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Implement basic reports; fix boat example

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feature/composition2
Alinson S. Xavier 2 years ago
parent 06642c631f
commit 319e5f1ed3
Signed by: isoron
GPG Key ID: 0DA8E4B9E1109DCA
11 changed files with 280 additions and 3144 deletions
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2
Project.toml
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@ -4,6 +4,8 @@ authors = ["Alinson S. Xavier <git@axavier.org>"]
version = "0.1.0" version = "0.1.0"
[deps] [deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d" Geodesy = "0ef565a4-170c-5f04-8de2-149903a85f3d"
JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572" JuMP = "4076af6c-e467-56ae-b986-b466b2749572"

3
src/RELOG.jl
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@ -5,5 +5,8 @@ include("instance/parse.jl")
include("model/jumpext.jl") include("model/jumpext.jl")
include("model/dist.jl") include("model/dist.jl")
include("model/build.jl") include("model/build.jl")
include("reports/plants.jl")
include("reports/transportation.jl")
include("reports/centers.jl")
end # module RELOG end # module RELOG

14
src/model/build.jl
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@ -6,14 +6,15 @@ function build_model(instance::Instance; optimizer, variable_names::Bool = false
products = instance.products products = instance.products
plants = instance.plants plants = instance.plants
T = 1:instance.time_horizon T = 1:instance.time_horizon
model.ext[:instance] = instance
# Transportation edges # Transportation edges
# ------------------------------------------------------------------------- # -------------------------------------------------------------------------
# Connectivity # Connectivity
E = [] model.ext[:E] = E = []
E_in = Dict(src => [] for src in plants ∪ centers) model.ext[:E_in] = E_in = Dict(src => [] for src in plants ∪ centers)
E_out = Dict(src => [] for src in plants ∪ centers) model.ext[:E_out] = E_out = Dict(src => [] for src in plants ∪ centers)
function push_edge!(src, dst, m) function push_edge!(src, dst, m)
push!(E, (src, dst, m)) push!(E, (src, dst, m))
@ -28,7 +29,7 @@ function build_model(instance::Instance; optimizer, variable_names::Bool = false
# Plant to plant # Plant to plant
for p2 in plants for p2 in plants
p1 != p2 || continue p1 != p2 || continue
m ∉ keys(p2.input_mix) || continue m ∈ keys(p2.input_mix) || continue
push_edge!(p1, p2, m) push_edge!(p1, p2, m)
end end
@ -57,7 +58,7 @@ function build_model(instance::Instance; optimizer, variable_names::Bool = false
end end
# Distances # Distances
distances = Dict() model.ext[:distances] = distances = Dict()
for (p1, p2, m) in E for (p1, p2, m) in E
d = _calculate_distance(p1.latitude, p1.longitude, p2.latitude, p2.longitude) d = _calculate_distance(p1.latitude, p1.longitude, p2.latitude, p2.longitude)
distances[p1, p2, m] = d distances[p1, p2, m] = d
@ -86,9 +87,6 @@ function build_model(instance::Instance; optimizer, variable_names::Bool = false
for p in plants, m in keys(p.output), t in T for p in plants, m in keys(p.output), t in T
z_prod[p.name, m.name, t] = @variable(model, lower_bound = 0) z_prod[p.name, m.name, t] = @variable(model, lower_bound = 0)
end end
for c in centers, m in c.outputs, t in T
z_prod[c.name, m.name, t] = @variable(model, lower_bound = 0)
end
# Amount of product m disposed at plant/center p at time T # Amount of product m disposed at plant/center p at time T
z_disp = _init(model, :z_disp) z_disp = _init(model, :z_disp)

