use StableRNG

Author: BatyLeo

test/vsp.jl

@@ -3,15 +3,22 @@
     using DecisionFocusedLearningBenchmarks.StochasticVehicleScheduling
     using Graphs
     using Plots
+    using StableRNGs: StableRNG
 
     b = StochasticVehicleSchedulingBenchmark(; nb_tasks=25, nb_scenarios=10)
 
     N = 5
-    dataset = generate_dataset(b, N; seed=0)
-    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)
+    dataset = generate_dataset(b, N; seed=0, rng=StableRNG(0))
+    mip_dataset = generate_dataset(b, N; seed=0, rng=StableRNG(0), algorithm=compact_mip)
+    mipl_dataset = generate_dataset(
+        b, N; seed=0, rng=StableRNG(0), algorithm=compact_linearized_mip
+    )
+    local_search_dataset = generate_dataset(
+        b, N; seed=0, rng=StableRNG(0), algorithm=local_search
+    )
+    deterministic_dataset = generate_dataset(
+        b, N; seed=0, rng=StableRNG(0), algorithm=deterministic_mip
+    )
     @test length(dataset) == N
 
     figure_1 = plot_instance(b, dataset[1])
@@ -28,7 +35,6 @@
     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_mip ≈ gap_mipl rtol = 1e-2
     @test gap_mip >= gap_local_search
     @test gap_mip >= gap