste,Cardboard,2,4976.0,4976.0,0.0,0.0,4976.0,0.0,0.0
+Collection (Phoenix),Waste,Film,1,3635.0,3635.0,0.0,0.0,3635.0,0.0,0.0
+Collection (Phoenix),Waste,Film,2,2275.0,2275.0,0.0,0.0,2275.0,0.0,0.0
+Collection (Phoenix),Waste,Paper,1,6543.0,6543.0,0.0,0.0,6543.0,0.0,0.0
+Collection (Phoenix),Waste,Paper,2,4095.0,4095.0,0.0,0.0,4095.0,0.0,0.0
+Collection (Phoenix),Waste,Cardboard,1,5089.0,5089.0,0.0,0.0,5089.0,0.0,0.0
+Collection (Phoenix),Waste,Cardboard,2,3185.0,3185.0,0.0,0.0,3185.0,0.0,0.0
+Collection (San Diego),Waste,Film,1,6690.0,6690.0,0.0,0.0,6690.0,0.0,0.0
+Collection (San Diego),Waste,Film,2,8460.0,8460.0,0.0,0.0,8460.0,0.0,0.0
+Collection (San Diego),Waste,Paper,1,1338.0,1338.0,0.0,0.0,1338.0,0.0,0.0
+Collection (San Diego),Waste,Paper,2,1692.0,1692.0,0.0,0.0,1692.0,0.0,0.0
+Collection (San Diego),Waste,Cardboard,1,4014.0,4014.0,0.0,0.0,4014.0,0.0,0.0
+Collection (San Diego),Waste,Cardboard,2,5076.0,5076.0,0.0,0.0,5076.0,0.0,0.0
+Collection (Philadelphia),Waste,Film,1,220.0,220.0,0.0,0.0,220.0,0.0,0.0
+Collection (Philadelphia),Waste,Film,2,962.0,962.0,0.0,0.0,962.0,0.0,0.0
+Collection (Philadelphia),Waste,Paper,1,880.0,880.0,0.0,0.0,880.0,0.0,0.0
+Collection (Philadelphia),Waste,Paper,2,3848.0,3848.0,0.0,0.0,3848.0,0.0,0.0
+Collection (Philadelphia),Waste,Cardboard,1,880.0,880.0,0.0,0.0,880.0,0.0,0.0
+Collection (Philadelphia),Waste,Cardboard,2,3848.0,3848.0,0.0,0.0,3848.0,0.0,0.0
+Collection (Houston),Waste,Film,1,2668.0,2668.0,0.0,0.0,2668.0,0.0,0.0
+Collection (Houston),Waste,Film,2,1852.0,1852.0,0.0,0.0,1852.0,0.0,0.0
+Collection (Houston),Waste,Paper,1,4669.0,4669.0,0.0,0.0,4669.0,0.0,0.0
+Collection (Houston),Waste,Paper,2,3241.0,3241.0,0.0,0.0,3241.0,0.0,0.0
+Collection (Houston),Waste,Cardboard,1,6670.0,6670.0,0.0,0.0,6670.0,0.0,0.0
+Collection (Houston),Waste,Cardboard,2,4630.0,4630.0,0.0,0.0,4630.0,0.0,0.0
diff --git a/output-3/plant-emissions.csv b/output-3/plant-emissions.csv
new file mode 100644
index 0000000..23e54f4
--- /dev/null
+++ b/output-3/plant-emissions.csv
@@ -0,0 +1,13 @@
+plant,emission,time,amount emitted (tonne),emission cost ($)
+MRF (Chicago),CO2,1,88.11999999999999,0.8811999999999999
+MRF (Chicago),CO2,2,319.09,3.1908999999999996
+Paper Mill (Phoenix),CO2,1,111.96120000000002,1.1196120000000003
+Paper Mill (Phoenix),CO2,2,121.9771,1.219771
+Paper Mill (Dallas),CO2,1,97.26479999999998,0.9726479999999998
+Paper Mill (Dallas),CO2,2,113.78059999999999,1.1378059999999999
+MRF (Phoenix),CO2,1,443.25000000000006,4.432500000000001
+MRF (Phoenix),CO2,2,500.0,5.0
+MRF (Dallas),CO2,1,265.19,2.6519
+MRF (Dallas),CO2,2,398.83,3.9882999999999997
+Paper Mill (Chicago),CO2,1,17.8095,0.178095
+Paper Mill (Chicago),CO2,2,62.10079999999999,0.6210079999999999
diff --git a/output-3/plant-outputs.csv b/output-3/plant-outputs.csv
new file mode 100644
index 0000000..770ce5e
--- /dev/null
+++ b/output-3/plant-outputs.csv
@@ -0,0 +1,37 @@
+plant,product,component,time,amount produced (tonne),amount disposed (tonne),amount sent (tonne),disposal cost ($)
+MRF (Chicago),Film bale,Film,1,1887.4799999999998,1887.4799999999998,0.0,-18874.8
+MRF (Chicago),Film bale,Film,2,8629.88,8629.88,0.0,-86298.79999999999
