容量限制

车辆故障路由 (CVRP) 是一种 VRP,车辆承载能力有限,需要在各个地点取货或交货。这些物品具有数量(例如重量或体积),而车辆具有的最大容量。问题在于以最低的费用自提或配送商品,同时不超过车辆的容量。

在以下示例中,我们假定提取了所有商品。如果所有项目都在投递,在这种情况下,解决此问题的节目也可以正常运行:在本例中,您可以考虑在车辆完全离开仓库时应用容量限制。但在这两种情况下,容量限制的实现方式相同。

CVRP 示例

接下来,我们将介绍具有容量限制的 VRP 示例。该示例对上述 VRP 示例进行了扩展,并添加了以下要求。在每个位置,都有与要提货的商品数量相对应的需求。此外,每辆车的最大容量为 15。(我们不会指定需求或容量的单位)。

下面的网格以蓝色显示要到访的地点,以黑色显示公司地点。需求会显示在每个地理位置的右下角。如需详细了解如何定义位置,请参阅 VRP 部分中的位置坐标

问题在于,需要找到分配给总距离最短的车辆的路线,使车辆的总承载量绝不会超出其容量。

使用 OR 工具解析 CVRP 示例

以下各部分介绍了如何使用 OR 工具解决 CVRP 示例。

创建数据

此示例中的数据包括上一个 VRP 示例中的数据,并添加以下需求和车辆容量:

Python

data["demands"] = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]
data["vehicle_capacities"] = [15, 15, 15, 15]

C++

const std::vector<int64_t> demands{
    0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8,
};
const std::vector<int64_t> vehicle_capacities{15, 15, 15, 15};

Java

public final long[] demands = {0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8};
public final long[] vehicleCapacities = {15, 15, 15, 15};

C#

public long[] Demands = { 0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8 };
public long[] VehicleCapacities = { 15, 15, 15, 15 };

这些数据中的新项包括:

  • 需求:每个地理位置都有与要提货的商品数量(例如重量或体积)对应的需求
  • 容量:每辆车都有一个容量,即车辆可以容纳的最大数量。当车辆沿路线行驶时,携带的商品的总数量绝不会超出其容量。

添加距离回调

距离回调(返回任意两个位置之间的距离的函数)的定义与上述 VRP 示例相同。

添加需求回调和容量限制

除了距离回调之外,该求解器还需要一个需求回调(用于返回每个位置的需求)以及一个容量限制维度。以下代码会创建这些内容。

Python

def demand_callback(from_index):
    """Returns the demand of the node."""
    # Convert from routing variable Index to demands NodeIndex.
    from_node = manager.IndexToNode(from_index)
    return data["demands"][from_node]

demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)
routing.AddDimensionWithVehicleCapacity(
    demand_callback_index,
    0,  # null capacity slack
    data["vehicle_capacities"],  # vehicle maximum capacities
    True,  # start cumul to zero
    "Capacity",
)

C++

const int demand_callback_index = routing.RegisterUnaryTransitCallback(
    [&data, &manager](const int64_t from_index) -> int64_t {
      // Convert from routing variable Index to demand NodeIndex.
      const int from_node = manager.IndexToNode(from_index).value();
      return data.demands[from_node];
    });
routing.AddDimensionWithVehicleCapacity(
    demand_callback_index,    // transit callback index
    int64_t{0},               // null capacity slack
    data.vehicle_capacities,  // vehicle maximum capacities
    true,                     // start cumul to zero
    "Capacity");

Java

final int demandCallbackIndex = routing.registerUnaryTransitCallback((long fromIndex) -> {
  // Convert from routing variable Index to user NodeIndex.
  int fromNode = manager.indexToNode(fromIndex);
  return data.demands[fromNode];
});
routing.addDimensionWithVehicleCapacity(demandCallbackIndex, 0, // null capacity slack
    data.vehicleCapacities, // vehicle maximum capacities
    true, // start cumul to zero
    "Capacity");

