vrp代码

VRP（Vehicle Routing Problem）是指在一定的条件下，规划运输车辆的路线和装载量，使得所有的运输需求能够得到满足，并且使得总的运输成本最小化。

以下是一个Python的VRP求解代码示例：

import numpy as np
from scipy.spatial.distance import pdist, squareform
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp

# 定义问题类
class VRP:
    def __init__(self, num_vehicles, depot, customers):
        self.num_vehicles = num_vehicles  # 车辆数目
        self.depot = depot  # 起点
        self.customers = customers  # 客户列表

        # 计算距离矩阵
        self.dist_matrix = squareform(pdist(np.concatenate((self.depot, self.customers), axis=0)))

        # 创建路线规划求解器
        self.manager = pywrapcp.RoutingIndexManager(len(self.dist_matrix), self.num_vehicles, self.depot)
        self.routing = pywrapcp.RoutingModel(self.manager)

        # 设置距离回调函数
        self.def_distance_callback()

    # 定义距离回调函数
    def def_distance_callback(self):
        def distance_callback(from_index, to_index):
            from_node = self.manager.IndexToNode(from_index)
            to_node = self.manager.IndexToNode(to_index)
            return self.dist_matrix[from_node][to_node]

        self.routing.SetArcCostEvaluatorOfAllVehicles(distance_callback)

    # 求解VRP
    def solve(self, time_limit=10000):
        # 设置求解参数
        search_parameters = pywrapcp.DefaultRoutingSearchParameters()
        search_parameters.local_search_metaheuristic = routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH
        search_parameters.time_limit.seconds = time_limit

        # 求解VRP
        assignment = self.routing.SolveWithParameters(search_parameters)

        # 输出结果
        if assignment:
            print('Total distance of all routes: {} meters'.format(assignment.ObjectiveValue()))
            for vehicle_id in range(self.num_vehicles):
                index = self.routing.Start(vehicle_id)
                plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
                route_distance = 0
                while not self.routing.IsEnd(index):
                    node_index = self.manager.IndexToNode(index)
                    plan_output += ' {} -> '.format(node_index)
                    previous_index = index
                    index = assignment.Value(self.routing.NextVar(index))
                    route_distance += self.routing.GetArcCostForVehicle(previous_index, index, vehicle_id)
                node_index = self.manager.IndexToNode(index)
                plan_output += ' {}\n'.format(node_index)
                plan_output += 'Distance of the route: {} meters\n'.format(route_distance)
                print(plan_output)
        else:
            print('No solution found.')

使用示例：

# 定义客户数据
num_customers = 5
depot = [(0, 0)]
customers = np.random.rand(num_customers, 2) * 10

# 定义VRP问题
vrp = VRP(num_vehicles=2, depot=depot, customers=customers)

# 求解VRP
vrp.solve()

这个示例代码使用了Google OR-Tools库中的路线规划求解器，它可以自动求解VRP问题，并给出最优解。