Implement multi-period heuristic

src/docs/usage.md

@@ -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)
+```

src/instance.jl

@@ -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

src/model.jl

@@ -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
@@ -224,20 +226,22 @@ function solve(instance::Instance;
         return OrderedDict()
     end
     
-    @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)
-    JuMP.set_optimizer(model.mip, lp_optimizer)
-    for var in all_vars
-        if JuMP.is_binary(var)
-            JuMP.unset_binary(var)
-            JuMP.fix(var, vals[var])
+    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)
+        JuMP.set_optimizer(model.mip, lp_optimizer)
+        for var in all_vars
+            if JuMP.is_binary(var)
+                JuMP.unset_binary(var)
+                JuMP.fix(var, vals[var])
+            end
         end
+        JuMP.optimize!(model.mip)
     end
-    JuMP.optimize!(model.mip)
     
     @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
     
@@ -291,15 +325,17 @@ function get_solution(model::ManufacturingModel)
     end
     
     # Products
-    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],
-        )
-        if n.product.name ∉ keys(output["Products"])
-            output["Products"][n.product.name] = OrderedDict()
+    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]
+            )
+            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
-        output["Products"][n.product.name][n.location.name] = location_dict
     end
     
     # Plants