C#

int demandCallbackIndex = routing.RegisterUnaryTransitCallback((long fromIndex) =>
                                                               {
                                                                   // Convert from routing variable Index to
                                                                   // demand NodeIndex.
                                                                   var fromNode =
                                                                       manager.IndexToNode(fromIndex);
                                                                   return data.Demands[fromNode];
                                                               });
routing.AddDimensionWithVehicleCapacity(demandCallbackIndex, 0, // null capacity slack
                                        data.VehicleCapacities, // vehicle maximum capacities
                                        true,                   // start cumul to zero
                                        "Capacity");

与将一对位置作为输入而执行的距离回调不同,需求回调仅依赖于交付的位置 (from_node)。

由于容量限制涉及车辆携带的负载(即车辆沿途累积的重量),因此我们需要为容量创建一个维度,类似于上一个 VRP 示例中的距离维度。

在本例中,我们使用 AddDimensionWithVehicleCapacity 方法,该方法采用容量矢量。

由于此示例中的所有车辆容量都相同,因此您可以使用 AddDimension 方法,该方法为所有车辆数量设置一个上限。但 AddDimensionWithVehicleCapacity 能处理更常见的情况,即不同车辆具有不同的容量。

多种货运类型和容量的问题

在更复杂的 CVRP 中,每辆车可能搭载多种不同类型的货物,每种类型的车辆都有最大容量。例如,燃料运输卡车可能使用多种容量各异的油箱运输多种燃料。为了处理此类问题,只需为每种货运类型创建不同的容量回调和维度(请务必为它们指定唯一名称)。

添加解决方案打印机

解决方案打印机会显示每辆车的路线及其累计负载,即车辆在路线上停止时搭载的总金额。

Python

def print_solution(data, manager, routing, solution):
    """Prints solution on console."""
    print(f"Objective: {solution.ObjectiveValue()}")
    total_distance = 0
    total_load = 0
    for vehicle_id in range(data["num_vehicles"]):
        index = routing.Start(vehicle_id)
        plan_output = f"Route for vehicle {vehicle_id}:\n"
        route_distance = 0
        route_load = 0
        while not routing.IsEnd(index):
            node_index = manager.IndexToNode(index)
            route_load += data["demands"][node_index]
            plan_output += f" {node_index} Load({route_load}) -> "
            previous_index = index
            index = solution.Value(routing.NextVar(index))
            route_distance += routing.GetArcCostForVehicle(
                previous_index, index, vehicle_id
            )
        plan_output += f" {manager.IndexToNode(index)} Load({route_load})\n"
        plan_output += f"Distance of the route: {route_distance}m\n"
        plan_output += f"Load of the route: {route_load}\n"
        print(plan_output)
        total_distance += route_distance
        total_load += route_load
    print(f"Total distance of all routes: {total_distance}m")
    print(f"Total load of all routes: {total_load}")

C++

//! @brief Print the solution.
//! @param[in] data Data of the problem.
//! @param[in] manager Index manager used.
//! @param[in] routing Routing solver used.
//! @param[in] solution Solution found by the solver.
void PrintSolution(const DataModel& data, const RoutingIndexManager& manager,
                   const RoutingModel& routing, const Assignment& solution) {
  int64_t total_distance = 0;
  int64_t total_load = 0;
  for (int vehicle_id = 0; vehicle_id < data.num_vehicles; ++vehicle_id) {
    int64_t index = routing.Start(vehicle_id);
    LOG(INFO) << "Route for Vehicle " << vehicle_id << ":";
    int64_t route_distance = 0;
    int64_t route_load = 0;
    std::stringstream route;
    while (!routing.IsEnd(index)) {
      const int node_index = manager.IndexToNode(index).value();
      route_load += data.demands[node_index];
      route << node_index << " Load(" << route_load << ") -> ";
      const int64_t previous_index = index;
      index = solution.Value(routing.NextVar(index));
      route_distance += routing.GetArcCostForVehicle(previous_index, index,
                                                     int64_t{vehicle_id});
    }
    LOG(INFO) << route.str() << manager.IndexToNode(index).value();
    LOG(INFO) << "Distance of the route: " << route_distance << "m";
    LOG(INFO) << "Load of the route: " << route_load;
    total_distance += route_distance;
    total_load += route_load;
  }
  LOG(INFO) << "Total distance of all routes: " << total_distance << "m";
  LOG(INFO) << "Total load of all routes: " << total_load;
  LOG(INFO) << "";
  LOG(INFO) << "Advanced usage:";
  LOG(INFO) << "Problem solved in " << routing.solver()->wall_time() << "ms";
}

