105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
"""Simple Travelling Salesperson Problem (TSP) between cities."""
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from ortools.constraint_solver import routing_enums_pb2
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from ortools.constraint_solver import pywrapcp
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import timeit
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def create_data_model():
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"""Stores the data for the problem."""
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data = {}
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data["distance_matrix"] = [
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[8, 8, 15, 7, 11, 17, 8, 3, 4, 11, 9, 6, 2, 7, 15, 6, 16, 5, 9, 14],
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[16, 17, 13, 16, 15, 17, 12, 11, 7, 8, 13, 3, 7, 15, 9, 8, 16, 18, 7, 5],
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[16, 4, 1, 9, 17, 11, 18, 2, 16, 13, 6, 5, 2, 7, 18, 16, 19, 14, 14, 9],
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[9, 9, 7, 12, 16, 12, 3, 5, 16, 18, 10, 7, 6, 12, 4, 5, 14, 11, 5, 5],
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[5, 2, 16, 18, 15, 15, 8, 10, 15, 18, 10, 7, 2, 1, 15, 17, 2, 15, 9, 6],
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[9, 7, 11, 10, 12, 6, 10, 1, 5, 20, 19, 12, 13, 1, 19, 16, 17, 20, 16, 2],
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[15, 7, 3, 15, 20, 5, 10, 4, 20, 13, 5, 11, 20, 16, 13, 4, 7, 11, 13, 17],
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[11, 16, 19, 15, 12, 12, 10, 9, 1, 15, 17, 17, 18, 17, 9, 12, 9, 4, 17, 3],
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[2, 5, 16, 14, 5, 3, 16, 7, 3, 2, 1, 3, 20, 16, 14, 9, 10, 18, 20, 8],
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[9, 4, 20, 2, 11, 19, 5, 3, 11, 2, 13, 20, 17, 5, 12, 17, 17, 9, 5, 4],
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[5, 14, 7, 14, 3, 13, 1, 13, 17, 6, 18, 18, 16, 15, 18, 19, 15, 12, 9, 4],
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[14, 2, 17, 8, 11, 16, 7, 12, 12, 6, 13, 12, 6, 18, 3, 7, 3, 1, 7, 17],
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[8, 10, 8, 13, 15, 20, 2, 11, 9, 1, 4, 14, 1, 19, 15, 13, 18, 16, 10, 2],
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[16, 2, 14, 3, 11, 17, 7, 6, 4, 2, 3, 8, 20, 8, 7, 12, 1, 4, 20, 10],
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[5, 19, 10, 18, 6, 18, 2, 11, 6, 18, 11, 18, 13, 7, 18, 15, 3, 10, 20, 7],
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[16, 19, 12, 19, 15, 12, 14, 8, 7, 11, 4, 13, 19, 12, 14, 13, 3, 7, 8, 20],
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[4, 4, 15, 5, 16, 14, 18, 11, 18, 20, 19, 18, 18, 4, 3, 20, 5, 18, 18, 1],
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[19, 6, 2, 15, 20, 3, 13, 7, 10, 17, 12, 11, 10, 19, 6, 2, 2, 19, 15, 1],
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[16, 16, 3, 7, 6, 12, 6, 3, 2, 1, 11, 8, 13, 16, 14, 5, 20, 20, 10, 3],
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[3, 14, 13, 20, 20, 2, 7, 14, 2, 6, 2, 19, 20, 20, 14, 7, 7, 5, 15, 18],
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]
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data["num_vehicles"] = 1
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data["depot"] = 0
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return data
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def print_solution(manager, routing, solution):
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"""Prints solution on console."""
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print(f"Objective: {solution.ObjectiveValue()} miles")
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index = routing.Start(0)
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plan_output = "Route for vehicle 0:\n"
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route_distance = 0
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while not routing.IsEnd(index):
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plan_output += f" {manager.IndexToNode(index) + 1} ->"
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previous_index = index
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index = solution.Value(routing.NextVar(index))
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route_distance += routing.GetArcCostForVehicle(previous_index, index, 0)
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plan_output += f" {manager.IndexToNode(index)}\n"
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plan_output += f"Route distance: {route_distance} miles\n"
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print(plan_output)
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def main():
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"""Entry point of the program."""
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# Instantiate the data problem.
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data = create_data_model()
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# Create the routing index manager.
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manager = pywrapcp.RoutingIndexManager(
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len(data["distance_matrix"]), data["num_vehicles"], data["depot"]
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)
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# Create Routing Model.
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routing = pywrapcp.RoutingModel(manager)
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def distance_callback(from_index, to_index):
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"""Returns the distance between the two nodes."""
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# Convert from routing variable Index to distance matrix NodeIndex.
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from_node = manager.IndexToNode(from_index)
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to_node = manager.IndexToNode(to_index)
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return data["distance_matrix"][from_node][to_node]
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transit_callback_index = routing.RegisterTransitCallback(distance_callback)
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# Define cost of each arc.
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routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
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# Setting first solution heuristic.
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# Local Optimum
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# search_parameters = pywrapcp.DefaultRoutingSearchParameters()
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# search_parameters.first_solution_strategy = (
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# routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
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# )
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# Global Optimum
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search_parameters = pywrapcp.DefaultRoutingSearchParameters()
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search_parameters.local_search_metaheuristic = (
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routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
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search_parameters.time_limit.seconds = 30
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search_parameters.log_search = True
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# Solve the problem.
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solution = routing.SolveWithParameters(search_parameters)
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# Print solution on console.
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if solution:
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print_solution(manager, routing, solution)
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if __name__ == "__main__":
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start = timeit.default_timer()
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main()
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stop = timeit.default_timer()
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print('Time: ', stop - start)
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