Initial commit: cuGenOpt GPU optimization solver

benchmark/experiments/e2_vs_routing/routing.py

@@ -0,0 +1,113 @@
+"""
+E2: GenSolver vs 专用求解器 (OR-Tools Routing) — Routing 侧
+
+目的：与 gpu.cu 对比，展示专用求解器的质量优势
+实例：TSP (全部 TSPLIB), VRP (A-n32-k5)
+时间预算：1s, 5s, 10s, 30s, 60s
+
+用法：python routing.py [tsp|vrp|all]
+"""
+import sys
+import os
+import time
+from ortools.constraint_solver import routing_enums_pb2, pywrapcp
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "common"))
+from instances import load_tsp, load_vrp, euc2d_dist_matrix, TSP_INSTANCES, VRP_INSTANCES
+
+TSP_TIME_BUDGETS = [1, 5, 10, 30, 60]
+VRP_TIME_BUDGETS = [1, 5, 10, 30]
+
+
+def solve_tsp_routing(dist, n, time_limit_sec):
+    manager = pywrapcp.RoutingIndexManager(n, 1, 0)
+    routing = pywrapcp.RoutingModel(manager)
+
+    def dist_callback(from_idx, to_idx):
+        return dist[manager.IndexToNode(from_idx)][manager.IndexToNode(to_idx)]
+
+    transit_id = routing.RegisterTransitCallback(dist_callback)
+    routing.SetArcCostEvaluatorOfAllVehicles(transit_id)
+
+    params = pywrapcp.DefaultRoutingSearchParameters()
+    params.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
+    params.local_search_metaheuristic = routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
+    params.time_limit.seconds = time_limit_sec
+
+    t0 = time.perf_counter()
+    solution = routing.SolveWithParameters(params)
+    elapsed_ms = (time.perf_counter() - t0) * 1000.0
+    obj = solution.ObjectiveValue() if solution else float("inf")
+    return obj, elapsed_ms
+
+
+def solve_cvrp_routing(dist, demands, n, n_vehicles, capacity, time_limit_sec):
+    manager = pywrapcp.RoutingIndexManager(n, n_vehicles, 0)
+    routing = pywrapcp.RoutingModel(manager)
+
+    def dist_callback(from_idx, to_idx):
+        return dist[manager.IndexToNode(from_idx)][manager.IndexToNode(to_idx)]
+
+    transit_id = routing.RegisterTransitCallback(dist_callback)
+    routing.SetArcCostEvaluatorOfAllVehicles(transit_id)
+
+    def demand_callback(idx):
+        return demands[manager.IndexToNode(idx)]
+
+    demand_id = routing.RegisterUnaryTransitCallback(demand_callback)
+    routing.AddDimensionWithVehicleCapacity(demand_id, 0, [capacity] * n_vehicles, True, "Cap")
+
+    params = pywrapcp.DefaultRoutingSearchParameters()
+    params.first_solution_strategy = routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
+    params.local_search_metaheuristic = routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
+    params.time_limit.seconds = time_limit_sec
+
+    t0 = time.perf_counter()
+    solution = routing.SolveWithParameters(params)
+    elapsed_ms = (time.perf_counter() - t0) * 1000.0
+    obj = solution.ObjectiveValue() if solution else float("inf")
+    return obj, elapsed_ms
+
+
+def print_row(instance, config, obj, elapsed_ms, optimal):
+    if obj == float("inf"):
+        print(f"{instance},{config},0,inf,0.00,{elapsed_ms:.1f},inf,0,time")
+    else:
+        gap = (obj - optimal) / optimal * 100.0 if optimal > 0 else 0.0
+        print(f"{instance},{config},0,{obj:.2f},0.00,{elapsed_ms:.1f},{gap:.2f},0,time")
+    sys.stdout.flush()
+
+
+def run_tsp():
+    for entry in TSP_INSTANCES:
+        inst = load_tsp(entry)
+        print(f"  [e2-routing] TSP {inst['name']} (n={inst['n']})", file=sys.stderr)
+        dist = euc2d_dist_matrix(inst["coords"])
+        for t in TSP_TIME_BUDGETS:
+            obj, ms = solve_tsp_routing(dist, inst["n"], t)
+            print_row(inst["name"], f"routing_GLS_{t}s", obj, ms, inst["optimal"])
+
+
+def run_vrp():
+    for entry in VRP_INSTANCES:
+        inst = load_vrp(entry)
+        print(f"  [e2-routing] VRP {inst['name']} (n={inst['n']})", file=sys.stderr)
+        dist = euc2d_dist_matrix(inst["coords"])
+        for t in VRP_TIME_BUDGETS:
+            obj, ms = solve_cvrp_routing(
+                dist, inst["demands"], inst["n"],
+                inst["n_vehicles"], inst["capacity"], t)
+            print_row(inst["name"], f"routing_GLS_{t}s", obj, ms, inst["optimal"])
+
+
+def main():
+    print("instance,config,seed,obj,penalty,time_ms,gap_pct,generations,stop_reason")
+    target = sys.argv[1] if len(sys.argv) > 1 else "all"
+    if target in ("all", "tsp"):
+        run_tsp()
+    if target in ("all", "vrp"):
+        run_vrp()
+
+
+if __name__ == "__main__":
+    main()