Java

/// @brief Print the solution.
static void printSolution(
    DataModel data, RoutingModel routing, RoutingIndexManager manager, Assignment solution) {
  // Solution cost.
  logger.info("Objective: " + solution.objectiveValue());
  // Inspect solution.
  long totalDistance = 0;
  long totalLoad = 0;
  for (int i = 0; i < data.vehicleNumber; ++i) {
    long index = routing.start(i);
    logger.info("Route for Vehicle " + i + ":");
    long routeDistance = 0;
    long routeLoad = 0;
    String route = "";
    while (!routing.isEnd(index)) {
      long nodeIndex = manager.indexToNode(index);
      routeLoad += data.demands[(int) nodeIndex];
      route += nodeIndex + " Load(" + routeLoad + ") -> ";
      long previousIndex = index;
      index = solution.value(routing.nextVar(index));
      routeDistance += routing.getArcCostForVehicle(previousIndex, index, i);
    }
    route += manager.indexToNode(routing.end(i));
    logger.info(route);
    logger.info("Distance of the route: " + routeDistance + "m");
    totalDistance += routeDistance;
    totalLoad += routeLoad;
  }
  logger.info("Total distance of all routes: " + totalDistance + "m");
  logger.info("Total load of all routes: " + totalLoad);
}

C#

/// <summary>
///   Print the solution.
/// </summary>
static void PrintSolution(in DataModel data, in RoutingModel routing, in RoutingIndexManager manager,
                          in Assignment solution)
{
    Console.WriteLine($"Objective {solution.ObjectiveValue()}:");

    // Inspect solution.
    long totalDistance = 0;
    long totalLoad = 0;
    for (int i = 0; i < data.VehicleNumber; ++i)
    {
        Console.WriteLine("Route for Vehicle {0}:", i);
        long routeDistance = 0;
        long routeLoad = 0;
        var index = routing.Start(i);
        while (routing.IsEnd(index) == false)
        {
            long nodeIndex = manager.IndexToNode(index);
            routeLoad += data.Demands[nodeIndex];
            Console.Write("{0} Load({1}) -> ", nodeIndex, routeLoad);
            var previousIndex = index;
            index = solution.Value(routing.NextVar(index));
            routeDistance += routing.GetArcCostForVehicle(previousIndex, index, 0);
        }
        Console.WriteLine("{0}", manager.IndexToNode((int)index));
        Console.WriteLine("Distance of the route: {0}m", routeDistance);
        totalDistance += routeDistance;
        totalLoad += routeLoad;
    }
    Console.WriteLine("Total distance of all routes: {0}m", totalDistance);
    Console.WriteLine("Total load of all routes: {0}m", totalLoad);
}

主函数

本示例中的主要函数与 TSP 示例中的函数非常相似,但还添加了上述需求和容量维度

运行程序

完整的程序如下一部分所示。运行该程序后,它会显示以下输出:

Objective: 6208
Route for vehicle 0:
 0 Load(0) ->  4 Load(0) ->  3 Load(4) ->  1 Load(6) ->  7 Load(7) ->  0 Load(15)
Distance of the route: 1552m
Load of the route: 15

Route for vehicle 1:
 0 Load(0) ->  14 Load(0) ->  16 Load(4) ->  10 Load(12) ->  9 Load(14) ->  0 Load(15)
Distance of the route: 1552m
Load of the route: 15

