Implement the deterministic vsp as a possible solver

Author: BatyLeo

src/StochasticVehicleScheduling/StochasticVehicleScheduling.jl

@@ -3,7 +3,8 @@ module StochasticVehicleScheduling
 export StochasticVehicleSchedulingBenchmark
 export generate_dataset, generate_maximizer, generate_statistical_model
 export plot_instance, plot_solution
-export compact_linearized_mip, compact_mip, column_generation_algorithm, local_search
+export compact_linearized_mip,
+    compact_mip, column_generation_algorithm, local_search, deterministic_mip
 export evaluate_solution, is_feasible
 
 using ..Utils
@@ -44,6 +45,7 @@ include("solution/solution.jl")
 include("solution/algorithms/mip.jl")
 include("solution/algorithms/column_generation.jl")
 include("solution/algorithms/local_search.jl")
+include("solution/algorithms/deterministic_mip.jl")
 
 include("maximizer.jl")
 

src/StochasticVehicleScheduling/solution/algorithms/deterministic_mip.jl

@@ -0,0 +1,45 @@
+"""
+$TYPEDSIGNATURES
+
+Solves the deterministic version of the vehicle scheduling problem using a MIP model.
+Does not take into account the stochastic nature of the problem.
+"""
+function deterministic_mip(instance::Instance; model_builder=highs_model, silent=true)
+    (; graph, vehicle_cost) = instance
+    nb_nodes = nv(graph)
+    job_indices = 2:(nb_nodes - 1)
+    nodes = 1:nb_nodes
+
+    # Model definition
+    model = model_builder()
+    silent && set_silent(model)
+
+    # Variables and objective function
+    @variable(model, y[u in nodes, v in nodes; has_edge(graph, u, v)], Bin)
+
+    @objective(
+        model,
+        Min,
+        vehicle_cost * sum(y[1, v] for v in job_indices) # nb_vehicles
+    )
+
+    # Flow contraints
+    @constraint(
+        model,
+        flow[i in job_indices],
+        sum(y[j, i] for j in inneighbors(graph, i)) ==
+            sum(y[i, j] for j in outneighbors(graph, i))
+    )
+    @constraint(
+        model,
+        unit_demand[i in job_indices],
+        sum(y[j, i] for j in inneighbors(graph, i)) == 1
+    )
+
+    # Solve model
+    optimize!(model)
+    solution = value.(y)
+
+    sol = solution_from_JuMP_array(solution, graph)
+    return sol
+end

test/vsp.jl

@@ -11,6 +11,7 @@
     mip_dataset = generate_dataset(b, N; seed=0, algorithm=compact_mip)
     mipl_dataset = generate_dataset(b, N; seed=0, algorithm=compact_linearized_mip)
     local_search_dataset = generate_dataset(b, N; seed=0, algorithm=local_search)
+    deterministic_dataset = generate_dataset(b, N; seed=0, algorithm=deterministic_mip)
     @test length(dataset) == N
 
     figure_1 = plot_instance(b, dataset[1])
@@ -25,11 +26,17 @@
     gap_mip = compute_gap(b, mip_dataset, model, maximizer)
     gap_mipl = compute_gap(b, mipl_dataset, model, maximizer)
     gap_local_search = compute_gap(b, local_search_dataset, model, maximizer)
+    gap_deterministic = compute_gap(b, deterministic_dataset, model, maximizer)
 
-    @test gap >= 0 && gap_mip >= 0 && gap_mipl >= 0 && gap_local_search >= 0
+    @test gap >= 0 &&
+        gap_mip >= 0 &&
+        gap_mipl >= 0 &&
+        gap_local_search >= 0 &&
+        gap_deterministic >= 0
     @test gap_mip ≈ gap_mipl rtol = 1e-2
     @test gap_mip >= gap_local_search
     @test gap_mip >= gap
+    @test gap__local_search >= gap_deterministic
 
     for sample in dataset
         x = sample.x