Implement multi-period heuristic

gh-actions
Alinson S. Xavier 5 years ago
parent fe9cacef24
commit a6846be9eb

@ -5,11 +5,11 @@ VERSION := 0.4
all: docs test
build/sysimage.so: src/sysimage.jl Project.toml Manifest.toml
mkdir -p build
$(JULIA) src/sysimage.jl
build/test.log: $(SRC_FILES) build/sysimage.so
@echo Running tests...
cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl | tee ../build/test.log
cd test; $(JULIA) --sysimage ../build/sysimage.so runtests.jl
clean:
rm -rf build/*

@ -1,7 +1,7 @@
name = "RELOG"
uuid = "a2afcdf7-cf04-4913-85f9-c0d81ddf2008"
authors = ["Alinson S Xavier <axavier@anl.gov>"]
version = "0.4.0"
version = "0.4.1"
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@ -19,6 +19,7 @@ MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
PackageCompiler = "9b87118b-4619-50d2-8e1e-99f35a4d4d9d"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[compat]

@ -79,3 +79,20 @@ RELOG.solve("instance.json",
output="solution.json",
optimizer=gurobi)
```
### 4.2 Multi-period heuristics
For large-scale instances, it may be too time-consuming to find an exact optimal solution to the multi-period version of the problem. For these situations, RELOG includes a heuristic solution method, which proceeds as follows:
1. First, RELOG creates a single-period version of the problem, in which most values are replaced by their averages. This single-period problem is typically much easier to solve.
2. After solving the simplified problem, RELOG resolves the multi-period version of the problem, but considering only candidate plant locations that were selected by the optimal solution to the single-period version of the problem. All remaining candidate plant locations are removed.
To solve an instance using this heuristic, use the option `heuristic=true`, as shown below.
```julia
using RELOG
solution = RELOG.solve("/home/user/instance.json",
heuristic=true)
```

@ -2,7 +2,11 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using JSON, JSONSchema, Printf
using DataStructures
using JSON
using JSONSchema
using Printf
using Statistics
mutable struct Product
@ -55,6 +59,7 @@ mutable struct Instance
building_period::Array{Int64}
end
function validate(json, schema)
result = JSONSchema.validate(json, schema)
if result !== nothing
@ -77,7 +82,7 @@ function parsefile(path::String)::Instance
end
function parse(json::Dict)::Instance
function parse(json)::Instance
basedir = dirname(@__FILE__)
json_schema = JSON.parsefile("$basedir/schemas/input.json")
validate(json, Schema(json_schema))
@ -209,3 +214,55 @@ function parse(json::Dict)::Instance
return Instance(T, products, collection_centers, plants, building_period)
end
"""
_compress(instance::Instance)
Create a single-period instance from a multi-period one. Specifically,
replaces every time-dependent attribute, such as initial_amounts,
by a list with a single element, which is either a sum, an average,
or something else that makes sense to that specific attribute.
"""
function _compress(instance::Instance)::Instance
T = instance.time
compressed = deepcopy(instance)
compressed.time = 1
compressed.building_period = [1]
# Compress products
for p in compressed.products
p.transportation_cost = [mean(p.transportation_cost)]
p.transportation_energy = [mean(p.transportation_energy)]
for (emission_name, emission_value) in p.transportation_emissions
p.transportation_emissions[emission_name] = [mean(emission_value)]
end
end
# Compress collection centers
for c in compressed.collection_centers
c.amount = [maximum(c.amount) * T]
end
# Compress plants
for plant in compressed.plants
plant.energy = [mean(plant.energy)]
for (emission_name, emission_value) in plant.emissions
plant.emissions[emission_name] = [mean(emission_value)]
end
for s in plant.sizes
s.capacity *= T
s.variable_operating_cost = [mean(s.variable_operating_cost)]
s.opening_cost = [s.opening_cost[1]]
s.fixed_operating_cost = [sum(s.fixed_operating_cost)]
end
for (prod_name, disp_limit) in plant.disposal_limit
plant.disposal_limit[prod_name] = [sum(disp_limit)]
end
for (prod_name, disp_cost) in plant.disposal_cost
plant.disposal_cost[prod_name] = [mean(disp_cost)]
end
end
return compressed
end

@ -197,7 +197,9 @@ default_lp_optimizer = optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
function solve(instance::Instance;
optimizer=nothing,
output=nothing)
output=nothing,
marginal_costs=true,
)
milp_optimizer = lp_optimizer = optimizer
if optimizer == nothing
@ -224,6 +226,7 @@ function solve(instance::Instance;
return OrderedDict()
end
if marginal_costs
@info "Re-optimizing with integer variables fixed..."
all_vars = JuMP.all_variables(model.mip)
vals = OrderedDict(var => JuMP.value(var) for var in all_vars)