Route for vehicle 2:
 0 Load(0) ->  12 Load(0) ->  11 Load(2) ->  15 Load(3) ->  13 Load(11) ->  0 Load(15)
Distance of the route: 1552m
Load of the route: 15

Route for vehicle 3:
 0 Load(0) ->  8 Load(0) ->  2 Load(8) ->  6 Load(9) ->  5 Load(13) ->  0 Load(15)
Distance of the route: 1552m
Load of the route: 15

Total Distance of all routes: 6208m
Total Load of all routes: 60

对于路线中的每个位置,输出都会显示:

  • 位置的索引。
  • 车辆在离开相应位置时承载的总负载。

  • 路由如下所示。

完成计划

遇到的车辆路线问题的完整程序如下所示。

Python

"""Capacited Vehicles Routing Problem (CVRP)."""

from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp


def create_data_model():
    """Stores the data for the problem."""
    data = {}
    data["distance_matrix"] = [
        # fmt: off
      [0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662],
      [548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210],
      [776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754],
      [696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358],
      [582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244],
      [274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708],
      [502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480],
      [194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856],
      [308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514],
      [194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468],
      [536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354],
      [502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844],
      [388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730],
      [354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536],
      [468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194],
      [776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798],
      [662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0],
        # fmt: on
    ]
    data["demands"] = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]
    data["vehicle_capacities"] = [15, 15, 15, 15]
    data["num_vehicles"] = 4
    data["depot"] = 0
    return data


def print_solution(data, manager, routing, solution):
    """Prints solution on console."""
    print(f"Objective: {solution.ObjectiveValue()}")
    total_distance = 0
    total_load = 0
    for vehicle_id in range(data["num_vehicles"]):
        index = routing.Start(vehicle_id)
        plan_output = f"Route for vehicle {vehicle_id}:\n"
        route_distance = 0
        route_load = 0
        while not routing.IsEnd(index):
            node_index = manager.IndexToNode(index)
            route_load += data["demands"][node_index]
            plan_output += f" {node_index} Load({route_load}) -> "
            previous_index = index
            index = solution.Value(routing.NextVar(index))
            route_distance += routing.GetArcCostForVehicle(
                previous_index, index, vehicle_id
            )
        plan_output += f" {manager.IndexToNode(index)} Load({route_load})\n"
        plan_output += f"Distance of the route: {route_distance}m\n"
        plan_output += f"Load of the route: {route_load}\n"
        print(plan_output)
        total_distance += route_distance
        total_load += route_load
    print(f"Total distance of all routes: {total_distance}m")
    print(f"Total load of all routes: {total_load}")


def main():
    """Solve the CVRP problem."""
    # 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)

    # Create and register a transit callback.
    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)

    # Add Capacity constraint.
    def demand_callback(from_index):
        """Returns the demand of the node."""
        # Convert from routing variable Index to demands NodeIndex.
        from_node = manager.IndexToNode(from_index)
        return data["demands"][from_node]

    demand_callback_index = routing.RegisterUnaryTransitCallback(demand_callback)
    routing.AddDimensionWithVehicleCapacity(
        demand_callback_index,
        0,  # null capacity slack
        data["vehicle_capacities"],  # vehicle maximum capacities
        True,  # start cumul to zero
        "Capacity",
    )

    # Setting first solution heuristic.
    search_parameters = pywrapcp.DefaultRoutingSearchParameters()
    search_parameters.first_solution_strategy = (
        routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC
    )
    search_parameters.local_search_metaheuristic = (
        routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
    )
    search_parameters.time_limit.FromSeconds(1)

    # Solve the problem.
    solution = routing.SolveWithParameters(search_parameters)

    # Print solution on console.
    if solution:
        print_solution(data, manager, routing, solution)


if __name__ == "__main__":
    main()