+MRF (Chicago),Film bale,Paper,1,92.70000000000002,92.70000000000002,0.0,-927.0000000000002
+MRF (Chicago),Film bale,Paper,2,304.58,304.58,0.0,-3045.7999999999997
+MRF (Chicago),Film bale,Cardboard,1,45.02,45.02,0.0,-450.20000000000005
+MRF (Chicago),Film bale,Cardboard,2,157.48000000000002,157.48000000000002,0.0,-1574.8000000000002
+MRF (Chicago),Cardboard bale,Paper,1,92.70000000000002,0.0,92.70000000000002,-0.0
+MRF (Chicago),Cardboard bale,Paper,2,304.579999999999,0.0,304.579999999999,-0.0
+MRF (Chicago),Cardboard bale,Cardboard,1,1688.25,0.0,1688.25,-0.0
+MRF (Chicago),Cardboard bale,Cardboard,2,5905.5,0.0,5905.5,-0.0
+Paper Mill (Phoenix),Cardboard sheets,Cardboard,1,10369.725,10369.725,0.0,-1.0369725e6
+Paper Mill (Phoenix),Cardboard sheets,Cardboard,2,11295.262499999999,11295.262499999999,0.0,-1.12952625e6
+Paper Mill (Dallas),Cardboard sheets,Cardboard,1,9132.824999999999,9132.824999999999,0.0,-913282.4999999999
+Paper Mill (Dallas),Cardboard sheets,Cardboard,2,10576.35,10576.35,0.0,-1.057635e6
+MRF (Phoenix),Film bale,Film,1,15425.2,15425.2,0.0,-154252.0
+MRF (Phoenix),Film bale,Film,2,18374.02,18374.02,0.0,-183740.2
+MRF (Phoenix),Film bale,Paper,1,280.62,280.62,0.0,-2806.2
+MRF (Phoenix),Film bale,Paper,2,307.96000000000004,307.96000000000004,0.0,-3079.6000000000004
+MRF (Phoenix),Film bale,Cardboard,1,291.08,291.08,0.0,-2910.7999999999997
+MRF (Phoenix),Film bale,Cardboard,2,317.06,317.06,0.0,-3170.6
+MRF (Phoenix),Cardboard bale,Paper,1,280.6200000000026,0.0,280.6200000000026,-0.0
+MRF (Phoenix),Cardboard bale,Paper,2,307.96000000000004,0.0,307.96000000000004,-0.0
+MRF (Phoenix),Cardboard bale,Cardboard,1,10915.5,0.0,10915.5,-0.0
+MRF (Phoenix),Cardboard bale,Cardboard,2,11889.75,0.0,11889.75,-0.0
+MRF (Dallas),Film bale,Film,1,7890.959999999999,7890.959999999999,0.0,-78909.59999999999
+MRF (Dallas),Film bale,Film,2,12530.279999999999,12530.279999999999,0.0,-125302.79999999999
+MRF (Dallas),Film bale,Paper,1,112.97999999999999,112.97999999999999,0.0,-1129.8
+MRF (Dallas),Film bale,Paper,2,245.06,245.06,0.0,-2450.6
+MRF (Dallas),Film bale,Cardboard,1,256.36,256.36,0.0,-2563.6000000000004
+MRF (Dallas),Film bale,Cardboard,2,296.88,296.88,0.0,-2968.8
+MRF (Dallas),Cardboard bale,Paper,1,112.97999999999774,0.0,112.97999999999774,-0.0
+MRF (Dallas),Cardboard bale,Paper,2,245.06,0.0,245.06,-0.0
+MRF (Dallas),Cardboard bale,Cardboard,1,9613.5,0.0,9613.5,-0.0
+MRF (Dallas),Cardboard bale,Cardboard,2,11133.0,0.0,11133.0,-0.0
+Paper Mill (Chicago),Cardboard sheets,Cardboard,1,1603.8374999999999,1603.8374999999999,0.0,-160383.75
+Paper Mill (Chicago),Cardboard sheets,Cardboard,2,5610.224999999999,5610.224999999999,0.0,-561022.5
diff --git a/output-3/plants.csv b/output-3/plants.csv
new file mode 100644
index 0000000..16b0a7c
--- /dev/null
+++ b/output-3/plants.csv
@@ -0,0 +1,13 @@
+plant,time,is open?,opening cost ($),fixed operating cost ($)
+MRF (Chicago),1,1.0,10000.0,1000.0
+MRF (Chicago),2,1.0,0.0,1000.0
+Paper Mill (Phoenix),1,1.0,10000.0,1000.0
+Paper Mill (Phoenix),2,1.0,0.0,1000.0
+Paper Mill (Dallas),1,1.0,10000.0,1000.0
+Paper Mill (Dallas),2,1.0,0.0,1000.0
+MRF (Phoenix),1,1.0,10000.0,1000.0
+MRF (Phoenix),2,1.0,0.0,1000.0
+MRF (Dallas),1,1.0,10000.0,1000.0
+MRF (Dallas),2,1.0,0.0,1000.0
