Implement resolve

src/model/build.jl

@@ -4,7 +4,6 @@
 
 using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
 
-
 function build_model(instance::Instance, graph::Graph, optimizer)::JuMP.Model
     model = Model(optimizer)
     model[:instance] = instance

src/model/resolve.jl

@@ -0,0 +1,97 @@
+# RELOG: Reverse Logistics Optimization
+# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
+# Released under the modified BSD license. See COPYING.md for more details.
+
+using JuMP
+
+function resolve(model_old, filename::AbstractString; kwargs...)::OrderedDict
+    @info "Reading $filename..."
+    instance = RELOG.parsefile(filename)
+    return resolve(model_old, instance; kwargs...)
+end
+
+function resolve(model_old, instance::Instance; optimizer = nothing)::OrderedDict
+    milp_optimizer = lp_optimizer = optimizer
+    if optimizer === nothing
+        milp_optimizer = _get_default_milp_optimizer()
+        lp_optimizer = _get_default_lp_optimizer()
+    end
+
+    @info "Building new graph..."
+    graph = build_graph(instance)
+    _print_graph_stats(instance, graph)
+
+    @info "Building new optimization model..."
+    model_new = RELOG.build_model(instance, graph, milp_optimizer)
+
+    @info "Fixing decision variables..."
+    _fix_plants!(model_old, model_new)
+    JuMP.set_optimizer(model_new, lp_optimizer)
+
+    @info "Optimizing MILP..."
+    JuMP.optimize!(model_new)
+
+    if !has_values(model_new)
+        @warn("No solution available")
+        return OrderedDict()
+    end
+
+    @info "Extracting solution..."
+    solution = get_solution(model_new, marginal_costs = true)
+
+    return solution
+end
+
+function _fix_plants!(model_old, model_new)::Nothing
+    T = model_new[:instance].time
+
+    # Fix open_plant variables
+    for ((node_old, t), var_old) in model_old[:open_plant]
+        value_old = JuMP.value(var_old)
+        node_new = model_new[:graph].name_to_process_node_map[(
+            node_old.location.plant_name,
+            node_old.location.location_name,
+        )]
+        var_new = model_new[:open_plant][node_new, t]
+        JuMP.unset_binary(var_new)
+        JuMP.fix(var_new, value_old)
+    end
+
+    # Fix is_open variables
+    for ((node_old, t), var_old) in model_old[:is_open]
+        value_old = JuMP.value(var_old)
+        node_new = model_new[:graph].name_to_process_node_map[(
+            node_old.location.plant_name,
+            node_old.location.location_name,
+        )]
+        var_new = model_new[:is_open][node_new, t]
+        JuMP.unset_binary(var_new)
+        JuMP.fix(var_new, value_old)
+    end
+
+    # Fix plant capacities
+    for ((node_old, t), var_old) in model_old[:capacity]
+        value_old = JuMP.value(var_old)
+        node_new = model_new[:graph].name_to_process_node_map[(
+            node_old.location.plant_name,
+            node_old.location.location_name,
+        )]
+        var_new = model_new[:capacity][node_new, t]
+        JuMP.delete_lower_bound(var_new)
+        JuMP.delete_upper_bound(var_new)
+        JuMP.fix(var_new, value_old)
+    end
+
+    # Fix plant expansion
+    for ((node_old, t), var_old) in model_old[:expansion]
+        value_old = JuMP.value(var_old)
+        node_new = model_new[:graph].name_to_process_node_map[(
+            node_old.location.plant_name,
+            node_old.location.location_name,
+        )]
+        var_new = model_new[:expansion][node_new, t]
+        JuMP.delete_lower_bound(var_new)
+        JuMP.delete_upper_bound(var_new)
+        JuMP.fix(var_new, value_old)
+    end
+end

src/model/solve.jl

@@ -4,24 +4,16 @@
 
 using JuMP, LinearAlgebra, Geodesy, Cbc, Clp, ProgressBars, Printf, DataStructures
 
-default_milp_optimizer = optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
-default_lp_optimizer = optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
+function _get_default_milp_optimizer()
+    return optimizer_with_attributes(Cbc.Optimizer, "logLevel" => 0)
+end
 
-function solve(
-    instance::Instance;
-    optimizer = nothing,
-    output = nothing,
-    marginal_costs = true,
-)
+function _get_default_lp_optimizer()
+    return optimizer_with_attributes(Clp.Optimizer, "LogLevel" => 0)
+end
 
-    milp_optimizer = lp_optimizer = optimizer
-    if optimizer == nothing
-        milp_optimizer = default_milp_optimizer
-        lp_optimizer = default_lp_optimizer
-    end
 
-    @info "Building graph..."
-    graph = RELOG.build_graph(instance)
+function _print_graph_stats(instance::Instance, graph::Graph)::Nothing
     @info @sprintf("    %12d time periods", instance.time)
     @info @sprintf("    %12d process nodes", length(graph.process_nodes))
     @info @sprintf("    %12d shipping nodes (plant)", length(graph.plant_shipping_nodes))
@@ -30,6 +22,26 @@ function solve(
         length(graph.collection_shipping_nodes)
     )
     @info @sprintf("    %12d arcs", length(graph.arcs))
+    return
+end
+
+function solve(
+    instance::Instance;
+    optimizer = nothing,
+    output = nothing,
+    marginal_costs = true,
+    return_model = false,
+)
+
+    milp_optimizer = lp_optimizer = optimizer
+    if optimizer == nothing
+        milp_optimizer = _get_default_milp_optimizer()
+        lp_optimizer = _get_default_lp_optimizer()
+    end
+
+    @info "Building graph..."
+    graph = RELOG.build_graph(instance)
+    _print_graph_stats(instance, graph)
 
     @info "Building optimization model..."
     model = RELOG.build_model(instance, graph, milp_optimizer)
@@ -62,7 +74,11 @@ function solve(
         write(solution, output)
     end
 
-    return solution
+    if return_model
+        return solution, model
+    else
+        return solution
+    end
 end
 
 function solve(filename::AbstractString; heuristic = false, kwargs...)