C++

#include <cstdint>
#include <sstream>
#include <vector>

#include "google/protobuf/duration.pb.h"
#include "ortools/constraint_solver/routing.h"
#include "ortools/constraint_solver/routing_enums.pb.h"
#include "ortools/constraint_solver/routing_index_manager.h"
#include "ortools/constraint_solver/routing_parameters.h"

namespace operations_research {
struct DataModel {
  const std::vector<std::vector<int64_t>> distance_matrix{
      {0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468,
       776, 662},
      {548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,
       1016, 868, 1210},
      {776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130,
       788, 1552, 754},
      {696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,
       1164, 560, 1358},
      {582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,
       1050, 674, 1244},
      {274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514,
       1050, 708},
      {502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514,
       1278, 480},
      {194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662,
       742, 856},
      {308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320,
       1084, 514},
      {194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274,
       810, 468},
      {536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730,
       388, 1152, 354},
      {502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308,
       650, 274, 844},
      {388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536,
       388, 730},
      {354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342,
       422, 536},
      {468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342,
       0, 764, 194},
      {776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388,
       422, 764, 0, 798},
      {662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536,
       194, 798, 0},
  };
  const std::vector<int64_t> demands{
      0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8,
  };
  const std::vector<int64_t> vehicle_capacities{15, 15, 15, 15};
  const int num_vehicles = 4;
  const RoutingIndexManager::NodeIndex depot{0};
};

//! @brief Print the solution.
//! @param[in] data Data of the problem.
//! @param[in] manager Index manager used.
//! @param[in] routing Routing solver used.
//! @param[in] solution Solution found by the solver.
void PrintSolution(const DataModel& data, const RoutingIndexManager& manager,
                   const RoutingModel& routing, const Assignment& solution) {
  int64_t total_distance = 0;
  int64_t total_load = 0;
  for (int vehicle_id = 0; vehicle_id < data.num_vehicles; ++vehicle_id) {
    int64_t index = routing.Start(vehicle_id);
    LOG(INFO) << "Route for Vehicle " << vehicle_id << ":";
    int64_t route_distance = 0;
    int64_t route_load = 0;
    std::stringstream route;
    while (!routing.IsEnd(index)) {
      const int node_index = manager.IndexToNode(index).value();
      route_load += data.demands[node_index];
      route << node_index << " Load(" << route_load << ") -> ";
      const int64_t previous_index = index;
      index = solution.Value(routing.NextVar(index));
      route_distance += routing.GetArcCostForVehicle(previous_index, index,
                                                     int64_t{vehicle_id});
    }
    LOG(INFO) << route.str() << manager.IndexToNode(index).value();
    LOG(INFO) << "Distance of the route: " << route_distance << "m";
    LOG(INFO) << "Load of the route: " << route_load;
    total_distance += route_distance;
    total_load += route_load;
  }
  LOG(INFO) << "Total distance of all routes: " << total_distance << "m";
  LOG(INFO) << "Total load of all routes: " << total_load;
  LOG(INFO) << "";
  LOG(INFO) << "Advanced usage:";
  LOG(INFO) << "Problem solved in " << routing.solver()->wall_time() << "ms";
}

void VrpCapacity() {
  // Instantiate the data problem.
  DataModel data;

  // Create Routing Index Manager
  RoutingIndexManager manager(data.distance_matrix.size(), data.num_vehicles,
                              data.depot);

  // Create Routing Model.
  RoutingModel routing(manager);

  // Create and register a transit callback.
  const int transit_callback_index = routing.RegisterTransitCallback(
      [&data, &manager](const int64_t from_index,
                        const int64_t to_index) -> int64_t {
        // Convert from routing variable Index to distance matrix NodeIndex.
        const int from_node = manager.IndexToNode(from_index).value();
        const int to_node = manager.IndexToNode(to_index).value();
        return data.distance_matrix[from_node][to_node];
      });

  // Define cost of each arc.
  routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index);

  // Add Capacity constraint.
  const int demand_callback_index = routing.RegisterUnaryTransitCallback(
      [&data, &manager](const int64_t from_index) -> int64_t {
        // Convert from routing variable Index to demand NodeIndex.
        const int from_node = manager.IndexToNode(from_index).value();
        return data.demands[from_node];
      });
  routing.AddDimensionWithVehicleCapacity(
      demand_callback_index,    // transit callback index
      int64_t{0},               // null capacity slack
      data.vehicle_capacities,  // vehicle maximum capacities
      true,                     // start cumul to zero
      "Capacity");

