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