+Paper Mill (Chicago),1,1.0,10000.0,1000.0
+Paper Mill (Chicago),2,1.0,0.0,1000.0
diff --git a/output-3/transp-emissions.csv b/output-3/transp-emissions.csv
new file mode 100644
index 0000000..9eb5ac7
--- /dev/null
+++ b/output-3/transp-emissions.csv
@@ -0,0 +1,28 @@
+source,destination,product,emission,time,distance (km),amount sent (tonne),amount emitted (tonne),emission cost ($)
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,CO2,1,0.0,1780.95,0.0,0.0
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,CO2,2,0.0,6210.079999999999,0.0,0.0
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,CO2,1,0.0,11196.120000000003,0.0,0.0
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,CO2,2,0.0,12197.71,0.0,0.0
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,CO2,1,0.0,9726.479999999998,0.0,0.0
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,CO2,2,0.0,11378.06,0.0,0.0
+Collection (San Antonio),MRF (Dallas),Waste,CO2,1,476.098,864.0,4113.48672,41.1348672
+Collection (San Antonio),MRF (Dallas),Waste,CO2,2,476.098,12720.0,60559.6656,605.596656
+Collection (San Jose),MRF (Phoenix),Waste,CO2,1,1173.985,4016.0,47147.23759999999,471.47237599999994
+Collection (San Jose),MRF (Phoenix),Waste,CO2,2,1173.985,7392.0,86780.97119999999,867.8097119999999
+Collection (San Jose),MRF (Dallas),Waste,CO2,2,2705.116,7488.0,202559.08608,2025.5908608000002
+Collection (New York City),MRF (Chicago),Waste,CO2,1,1293.755,2178.0,28177.983900000003,281.77983900000004
+Collection (New York City),MRF (Chicago),Waste,CO2,2,1293.755,14190.0,183583.83450000003,1835.8383450000003
+Collection (Los Angeles),MRF (Phoenix),Waste,CO2,1,668.923,13000.0,86959.99,869.5999
+Collection (Los Angeles),MRF (Phoenix),Waste,CO2,2,668.923,17825.0,119235.52475,1192.3552475
+Collection (Chicago),MRF (Chicago),Waste,CO2,1,0.0,4654.0,0.0,0.0
+Collection (Chicago),MRF (Chicago),Waste,CO2,2,0.0,9061.0,0.0,0.0
+Collection (Dallas),MRF (Dallas),Waste,CO2,1,0.0,11648.0,0.0,0.0
+Collection (Dallas),MRF (Dallas),Waste,CO2,2,0.0,9952.0,0.0,0.0
+Collection (Phoenix),MRF (Phoenix),Waste,CO2,1,0.0,15267.0,0.0,0.0
+Collection (Phoenix),MRF (Phoenix),Waste,CO2,2,0.0,9555.0,0.0,0.0
+Collection (San Diego),MRF (Phoenix),Waste,CO2,1,567.242,12042.0,68307.28164,683.0728164000001
+Collection (San Diego),MRF (Phoenix),Waste,CO2,2,567.242,15228.0,86379.61176,863.7961176
+Collection (Philadelphia),MRF (Chicago),Waste,CO2,1,1244.116,1980.0,24633.4968,246.334968
+Collection (Philadelphia),MRF (Chicago),Waste,CO2,2,1244.116,8658.0,107715.56328,1077.1556328000001
+Collection (Houston),MRF (Dallas),Waste,CO2,1,411.89,14007.0,57693.4323,576.9343230000001
+Collection (Houston),MRF (Dallas),Waste,CO2,2,411.89,9723.0,40048.0647,400.48064700000003
diff --git a/output-3/transp.csv b/output-3/transp.csv
new file mode 100644
index 0000000..2d18070
--- /dev/null
+++ b/output-3/transp.csv
@@ -0,0 +1,76 @@
+source,destination,product,component,time,distance (km),amount sent (tonne),transportation cost ($),variable operating cost ($)
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,Paper,1,0.0,92.70000000000002,0.0,92.70000000000002
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,Paper,2,0.0,304.579999999999,0.0,304.579999999999
