simple plot tests

Author: BatyLeo

src/DynamicVehicleScheduling/anticipative_solver.jl

@@ -130,44 +130,7 @@ function anticipative_solver(
 
     optimize!(model)
 
-    # Debug infeasibility
-    if termination_status(model) == JuMP.MOI.INFEASIBLE
-        @warn "Model is infeasible! Let's debug..."
-        @info "Number of nodes: $nb_nodes"
-        @info "Number of job indices: $(length(job_indices))"
-        @info "Epoch indices: $epoch_indices"
-        @info "Request epochs: $request_epoch"
-        @info "Start times: $start_time"
-        @info "Service times: $service_time"
-        @info "Epoch duration: $epoch_duration, Δ_dispatch: $Δ_dispatch"
-
-        # Check if any job can be scheduled in any epoch
-        for i in job_indices
-            can_schedule = false
-            for t in epoch_indices
-                # Check release constraint
-                if t >= request_epoch[i]
-                    # Check time limit constraint
-                    travel_time = duration[1, i]
-                    arrival_time = (t - 1) * epoch_duration + Δ_dispatch + travel_time
-                    if arrival_time <= start_time[i]
-                        can_schedule = true
-                        @info "Job $i can be scheduled in epoch $t (arrival: $arrival_time <= deadline: $(start_time[i]))"
-                        break
-                    else
-                        @info "Job $i cannot be scheduled in epoch $t: arrival $arrival_time > deadline $(start_time[i])"
-                    end
-                else
-                    @info "Job $i cannot be scheduled in epoch $t: request epoch $(request_epoch[i]) > epoch $t"
-                end
-            end
-            if !can_schedule
-                @warn "Job $i cannot be scheduled in any epoch! Request epoch: $(request_epoch[i]), start time: $(start_time[i]), travel time: $(duration[1, i]), release index $(customer_index[i])"
-                @warn "  Epochs available: $epoch_indices"
-                @warn "  Epoch duration: $epoch_duration, Δ_dispatch: $Δ_dispatch"
-            end
-        end
-    end
+    @assert termination_status(model) == JuMP.MOI.OPTIMAL "Anticipative MIP did not solve to optimality! (status: $(termination_status(model)))"
     obj = JuMP.objective_value(model)
     epoch_routes = retrieve_routes_anticipative(
         value.(y), env, customer_index, epoch_indices

test/dynamic_vsp_plots.jl

@@ -0,0 +1,35 @@
+@testitem "Dynamic VSP Plots" begin
+    using DecisionFocusedLearningBenchmarks.DynamicVehicleScheduling
+    const DVSP = DecisionFocusedLearningBenchmarks.DynamicVehicleScheduling
+    using Plots
+
+    # Create test benchmark and data (similar to scripts/a.jl)
+    b = DynamicVehicleSchedulingBenchmark(; two_dimensional_features=true)
+    dataset = generate_dataset(b, 3)
+    environments = generate_environments(b, dataset; seed=0)
+    env = environments[1]
+
+    # Test basic plotting functions
+    fig1 = DVSP.plot_instancee(env)
+    @test fig1 isa Plots.Plot
+
+    # Test with anticipative solution and plot_epochs (like in the script)
+    instance = dataset[1].instance
+    scenario = generate_scenario(b, instance; seed=0)
+    v, y = generate_anticipative_solution(b, env, scenario; nb_epochs=3, reset_env=true)
+
+    fig2 = DVSP.plot_epochs(y)
+    @test fig2 isa Plots.Plot
+
+    # Test animation
+    temp_filename = tempname() * ".gif"
+    try
+        anim = DVSP.animate_epochs(y; filename=temp_filename, fps=1)
+        @test anim isa Plots.AnimatedGif || anim isa Plots.Animation
+        @test isfile(temp_filename)
+    finally
+        if isfile(temp_filename)
+            rm(temp_filename)
+        end
+    end
+end