@ -235,9 +238,10 @@ function solve(instance::Instance;
end
end
JuMP.optimize!(model.mip)
end
@info "Extracting solution..."
solution = get_solution(model)
solution = get_solution(model, marginal_costs=marginal_costs)
if output != nothing
@info "Writing solution: $output"
@ -249,13 +253,43 @@ function solve(instance::Instance;
return solution
end
function solve(filename::String; kwargs...)
function solve(filename::AbstractString;
heuristic=false,
kwargs...,
)
@info "Reading $filename..."
instance = RELOG.parsefile(filename)
return solve(instance; kwargs...)
if heuristic
@info "Solving single-period version..."
compressed = _compress(instance)
csol = solve(compressed;
output=nothing,
marginal_costs=false,
kwargs...)
@info "Filtering candidate locations..."
selected_pairs = []
for (plant_name, plant_dict) in csol["Plants"]
for (location_name, location_dict) in plant_dict
push!(selected_pairs, (plant_name, location_name))
end
end
filtered_plants = []
for p in instance.plants
if (p.plant_name, p.location_name) in selected_pairs
push!(filtered_plants, p)
end
end
instance.plants = filtered_plants
@info "Solving original version..."
end
sol = solve(instance; kwargs...)
return sol
end
function get_solution(model::ManufacturingModel)
function get_solution(model::ManufacturingModel;
marginal_costs=true,
)
mip, vars, eqs, graph, instance = model.mip, model.vars, model.eqs, model.graph, model.instance
T = instance.time
@ -291,16 +325,18 @@ function get_solution(model::ManufacturingModel)
end
# Products
if marginal_costs
for n in graph.collection_shipping_nodes
location_dict = OrderedDict{Any, Any}(
"Marginal cost (\$/tonne)" => [round(abs(JuMP.shadow_price(eqs.balance[n, t])), digits=2)
for t in 1:T],
for t in 1:T]
)
if n.product.name keys(output["Products"])
output["Products"][n.product.name] = OrderedDict()
end
output["Products"][n.product.name][n.location.name] = location_dict
end
end
# Plants
for plant in instance.plants

@ -11,7 +11,6 @@ using RELOG
centers = instance.collection_centers
plants = instance.plants
products = instance.products
location_name_to_plant = Dict(p.location_name => p for p in plants)
product_name_to_product = Dict(p.name => p for p in products)
@ -75,5 +74,54 @@ using RELOG
@testset "validate timeseries" begin
@test_throws String RELOG.parsefile("fixtures/s1-wrong-length.json")
end
@testset "compress" begin
basedir = dirname(@__FILE__)
instance = RELOG.parsefile("$basedir/../instances/s1.json")
compressed = RELOG._compress(instance)
product_name_to_product = Dict(p.name => p for p in compressed.products)
location_name_to_facility = Dict()
for p in compressed.plants
location_name_to_facility[p.location_name] = p
end
for c in compressed.collection_centers
location_name_to_facility[c.name] = c
end
p1 = product_name_to_product["P1"]
p2 = product_name_to_product["P2"]
p3 = product_name_to_product["P3"]
c1 = location_name_to_facility["C1"]
l1 = location_name_to_facility["L1"]
@test compressed.time == 1
@test compressed.building_period == [1]
@test p1.name == "P1"
@test p1.transportation_cost [0.015]
@test p1.transportation_energy [0.115]
@test p1.transportation_emissions["CO2"] [0.051]
@test p1.transportation_emissions["CH4"] [0.0025]
@test c1.name == "C1"
@test c1.amount [1869.12]
@test l1.plant_name == "F1"
@test l1.location_name == "L1"
@test l1.energy [0.115]
@test l1.emissions["CO2"] [0.051]
@test l1.emissions["CH4"] [0.0025]
@test l1.sizes[1].opening_cost [500]
@test l1.sizes[2].opening_cost [1250]
@test l1.sizes[1].fixed_operating_cost [60]
@test l1.sizes[2].fixed_operating_cost [60]
@test l1.sizes[1].variable_operating_cost [30]
@test l1.sizes[2].variable_operating_cost [30]
@test l1.disposal_limit[p2] [2.0]
@test l1.disposal_limit[p3] [2.0]
@test l1.disposal_cost[p2] [-10.0]
@test l1.disposal_cost[p3] [-10.0]
end
end

@ -41,7 +41,7 @@ using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
#MOI.write_to_file(dest, "model.lp")
end
@testset "solve" begin
@testset "solve (exact)" begin
solution_filename = tempname()
solution = RELOG.solve("$(pwd())/../instances/s1.json",
output=solution_filename)
@ -59,6 +59,12 @@ using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
@test "F4" in keys(solution["Plants"])
end
@testset "solve (heuristic)" begin
# Should not crash
solution = RELOG.solve("$(pwd())/../instances/s1.json", heuristic=true)
end
@testset "infeasible solve" begin
json = JSON.parsefile("$(pwd())/../instances/s1.json")
for (location_name, location_dict) in json["products"]["P1"]["initial amounts"]
@ -66,7 +72,6 @@ using RELOG, Cbc, JuMP, Printf, JSON, MathOptInterface.FileFormats
end
RELOG.solve(RELOG.parse(json))
end
end

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