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,Cardboard,1,0.0,1688.25,0.0,1688.25
+MRF (Chicago),Paper Mill (Chicago),Cardboard bale,Cardboard,2,0.0,5905.5,0.0,5905.5
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,Paper,1,0.0,280.6200000000026,0.0,280.6200000000026
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,Paper,2,0.0,307.96000000000004,0.0,307.96000000000004
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,Cardboard,1,0.0,10915.5,0.0,10915.5
+MRF (Phoenix),Paper Mill (Phoenix),Cardboard bale,Cardboard,2,0.0,11889.75,0.0,11889.75
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,Paper,1,0.0,112.97999999999774,0.0,112.97999999999774
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,Paper,2,0.0,245.06,0.0,245.06
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,Cardboard,1,0.0,9613.5,0.0,9613.5
+MRF (Dallas),Paper Mill (Dallas),Cardboard bale,Cardboard,2,0.0,11133.0,0.0,11133.0
+Collection (San Antonio),MRF (Dallas),Waste,Film,1,476.098,288.0,6855.811200000001,288.0
+Collection (San Antonio),MRF (Dallas),Waste,Film,2,476.098,4240.0,100932.77600000001,4240.0
+Collection (San Antonio),MRF (Dallas),Waste,Paper,1,476.098,252.00000000000003,5998.834800000001,252.00000000000003
+Collection (San Antonio),MRF (Dallas),Waste,Paper,2,476.098,3710.0000000000005,88316.17900000002,3710.0000000000005
+Collection (San Antonio),MRF (Dallas),Waste,Cardboard,1,476.098,324.0,7712.7876000000015,324.0
+Collection (San Antonio),MRF (Dallas),Waste,Cardboard,2,476.098,4770.0,113549.37300000002,4770.0
+Collection (San Jose),MRF (Phoenix),Waste,Film,1,1173.985,1255.0,73667.55875,1255.0
+Collection (San Jose),MRF (Phoenix),Waste,Film,2,1173.985,2310.0,135595.2675,2310.0
+Collection (San Jose),MRF (Phoenix),Waste,Paper,1,1173.985,2510.0,147335.1175,2510.0
+Collection (San Jose),MRF (Phoenix),Waste,Paper,2,1173.985,4620.0,271190.535,4620.0
+Collection (San Jose),MRF (Phoenix),Waste,Cardboard,1,1173.985,251.0,14733.51175,251.0
+Collection (San Jose),MRF (Phoenix),Waste,Cardboard,2,1173.985,462.0,27119.053499999998,462.0
+Collection (San Jose),MRF (Dallas),Waste,Film,2,2705.116,2340.0,316498.572,2340.0
+Collection (San Jose),MRF (Dallas),Waste,Paper,2,2705.116,4680.0,632997.144,4680.0
+Collection (San Jose),MRF (Dallas),Waste,Cardboard,2,2705.116,468.0,63299.7144,468.0
+Collection (New York City),MRF (Chicago),Waste,Film,1,1293.755,990.0,64040.872500000005,990.0
+Collection (New York City),MRF (Chicago),Waste,Film,2,1293.755,6450.0,417235.98750000005,6450.0
+Collection (New York City),MRF (Chicago),Waste,Paper,1,1293.755,891.0,57636.78525000001,891.0
+Collection (New York City),MRF (Chicago),Waste,Paper,2,1293.755,5805.0,375512.38875000004,5805.0
+Collection (New York City),MRF (Chicago),Waste,Cardboard,1,1293.755,297.0,19212.26175,297.0
+Collection (New York City),MRF (Chicago),Waste,Cardboard,2,1293.755,1934.9999999999998,125170.79625,1934.9999999999998
+Collection (Los Angeles),MRF (Phoenix),Waste,Film,1,668.923,4160.0,139135.98400000003,4160.0
+Collection (Los Angeles),MRF (Phoenix),Waste,Film,2,668.923,5704.0,190776.8396,5704.0
+Collection (Los Angeles),MRF (Phoenix),Waste,Paper,1,668.923,3640.0000000000005,121743.98600000002,3640.0000000000005
