Or-tools: Pickup and delivery with Time Windows (PDPTW) with custom start/end locations - python

Created on 18 Sep 2019 · 2Comments · Source: google/or-tools

Hello I'm trying to setup my own pickup and delivery problem with time windows and custom start/end locations using python (3.6) and ortools (Version 7) library. In the below example I have a PDP where I set time windows and start/end locations. The below example will not run - it crashes and I've found some issues. During debugging I've noticed that when I go to set my time_window constraints NodeToIndex(location_idx) returns -1 . Is there any reason I should even use NodeToIndex in this case? Why Can't I just use location_idx? If I instead use location_idx to set the CumulVar then the code proceeds with time windows set however then fails when going to setup the "pickups_deliveries". I don't think it likes that I'm setting up delivery at an end node. Why is that the case? Can someone recommend how to do this?

Essentially from a high level I want the find the route for a driver at any start point I give it, to go to the depot and pick up 2+ packages and deliver them at different locations.

from __future__ import print_function
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver.pywrapcp import RoutingIndexManager, RoutingModel


def create_data_model():
    """Stores the data for the problem."""
    data = {}

    data['time_matrix'] = [
        #Dr #De #C1 #C2
        [0, 5, 3, 10],  # Driver
        [5, 0, 4, 8],  # Depot
        [3, 4, 0, 4],  # Customer 1
        [10, 8, 4, 0]  # Customer 2
    ]

    data['pickups_deliveries'] = [
        [1, 2],
        [1, 3]
    ]

    data['time_windows'] = [
        (0, 0), # driver
        (6, 6), # depot
        (10, 12), # c1
        (12, 16) # c2
    ]

    data['starts'] = [0]
    data['ends'] = [3]
    data['num_vehicles'] = 1
    return data


# todo replace with time one
def print_solution(data, manager: RoutingIndexManager, routing: RoutingModel, assignment):
    """Prints assignment on console."""
    time_dimension = routing.GetDimensionOrDie('Time')
    total_time = 0
    for vehicle_id in range(data['num_vehicles']):

        # Model inspection. Returns the variable index of the starting node of a vehicle route.
        index = routing.Start(vehicle_id)
        plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
        while not routing.IsEnd(index):
            time_var = time_dimension.CumulVar(index)
            plan_output += '{0} Time({1},{2}) -> '.format(
                manager.IndexToNode(index), assignment.Min(time_var),
                assignment.Max(time_var))
            index = assignment.Value(routing.NextVar(index))

        # handles the end location
        time_var = time_dimension.CumulVar(index)
        plan_output += '{0} Time({1},{2})\n'.format(
            manager.IndexToNode(index), assignment.Min(time_var),
            assignment.Max(time_var))
        plan_output += 'Time of the route: {}min\n'.format(
            assignment.Min(time_var))
        print(plan_output)
        total_time += assignment.Min(time_var)
    print('Total time of all routes: {}min'.format(total_time))


def main():
    """Solve the VRP with time windows."""
    # Instantiate the data problem.
    data = create_data_model()

    # Create the routing index manager.
    manager = pywrapcp.RoutingIndexManager(
        len(data['time_matrix']), data['num_vehicles'], data['starts'],
        data['ends'])

    # Create Routing Model.
    routing = pywrapcp.RoutingModel(manager)

    def time_callback(from_index, to_index):
        """Returns the travel time between the two nodes."""
        # Convert from routing variable Index to time matrix NodeIndex.
        from_node = manager.IndexToNode(from_index)
        to_node = manager.IndexToNode(to_index)
        return data['time_matrix'][from_node][to_node]

    transit_callback_index = routing.RegisterTransitCallback(time_callback)
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)

    # The code creates a dimension for the travel time of the vehicles, similar to the dimensions
    # for travel distance or demands in previous examples. Dimensions keep track of quantities that
    # accumulate over a vehicle's route. In the code above, time_dimension.CumulVar(index) is the
    # cumulative travel time when a vehicle arrives at the location with the given index.
    time = 'Time'
    routing.AddDimension(
        transit_callback_index,
        30,  # allow waiting time
        30,  # maximum time per vehicle
        False,  # Don't force start cumul to zero.
        time)
    # Returns a dimension from its name. Dies if the dimension does not exist.
    time_dimension = routing.GetDimensionOrDie(time)

    # Add time window constraints for each location except depot.
    for location_idx, time_window in enumerate(data['time_windows']):
        if location_idx == 0:  # idx 0 and 1 are the driver and customer
            continue

        # NodeToIndex returns -1 for end nodes
        index = manager.NodeToIndex(location_idx)
        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])

    # Not sure if I need this? Removed for now
    # Add time window constraints for each vehicle start node.
    # for vehicle_id in range(data['num_vehicles']):
    #     # Model inspection. Returns the variable index of the starting node of a vehicle route.
    #     start_index = routing.Start(vehicle_id)
    #     time_dimension.CumulVar(start_index).SetRange(data['time_windows'][0][0],
    #                                                   data['time_windows'][0][1])

    for i in range(data['num_vehicles']):
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.Start(i)))
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.End(i)))

    # Define Transportation Requests.
    for request in data['pickups_deliveries']:
        pickup_index = manager.NodeToIndex(request[0])
        delivery_index = manager.NodeToIndex(request[1])
        routing.AddPickupAndDelivery(pickup_index, delivery_index)
        routing.solver().Add(
            routing.VehicleVar(pickup_index) == routing.VehicleVar(
                delivery_index))
        routing.solver().Add(
            time_dimension.CumulVar(pickup_index) <=
            time_dimension.CumulVar(delivery_index))

    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.time_limit.seconds = 30
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
    assignment = routing.SolveWithParameters(search_parameters)

    if assignment:
        print_solution(data, manager, routing, assignment)


if __name__ == '__main__':
    main()

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