47
src/reports/centers.jl
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@ -0,0 +1,47 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function centers_report(model)::DataFrame
df = DataFrame()
df."center" = String[]
df."year" = Int[]
df."input product" = String[]
df."input amount (tonne)" = Float64[]
centers = model.ext[:instance].centers
T = 1:model.ext[:instance].time_horizon
for c in centers, t in T
input_name = (c.input === nothing) ? "" : c.input.name
input = round(value(model[:z_input][c.name, t]), digits = 3)
push!(df, [c.name, t, input_name, input])
end
return df
end
function center_outputs_report(model)::DataFrame
df = DataFrame()
df."center" = String[]
df."output product" = String[]
df."year" = Int[]
df."amount collected (tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
centers = model.ext[:instance].centers
T = 1:model.ext[:instance].time_horizon
for c in centers, m in c.outputs, t in T
collected = round(value(model[:z_collected][c.name, m.name, t]), digits = 3)
disposed = round(value(model[:z_disp][c.name, m.name, t]), digits = 3)
push!(df, [c.name, m.name, t, collected, disposed])
end
return df
end
write_centers_report(solution, filename) = CSV.write(filename, centers_report(solution))
write_center_outputs_report(solution, filename) =
CSV.write(filename, center_outputs_report(solution))

49
src/reports/plants.jl
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@ -0,0 +1,49 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function plants_report(model)::DataFrame
df = DataFrame()
df."plant" = String[]
df."year" = Int[]
df."operational?" = Bool[]
df."input amount (tonne)" = Float64[]
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, t in T
operational = JuMP.value(model[:x][p.name, t]) > 0.5
input = value(model[:z_input][p.name, t])
operational || continue
push!(df, [p.name, t, operational, input])
end
return df
end
function plant_outputs_report(model)::DataFrame
df = DataFrame()
df."plant" = String[]
df."output product" = String[]
df."year" = Int[]
df."amount produced (tonne)" = Float64[]
df."amount disposed (tonne)" = Float64[]
plants = model.ext[:instance].plants
T = 1:model.ext[:instance].time_horizon
for p in plants, m in keys(p.output), t in T
produced = JuMP.value(model[:z_prod][p.name, m.name, t])
disposed = JuMP.value(model[:z_disp][p.name, m.name, t])
produced > 1e-3 || continue
push!(df, [p.name, m.name, t, produced, disposed])
end
return df
end
write_plants_report(solution, filename) = CSV.write(filename, plants_report(solution))
write_plant_outputs_report(solution, filename) =
CSV.write(filename, plant_outputs_report(solution))

31
src/reports/transportation.jl
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@ -0,0 +1,31 @@
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using DataFrames
using CSV
function transportation_report(model)::DataFrame
df = DataFrame()
df."source" = String[]
df."destination" = String[]
df."product" = String[]
df."year" = Int[]
df."amount sent (tonne)" = Float64[]
df."distance (km)" = Float64[]
E = model.ext[:E]
distances = model.ext[:distances]
T = 1:model.ext[:instance].time_horizon
for (p1, p2, m) in E, t in T
amount = value(model[:y][p1.name, p2.name, m.name, t])
amount > 1e-3 || continue
distance = distances[p1, p2, m]
push!(df, [p1.name, p2.name, m.name, t, amount, distance])
end
return df
end
write_transportation_report(solution, filename) =
CSV.write(filename, transportation_report(solution))