+Collection (Los Angeles),MRF (Phoenix),Waste,Paper,2,668.923,4991.000000000001,166929.73465000006,4991.000000000001
+Collection (Los Angeles),MRF (Phoenix),Waste,Cardboard,1,668.923,5200.0,173919.98,5200.0
+Collection (Los Angeles),MRF (Phoenix),Waste,Cardboard,2,668.923,7130.0,238471.04950000002,7130.0
+Collection (Chicago),MRF (Chicago),Waste,Film,1,0.0,716.0,0.0,716.0
+Collection (Chicago),MRF (Chicago),Waste,Film,2,0.0,1394.0,0.0,1394.0
+Collection (Chicago),MRF (Chicago),Waste,Paper,1,0.0,2864.0,0.0,2864.0
+Collection (Chicago),MRF (Chicago),Waste,Paper,2,0.0,5576.0,0.0,5576.0
+Collection (Chicago),MRF (Chicago),Waste,Cardboard,1,0.0,1074.0,0.0,1074.0
+Collection (Chicago),MRF (Chicago),Waste,Cardboard,2,0.0,2091.0,0.0,2091.0
+Collection (Dallas),MRF (Dallas),Waste,Film,1,0.0,5096.0,0.0,5096.0
+Collection (Dallas),MRF (Dallas),Waste,Film,2,0.0,4354.0,0.0,4354.0
+Collection (Dallas),MRF (Dallas),Waste,Paper,1,0.0,728.0,0.0,728.0
+Collection (Dallas),MRF (Dallas),Waste,Paper,2,0.0,622.0,0.0,622.0
+Collection (Dallas),MRF (Dallas),Waste,Cardboard,1,0.0,5824.0,0.0,5824.0
+Collection (Dallas),MRF (Dallas),Waste,Cardboard,2,0.0,4976.0,0.0,4976.0
+Collection (Phoenix),MRF (Phoenix),Waste,Film,1,0.0,3635.0,0.0,3635.0
+Collection (Phoenix),MRF (Phoenix),Waste,Film,2,0.0,2275.0,0.0,2275.0
+Collection (Phoenix),MRF (Phoenix),Waste,Paper,1,0.0,6543.0,0.0,6543.0
+Collection (Phoenix),MRF (Phoenix),Waste,Paper,2,0.0,4095.0,0.0,4095.0
+Collection (Phoenix),MRF (Phoenix),Waste,Cardboard,1,0.0,5089.0,0.0,5089.0
+Collection (Phoenix),MRF (Phoenix),Waste,Cardboard,2,0.0,3185.0,0.0,3185.0
+Collection (San Diego),MRF (Phoenix),Waste,Film,1,567.242,6690.0,189742.449,6690.0
+Collection (San Diego),MRF (Phoenix),Waste,Film,2,567.242,8460.0,239943.36599999998,8460.0
+Collection (San Diego),MRF (Phoenix),Waste,Paper,1,567.242,1338.0,37948.489799999996,1338.0
+Collection (San Diego),MRF (Phoenix),Waste,Paper,2,567.242,1692.0,47988.6732,1692.0
+Collection (San Diego),MRF (Phoenix),Waste,Cardboard,1,567.242,4014.0,113845.46939999999,4014.0
+Collection (San Diego),MRF (Phoenix),Waste,Cardboard,2,567.242,5076.0,143966.0196,5076.0
+Collection (Philadelphia),MRF (Chicago),Waste,Film,1,1244.116,220.0,13685.276000000002,220.0
+Collection (Philadelphia),MRF (Chicago),Waste,Film,2,1244.116,962.0,59841.979600000006,962.0
+Collection (Philadelphia),MRF (Chicago),Waste,Paper,1,1244.116,880.0,54741.10400000001,880.0
+Collection (Philadelphia),MRF (Chicago),Waste,Paper,2,1244.116,3848.0,239367.91840000002,3848.0
+Collection (Philadelphia),MRF (Chicago),Waste,Cardboard,1,1244.116,880.0,54741.10400000001,880.0
+Collection (Philadelphia),MRF (Chicago),Waste,Cardboard,2,1244.116,3848.0,239367.91840000002,3848.0
+Collection (Houston),MRF (Dallas),Waste,Film,1,411.89,2668.0,54946.126,2668.0
+Collection (Houston),MRF (Dallas),Waste,Film,2,411.89,1852.0,38141.014,1852.0
+Collection (Houston),MRF (Dallas),Waste,Paper,1,411.89,4669.0,96155.7205,4669.0
+Collection (Houston),MRF (Dallas),Waste,Paper,2,411.89,3241.0,66746.7745,3241.0
+Collection (Houston),MRF (Dallas),Waste,Cardboard,1,411.89,6670.0,137365.315,6670.0
+Collection (Houston),MRF (Dallas),Waste,Cardboard,2,411.89,4630.0,95352.535,4630.0