  // Setting first solution heuristic.
  RoutingSearchParameters search_parameters = DefaultRoutingSearchParameters();
  search_parameters.set_first_solution_strategy(
      FirstSolutionStrategy::PATH_CHEAPEST_ARC);
  search_parameters.set_local_search_metaheuristic(
      LocalSearchMetaheuristic::GUIDED_LOCAL_SEARCH);
  search_parameters.mutable_time_limit()->set_seconds(1);

  // Solve the problem.
  const Assignment* solution = routing.SolveWithParameters(search_parameters);

  // Print solution on console.
  PrintSolution(data, manager, routing, *solution);
}
}  // namespace operations_research

int main(int /*argc*/, char* /*argv*/[]) {
  operations_research::VrpCapacity();
  return EXIT_SUCCESS;
}

Java

package com.google.ortools.constraintsolver.samples;
import com.google.ortools.Loader;
import com.google.ortools.constraintsolver.Assignment;
import com.google.ortools.constraintsolver.FirstSolutionStrategy;
import com.google.ortools.constraintsolver.LocalSearchMetaheuristic;
import com.google.ortools.constraintsolver.RoutingIndexManager;
import com.google.ortools.constraintsolver.RoutingModel;
import com.google.ortools.constraintsolver.RoutingSearchParameters;
import com.google.ortools.constraintsolver.main;
import com.google.protobuf.Duration;
import java.util.logging.Logger;

/** Minimal VRP. */
public final class VrpCapacity {
  private static final Logger logger = Logger.getLogger(VrpCapacity.class.getName());

  static class DataModel {
    public final long[][] distanceMatrix = {
        {0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662},
        {548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210},
        {776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754},
        {696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358},
        {582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244},
        {274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708},
        {502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480},
        {194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856},
        {308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514},
        {194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468},
        {536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354},
        {502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844},
        {388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730},
        {354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536},
        {468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194},
        {776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798},
        {662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0},
    };
    public final long[] demands = {0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8};
    public final long[] vehicleCapacities = {15, 15, 15, 15};
    public final int vehicleNumber = 4;
    public final int depot = 0;
  }

  /// @brief Print the solution.
  static void printSolution(
      DataModel data, RoutingModel routing, RoutingIndexManager manager, Assignment solution) {
    // Solution cost.
    logger.info("Objective: " + solution.objectiveValue());
    // Inspect solution.
    long totalDistance = 0;
    long totalLoad = 0;
    for (int i = 0; i < data.vehicleNumber; ++i) {
      long index = routing.start(i);
      logger.info("Route for Vehicle " + i + ":");
      long routeDistance = 0;
      long routeLoad = 0;
      String route = "";
      while (!routing.isEnd(index)) {
        long nodeIndex = manager.indexToNode(index);
        routeLoad += data.demands[(int) nodeIndex];
        route += nodeIndex + " Load(" + routeLoad + ") -> ";
        long previousIndex = index;
        index = solution.value(routing.nextVar(index));
        routeDistance += routing.getArcCostForVehicle(previousIndex, index, i);
      }
      route += manager.indexToNode(routing.end(i));
      logger.info(route);
      logger.info("Distance of the route: " + routeDistance + "m");
      totalDistance += routeDistance;
      totalLoad += routeLoad;
    }
    logger.info("Total distance of all routes: " + totalDistance + "m");
    logger.info("Total load of all routes: " + totalLoad);
  }

  public static void main(String[] args) throws Exception {
    Loader.loadNativeLibraries();
    // Instantiate the data problem.
    final DataModel data = new DataModel();

    // Create Routing Index Manager
    RoutingIndexManager manager =
        new RoutingIndexManager(data.distanceMatrix.length, data.vehicleNumber, data.depot);

