ortools-clone/ortools/constraint_solver/samples/vrp_resources.py

#!/usr/bin/env python3
# Copyright 2010-2025 Google LLC
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# [START program]
"""Vehicles Routing Problem (VRP) with Resource Constraints."""

# [START import]
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

# [END import]


# [START data_model]
def create_data_model():
    """Stores the data for the problem."""
    data = {}
    data["time_matrix"] = [
        [0, 6, 9, 8, 7, 3, 6, 2, 3, 2, 6, 6, 4, 4, 5, 9, 7],
        [6, 0, 8, 3, 2, 6, 8, 4, 8, 8, 13, 7, 5, 8, 12, 10, 14],
        [9, 8, 0, 11, 10, 6, 3, 9, 5, 8, 4, 15, 14, 13, 9, 18, 9],
        [8, 3, 11, 0, 1, 7, 10, 6, 10, 10, 14, 6, 7, 9, 14, 6, 16],
        [7, 2, 10, 1, 0, 6, 9, 4, 8, 9, 13, 4, 6, 8, 12, 8, 14],
        [3, 6, 6, 7, 6, 0, 2, 3, 2, 2, 7, 9, 7, 7, 6, 12, 8],
        [6, 8, 3, 10, 9, 2, 0, 6, 2, 5, 4, 12, 10, 10, 6, 15, 5],
        [2, 4, 9, 6, 4, 3, 6, 0, 4, 4, 8, 5, 4, 3, 7, 8, 10],
        [3, 8, 5, 10, 8, 2, 2, 4, 0, 3, 4, 9, 8, 7, 3, 13, 6],
        [2, 8, 8, 10, 9, 2, 5, 4, 3, 0, 4, 6, 5, 4, 3, 9, 5],
        [6, 13, 4, 14, 13, 7, 4, 8, 4, 4, 0, 10, 9, 8, 4, 13, 4],
        [6, 7, 15, 6, 4, 9, 12, 5, 9, 6, 10, 0, 1, 3, 7, 3, 10],
        [4, 5, 14, 7, 6, 7, 10, 4, 8, 5, 9, 1, 0, 2, 6, 4, 8],
        [4, 8, 13, 9, 8, 7, 10, 3, 7, 4, 8, 3, 2, 0, 4, 5, 6],
        [5, 12, 9, 14, 12, 6, 6, 7, 3, 3, 4, 7, 6, 4, 0, 9, 2],
        [9, 10, 18, 6, 8, 12, 15, 8, 13, 9, 13, 3, 4, 5, 9, 0, 9],
        [7, 14, 9, 16, 14, 8, 5, 10, 6, 5, 4, 10, 8, 6, 2, 9, 0],
    ]
    data["time_windows"] = [
        (0, 5),  # depot
        (7, 12),  # 1
        (10, 15),  # 2
        (5, 14),  # 3
        (5, 13),  # 4
        (0, 5),  # 5
        (5, 10),  # 6
        (0, 10),  # 7
        (5, 10),  # 8
        (0, 5),  # 9
        (10, 16),  # 10
        (10, 15),  # 11
        (0, 5),  # 12
        (5, 10),  # 13
        (7, 12),  # 14
        (10, 15),  # 15
        (5, 15),  # 16
    ]
    data["num_vehicles"] = 4
    # [START resources_data]
    data["vehicle_load_time"] = 5
    data["vehicle_unload_time"] = 5
    data["depot_capacity"] = 2
    # [END resources_data]
    data["depot"] = 0
    return data
    # [END data_model]


# [START solution_printer]
def print_solution(data, manager, routing, solution):
    """Prints solution on console."""
    print(f"Objective: {solution.ObjectiveValue()}")
    time_dimension = routing.GetDimensionOrDie("Time")
    total_time = 0
    for vehicle_id in range(data["num_vehicles"]):
        if not routing.IsVehicleUsed(solution, vehicle_id):
            continue
        index = routing.Start(vehicle_id)
        plan_output = f"Route for vehicle {vehicle_id}:\n"
        while not routing.IsEnd(index):
            time_var = time_dimension.CumulVar(index)
            plan_output += (
                f"{manager.IndexToNode(index)}"
                f" Time({solution.Min(time_var)}, {solution.Max(time_var)})"
                " -> "
            )
            index = solution.Value(routing.NextVar(index))
        time_var = time_dimension.CumulVar(index)
        plan_output += (
            f"{manager.IndexToNode(index)}"
            f" Time({solution.Min(time_var)},{solution.Max(time_var)})\n"
        )
        plan_output += f"Time of the route: {solution.Min(time_var)}min\n"
        print(plan_output)
        total_time += solution.Min(time_var)
    print(f"Total time of all routes: {total_time}min")
    # [END solution_printer]


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

    # Create the routing index manager.
    # [START index_manager]
    manager = pywrapcp.RoutingIndexManager(
        len(data["time_matrix"]), data["num_vehicles"], data["depot"]
    )
    # [END index_manager]

    # Create Routing Model.
    # [START routing_model]
    routing = pywrapcp.RoutingModel(manager)
    # [END routing_model]

    # Create and register a transit callback.
    # [START transit_callback]
    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)
    # [END transit_callback]

    # Define cost of each arc.
    # [START arc_cost]
    routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
    # [END arc_cost]

    # Add Time Windows constraint.
    # [START time_windows_constraint]
    time = "Time"
    routing.AddDimension(
        transit_callback_index,
        60,  # allow waiting time
        60,  # maximum time per vehicle
        False,  # Don't force start cumul to zero.
        time,
    )
    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:
            continue
        index = manager.NodeToIndex(location_idx)
        time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
    # Add time window constraints for each vehicle start node.
    for vehicle_id in range(data["num_vehicles"]):
        index = routing.Start(vehicle_id)
        time_dimension.CumulVar(index).SetRange(
            data["time_windows"][0][0], data["time_windows"][0][1]
        )
    # [END time_windows_constraint]

    # Add resource constraints at the depot.
    # [START depot_load_time]
    solver = routing.solver()
    intervals = []
    for i in range(data["num_vehicles"]):
        # Add time windows at start of routes
        intervals.append(
            solver.FixedDurationIntervalVar(
                time_dimension.CumulVar(routing.Start(i)),
                data["vehicle_load_time"],
                "depot_interval",
            )
        )
        # Add time windows at end of routes.
        intervals.append(
            solver.FixedDurationIntervalVar(
                time_dimension.CumulVar(routing.End(i)),
                data["vehicle_unload_time"],
                "depot_interval",
            )
        )
    # [END depot_load_time]

    # [START depot_capacity]
    depot_usage = [1 for _ in range(len(intervals))]
    solver.Add(
        solver.Cumulative(intervals, depot_usage, data["depot_capacity"], "depot")
    )
    # [END depot_capacity]

    # Instantiate route start and end times to produce feasible times.
    # [START depot_start_end_times]
    for i in range(data["num_vehicles"]):
        routing.AddVariableMinimizedByFinalizer(
            time_dimension.CumulVar(routing.Start(i))
        )
        routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(i)))
    # [END depot_start_end_times]

    # Setting first solution heuristic.
    # [START parameters]
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
    )
    # [END parameters]

    # Solve the problem.
    # [START solve]
    solution = routing.SolveWithParameters(search_parameters)
    # [END solve]

    # Print solution on console.
    # [START print_solution]
    if solution:
        print_solution(data, manager, routing, solution)
    # [END print_solution]
    else:
        print("No solution found !")


if __name__ == "__main__":
    main()
# [END program]