94
test/fixtures/boat_example.ipynb vendored
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@ -2,51 +2,59 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 67,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"import json\n", "import json\n",
"\n", "\n",
"# cities_a = {\n",
"# \"Chicago\": [41.881832, -87.623177],\n",
"# \"New York City\": [40.712776, -74.005974],\n",
"# \"Los Angeles\": [34.052235, -118.243683],\n",
"# \"Houston\": [29.760427, -95.369804],\n",
"# \"Phoenix\": [33.448376, -112.074036],\n",
"# \"Philadelphia\": [39.952583, -75.165222],\n",
"# \"San Antonio\": [29.424122, -98.493629],\n",
"# \"San Diego\": [32.715736, -117.161087],\n",
"# \"Dallas\": [32.776664, -96.796988],\n",
"# \"San Jose\": [37.338208, -121.886329],\n",
"# \"Austin\": [30.267153, -97.743061],\n",
"# \"Jacksonville\": [30.332184, -81.655651],\n",
"# \"Fort Worth\": [32.755488, -97.330766],\n",
"# \"Columbus\": [39.961176, -82.998794],\n",
"# \"Charlotte\": [35.227087, -80.843127],\n",
"# \"Indianapolis\": [39.768403, -86.158068],\n",
"# \"San Francisco\": [37.774929, -122.419416],\n",
"# \"Seattle\": [47.606209, -122.332071],\n",
"# \"Denver\": [39.739236, -104.990251],\n",
"# \"Washington D.C.\": [38.907192, -77.036871],\n",
"# \"Nashville\": [36.162664, -86.781602],\n",
"# \"Detroit\": [42.331427, -83.045754],\n",
"# \"Oklahoma City\": [35.467560, -97.516428],\n",
"# \"Portland\": [45.505106, -122.675026],\n",
"# \"Las Vegas\": [36.169941, -115.139830],\n",
"# }\n",
"\n",
"# cities_b = {\n",
"# \"Chicago\": [41.881832, -87.623177],\n",
"# \"Phoenix\": [33.448376, -112.074036],\n",
"# \"Dallas\": [32.776664, -96.796988],\n",
"# \"San Jose\": [37.338208, -121.886329],\n",
"# \"Seattle\": [47.606209, -122.332071],\n",
"# \"Las Vegas\": [36.169941, -115.139830],\n",
"# }\n",
"\n",
"cities_a = {\n", "cities_a = {\n",
" \"Chicago\": [41.881832, -87.623177],\n", " \"Chicago\": [41.881832, -87.623177],\n",
" \"New York City\": [40.712776, -74.005974],\n",
" \"Los Angeles\": [34.052235, -118.243683],\n",
" \"Houston\": [29.760427, -95.369804],\n",
" \"Phoenix\": [33.448376, -112.074036],\n",
" \"Philadelphia\": [39.952583, -75.165222],\n",
" \"San Antonio\": [29.424122, -98.493629],\n",
" \"San Diego\": [32.715736, -117.161087],\n",
" \"Dallas\": [32.776664, -96.796988],\n",
" \"San Jose\": [37.338208, -121.886329],\n",
" \"Austin\": [30.267153, -97.743061],\n",
" \"Jacksonville\": [30.332184, -81.655651],\n",
" \"Fort Worth\": [32.755488, -97.330766],\n",
" \"Columbus\": [39.961176, -82.998794],\n",
" \"Charlotte\": [35.227087, -80.843127],\n",
" \"Indianapolis\": [39.768403, -86.158068],\n",
" \"San Francisco\": [37.774929, -122.419416],\n",
" \"Seattle\": [47.606209, -122.332071],\n",
" \"Denver\": [39.739236, -104.990251],\n",
" \"Washington D.C.\": [38.907192, -77.036871],\n",
" \"Nashville\": [36.162664, -86.781602],\n",
" \"Detroit\": [42.331427, -83.045754],\n",
" \"Oklahoma City\": [35.467560, -97.516428],\n",
" \"Portland\": [45.505106, -122.675026],\n",
" \"Las Vegas\": [36.169941, -115.139830],\n",
"}\n", "}\n",
"\n", "\n",
"cities_b = {\n", "cities_b = {\n",
" \"Chicago\": [41.881832, -87.623177],\n", " \"Chicago\": [41.881832, -87.623177],\n",
" \"Phoenix\": [33.448376, -112.074036],\n",
" \"Dallas\": [32.776664, -96.796988],\n",
" \"San Jose\": [37.338208, -121.886329],\n",
" \"Seattle\": [47.606209, -122.332071],\n",
" \"Las Vegas\": [36.169941, -115.139830],\n",
"}\n", "}\n",
"\n", "\n",
"parameters = {\n", "parameters = {\n",
" \"time horizon (years)\": 10,\n", " \"time horizon (years)\": 1,\n",
" \"building period (years)\": [1],\n", " \"building period (years)\": [1],\n",
" \"distance metric\": \"Euclidean\",\n", " \"distance metric\": \"Euclidean\",\n",
"}\n", "}\n",
@ -54,24 +62,24 @@
"nail_factory = {\n", "nail_factory = {\n",
" \"input\": None,\n", " \"input\": None,\n",
" \"outputs\": [\"Nail\"],\n", " \"outputs\": [\"Nail\"],\n",
" \"fixed output (tonne)\": {\"Nail\": 1},\n", " \"fixed output (tonne)\": {\"Nail\": 5},\n",