    // Create Routing Model.
    RoutingModel routing = new RoutingModel(manager);

    // Create and register a transit callback.
    final int transitCallbackIndex =
        routing.registerTransitCallback((long fromIndex, long toIndex) -> {
          // Convert from routing variable Index to user NodeIndex.
          int fromNode = manager.indexToNode(fromIndex);
          int toNode = manager.indexToNode(toIndex);
          return data.distanceMatrix[fromNode][toNode];
        });

    // Define cost of each arc.
    routing.setArcCostEvaluatorOfAllVehicles(transitCallbackIndex);

    // Add Capacity constraint.
    final int demandCallbackIndex = routing.registerUnaryTransitCallback((long fromIndex) -> {
      // Convert from routing variable Index to user NodeIndex.
      int fromNode = manager.indexToNode(fromIndex);
      return data.demands[fromNode];
    });
    routing.addDimensionWithVehicleCapacity(demandCallbackIndex, 0, // null capacity slack
        data.vehicleCapacities, // vehicle maximum capacities
        true, // start cumul to zero
        "Capacity");

    // Setting first solution heuristic.
    RoutingSearchParameters searchParameters =
        main.defaultRoutingSearchParameters()
            .toBuilder()
            .setFirstSolutionStrategy(FirstSolutionStrategy.Value.PATH_CHEAPEST_ARC)
            .setLocalSearchMetaheuristic(LocalSearchMetaheuristic.Value.GUIDED_LOCAL_SEARCH)
            .setTimeLimit(Duration.newBuilder().setSeconds(1).build())
            .build();

    // Solve the problem.
    Assignment solution = routing.solveWithParameters(searchParameters);

    // Print solution on console.
    printSolution(data, routing, manager, solution);
  }

  private VrpCapacity() {}
}

C#

using System;
using System.Collections.Generic;
using Google.OrTools.ConstraintSolver;
using Google.Protobuf.WellKnownTypes; // Duration

/// <summary>
///   Minimal TSP using distance matrix.
/// </summary>
public class VrpCapacity
{
    class DataModel
    {
        public long[,] DistanceMatrix = {
            { 0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354, 468, 776, 662 },
            { 548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674, 1016, 868, 1210 },
            { 776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164, 1130, 788, 1552, 754 },
            { 696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822, 1164, 560, 1358 },
            { 582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708, 1050, 674, 1244 },
            { 274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628, 514, 1050, 708 },
            { 502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856, 514, 1278, 480 },
            { 194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320, 662, 742, 856 },
            { 308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662, 320, 1084, 514 },
            { 194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388, 274, 810, 468 },
            { 536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764, 730, 388, 1152, 354 },
            { 502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114, 308, 650, 274, 844 },
            { 388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194, 536, 388, 730 },
            { 354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0, 342, 422, 536 },
            { 468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536, 342, 0, 764, 194 },
            { 776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274, 388, 422, 764, 0, 798 },
            { 662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730, 536, 194, 798, 0 }
        };
        public long[] Demands = { 0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8 };
        public long[] VehicleCapacities = { 15, 15, 15, 15 };
        public int VehicleNumber = 4;
        public int Depot = 0;
    };

    /// <summary>
    ///   Print the solution.
    /// </summary>
    static void PrintSolution(in DataModel data, in RoutingModel routing, in RoutingIndexManager manager,
                              in Assignment solution)
    {
        Console.WriteLine($"Objective {solution.ObjectiveValue()}:");

