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