" \"variable output (tonne/tonne)\": {\"Nail\": 0},\n", " \"variable output (tonne/tonne)\": {\"Nail\": 0},\n",
" \"revenue ($/tonne)\": None,\n", " \"revenue ($/tonne)\": None,\n",
" \"collection cost ($/tonne)\": {\"Nail\": 1000},\n", " \"collection cost ($/tonne)\": {\"Nail\": 1000},\n",
" \"operating cost ($)\": 0,\n", " \"operating cost ($)\": 0,\n",
" \"disposal limit (tonne)\": {\"Nail\": None},\n", " \"disposal limit (tonne)\": {\"Nail\": 0},\n",
" \"disposal cost ($/tonne)\": {\"Nail\": 0},\n", " \"disposal cost ($/tonne)\": {\"Nail\": 0},\n",
"}\n", "}\n",
"\n", "\n",
"forest = {\n", "forest = {\n",
" \"input\": None,\n", " \"input\": None,\n",
" \"outputs\": [\"Wood\"],\n", " \"outputs\": [\"Wood\"],\n",
" \"fixed output (tonne)\": {\"Wood\": 100},\n", " \"fixed output (tonne)\": {\"Wood\": 95},\n",
" \"variable output (tonne/tonne)\": {\"Wood\": 0},\n", " \"variable output (tonne/tonne)\": {\"Wood\": 0},\n",
" \"revenue ($/tonne)\": None,\n", " \"revenue ($/tonne)\": None,\n",
" \"collection cost ($/tonne)\": {\"Wood\": 250},\n", " \"collection cost ($/tonne)\": {\"Wood\": 250},\n",
" \"operating cost ($)\": 0,\n", " \"operating cost ($)\": 0,\n",
" \"disposal limit (tonne)\": {\"Wood\": None},\n", " \"disposal limit (tonne)\": {\"Wood\": 0},\n",
" \"disposal cost ($/tonne)\": {\"Wood\": 0},\n", " \"disposal cost ($/tonne)\": {\"Wood\": 0},\n",
"}\n", "}\n",
"\n", "\n",
@ -80,7 +88,7 @@
" \"outputs\": [\"UsedBoat\"],\n", " \"outputs\": [\"UsedBoat\"],\n",
" \"fixed output (tonne)\": {\"UsedBoat\": 0},\n", " \"fixed output (tonne)\": {\"UsedBoat\": 0},\n",
" \"variable output (tonne/tonne)\": {\"UsedBoat\": [0.10, 0.25, 0.10]},\n", " \"variable output (tonne/tonne)\": {\"UsedBoat\": [0.10, 0.25, 0.10]},\n",
" \"revenue ($/tonne)\": 3_000,\n", " \"revenue ($/tonne)\": 300_000,\n",
" \"collection cost ($/tonne)\": {\"UsedBoat\": 100},\n", " \"collection cost ($/tonne)\": {\"UsedBoat\": 100},\n",
" \"operating cost ($)\": 125_000,\n", " \"operating cost ($)\": 125_000,\n",
" \"disposal limit (tonne)\": {\"UsedBoat\": 0},\n", " \"disposal limit (tonne)\": {\"UsedBoat\": 0},\n",
@ -115,7 +123,7 @@
" },\n", " },\n",
" \"capacities\": [\n", " \"capacities\": [\n",
" {\n", " {\n",
" \"size (tonne)\": 50,\n", " \"size (tonne)\": 200,\n",
" \"opening cost ($)\": 10_000,\n", " \"opening cost ($)\": 10_000,\n",
" \"fixed operating cost ($)\": 1_000,\n", " \"fixed operating cost ($)\": 1_000,\n",
" \"variable operating cost ($/tonne)\": 5,\n", " \"variable operating cost ($/tonne)\": 5,\n",
@ -144,7 +152,7 @@
" \"Nail\": 0,\n", " \"Nail\": 0,\n",
" \"Wood\": 0,\n", " \"Wood\": 0,\n",
" },\n", " },\n",
" \"disposal limit (tonne)\": {\"Nail\": None, \"Wood\": None},\n", " \"disposal limit (tonne)\": {\"Nail\": 0, \"Wood\": 0},\n",
" \"capacities\": [\n", " \"capacities\": [\n",
" {\n", " {\n",
" \"size (tonne)\": 50,\n", " \"size (tonne)\": 50,\n",
@ -166,7 +174,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 68,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -199,8 +207,10 @@
" \"longitude (deg)\": city_location[1],\n", " \"longitude (deg)\": city_location[1],\n",
" }\n", " }\n",
" for (city_name, city_location) in cities_a.items()\n", " for (city_name, city_location) in cities_a.items()\n",
" },\n", " }\n",
" \"plants\": {\n", " ,\n",
" \"plants\":\n",
" {\n",
" f\"BoatFactory ({city_name})\": {\n", " f\"BoatFactory ({city_name})\": {\n",
" **boat_factory,\n", " **boat_factory,\n",
" \"latitude (deg)\": city_location[0],\n", " \"latitude (deg)\": city_location[0],\n",
@ -209,7 +219,7 @@
" for (city_name, city_location) in cities_a.items()\n", " for (city_name, city_location) in cities_a.items()\n",
" } | {\n", " } | {\n",
" f\"RecyclingPlant ({city_name})\": {\n", " f\"RecyclingPlant ({city_name})\": {\n",
" **boat_factory,\n", " **recycling_plant,\n",
" \"latitude (deg)\": city_location[0],\n", " \"latitude (deg)\": city_location[0],\n",
" \"longitude (deg)\": city_location[1],\n", " \"longitude (deg)\": city_location[1],\n",
" }\n", " }\n",