        // Inspect solution.
        long totalDistance = 0;
        long totalLoad = 0;
        for (int i = 0; i < data.VehicleNumber; ++i)
        {
            Console.WriteLine("Route for Vehicle {0}:", i);
            long routeDistance = 0;
            long routeLoad = 0;
            var index = routing.Start(i);
            while (routing.IsEnd(index) == false)
            {
                long nodeIndex = manager.IndexToNode(index);
                routeLoad += data.Demands[nodeIndex];
                Console.Write("{0} Load({1}) -> ", nodeIndex, routeLoad);
                var previousIndex = index;
                index = solution.Value(routing.NextVar(index));
                routeDistance += routing.GetArcCostForVehicle(previousIndex, index, 0);
            }
            Console.WriteLine("{0}", manager.IndexToNode((int)index));
            Console.WriteLine("Distance of the route: {0}m", routeDistance);
            totalDistance += routeDistance;
            totalLoad += routeLoad;
        }
        Console.WriteLine("Total distance of all routes: {0}m", totalDistance);
        Console.WriteLine("Total load of all routes: {0}m", totalLoad);
    }

    public static void Main(String[] args)
    {
        // Instantiate the data problem.
        DataModel data = new DataModel();

        // Create Routing Index Manager
        RoutingIndexManager manager =
            new RoutingIndexManager(data.DistanceMatrix.GetLength(0), data.VehicleNumber, data.Depot);

        // Create Routing Model.
        RoutingModel routing = new RoutingModel(manager);

        // Create and register a transit callback.
        int transitCallbackIndex = routing.RegisterTransitCallback((long fromIndex, long toIndex) =>
                                                                   {
                                                                       // Convert from routing variable Index to
                                                                       // distance matrix NodeIndex.
                                                                       var fromNode = manager.IndexToNode(fromIndex);
                                                                       var toNode = manager.IndexToNode(toIndex);
                                                                       return data.DistanceMatrix[fromNode, toNode];
                                                                   });

        // Define cost of each arc.
        routing.SetArcCostEvaluatorOfAllVehicles(transitCallbackIndex);

        // Add Capacity constraint.
        int demandCallbackIndex = routing.RegisterUnaryTransitCallback((long fromIndex) =>
                                                                       {
                                                                           // Convert from routing variable Index to
                                                                           // demand NodeIndex.
                                                                           var fromNode =
                                                                               manager.IndexToNode(fromIndex);
                                                                           return data.Demands[fromNode];
                                                                       });
        routing.AddDimensionWithVehicleCapacity(demandCallbackIndex, 0, // null capacity slack
                                                data.VehicleCapacities, // vehicle maximum capacities
                                                true,                   // start cumul to zero
                                                "Capacity");

        // Setting first solution heuristic.
        RoutingSearchParameters searchParameters =
            operations_research_constraint_solver.DefaultRoutingSearchParameters();
        searchParameters.FirstSolutionStrategy = FirstSolutionStrategy.Types.Value.PathCheapestArc;
        searchParameters.LocalSearchMetaheuristic = LocalSearchMetaheuristic.Types.Value.GuidedLocalSearch;
        searchParameters.TimeLimit = new Duration { Seconds = 1 };

        // Solve the problem.
        Assignment solution = routing.SolveWithParameters(searchParameters);

        // Print solution on console.
        PrintSolution(data, routing, manager, solution);
    }
}

GitHub 上列出了其他类型的约束条件的车辆路由问题示例(查找名称中带有“vrp”的示例)。

如果问题没有解决方案,会怎么样?

具有约束条件的路由问题(例如 CVRP)可能没有可行的解决方案,例如,当运输的商品总数超过车辆的总容量时。如果您尝试解决这样的问题,求解器可能会执行详尽的搜索,这会花费很长时间,最终您必须放弃并中断该程序。

通常不会产生任何问题。不过,您可以通过以下几种方法,在程序没有解决方案时阻止程序运行很长时间:

  • 在程序中设置时间限制,即使在没有找到解决方案的情况下,该限制也会停止搜索。但请注意,如果问题的解决方案需要很长时间进行搜索,该程序可能会在查找解决方案之前达到时间限制。
  • 设置针对营业地点访问者数量下降的处罚。这样,在无法避免此问题时,求解器可以返回一个不会访问所有位置的“解决方案”。请参阅处罚和访问次数减少

一般来说,我们很难判断给定问题是否能够解决问题。即使对 CVRP 的需求总量不超过总容量,确定所有商品是否都能车辆内载的一个问题是多个 kacksack 问题