3158
test/fixtures/boat_example.json vendored
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5
test/src/RELOGT.jl
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@ -7,6 +7,7 @@ using JuliaFormatter
include("instance/parse_test.jl") include("instance/parse_test.jl")
include("model/build_test.jl") include("model/build_test.jl")
include("model/dist_test.jl") include("model/dist_test.jl")
include("reports_test.jl")
basedir = dirname(@__FILE__) basedir = dirname(@__FILE__)
@ -18,9 +19,9 @@ function runtests()
@testset "RELOG" begin @testset "RELOG" begin
instance_parse_test_1() instance_parse_test_1()
instance_parse_test_2() instance_parse_test_2()
model_build_test_1() model_build_test()
model_build_test_2()
model_dist_test() model_dist_test()
report_tests()
end end
end end

9
test/src/model/build_test.jl
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@ -3,7 +3,7 @@ using Test
using HiGHS using HiGHS
using JuMP using JuMP
function model_build_test_1() function model_build_test()
instance = RELOG.parsefile(fixture("simple.json")) instance = RELOG.parsefile(fixture("simple.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true) model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
y = model[:y] y = model[:y]
@ -110,10 +110,3 @@ function model_build_test_1()
"eq_disposal_limit[C1,P2,1] : z_disp[C1,P2,1] ≤ 0" "eq_disposal_limit[C1,P2,1] : z_disp[C1,P2,1] ≤ 0"
@test ("C1", "P3", 1) ∉ keys(model[:eq_disposal_limit]) @test ("C1", "P3", 1) ∉ keys(model[:eq_disposal_limit])
end end
function model_build_test_2()
instance = RELOG.parsefile(fixture("boat_example.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer)
optimize!(model)
end

12
test/src/reports_test.jl
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@ -0,0 +1,12 @@
function report_tests()
# Load and solve the boat example
instance = RELOG.parsefile(fixture("boat_example.json"))
model = RELOG.build_model(instance, optimizer = HiGHS.Optimizer, variable_names = true)
optimize!(model)
write_to_file(model, "tmp/model.lp")
RELOG.write_plants_report(model, "tmp/plants.csv")
RELOG.write_plant_outputs_report(model, "tmp/plant_outputs.csv")
RELOG.write_centers_report(model, "tmp/centers.csv")
RELOG.write_center_outputs_report(model, "tmp/center_outputs.csv")
RELOG.write_transportation_report(model, "tmp/transportation.csv")
end
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