python代码解决实例

要使用Python代码解决《基于禁忌搜索算法的物流系统车辆路径优化》中的实例，你可以参考以下步骤和代码示例。这个例子将展示如何实现禁忌搜索算法（Tabu Search, TS）来解决带软时间窗的车辆路径问题（VRP with Soft Time Windows, VRPSTW）。

### 1. 导入必要的库

import numpy as np
import random
import matplotlib.pyplot as plt
from itertools import permutations

### 2. 定义问题参数

# 分拨中心和客户点的数据
num_customers = 30
depot = {'id': 0, 'x': 35, 'y': 35, 'start_time': 8 * 60, 'end_time': 11.5 * 60, 'service_time': 0}
customers = [
    {'id': i, 'x': random.randint(0, 100), 'y': random.randint(0, 100),
     'demand': random.randint(100, 500), 'start_time': random.randint(0, 180),
     'end_time': random.randint(100, 240), 'service_time': 10} for i in range(1, num_customers + 1)
]

# 车辆参数
num_vehicles = 20
vehicle_capacity = 2000
vehicle_speed = 60  # km/h
vehicle_start_cost = 80  # 元/辆
vehicle_overtime_cost = 50  # 元/小时
early_penalty_cost = 30  # 元/小时
late_penalty_cost = 120  # 元/小时
service_time = 10  # 分钟
max_working_time = 3.5 * 60  # 分钟

### 3. 计算距离矩阵

def calculate_distance_matrix(customers):
    n = len(customers) + 1  # 包括分拨中心
    distance_matrix = np.zeros((n, n))
    
    for i in range(n):
        if i == 0:
            x1, y1 = depot['x'], depot['y']
        else:
            x1, y1 = customers[i-1]['x'], customers[i-1]['y']
        
        for j in range(n):
            if j == 0:
                x2, y2 = depot['x'], depot['y']
            else:
                x2, y2 = customers[j-1]['x'], customers[j-1]['y']
            
            distance_matrix[i][j] = np.sqrt((x1 - x2)**2 + (y1 - y2)**2)
    
    return distance_matrix

distance_matrix = calculate_distance_matrix(customers)

### 4. 初始化禁忌搜索算法

class TabuSearch:
    def __init__(self, distance_matrix, customers, num_vehicles, vehicle_capacity, vehicle_start_cost, vehicle_overtime_cost, early_penalty_cost, late_penalty_cost, max_working_time, max_iterations=2000, tabu_tenure=20):
        self.distance_matrix = distance_matrix
        self.customers = customers
        self.num_vehicles = num_vehicles
        self.vehicle_capacity = vehicle_capacity
        self.vehicle_start_cost = vehicle_start_cost
        self.vehicle_overtime_cost = vehicle_overtime_cost
        self.early_penalty_cost = early_penalty_cost
        self.late_penalty_cost = late_penalty_cost
        self.max_working_time = max_working_time
        self.max_iterations = max_iterations
        self.tabu_tenure = tabu_tenure
        self.tabu_list = []
        self.best_solution = None
        self.best_cost = float('inf')
        self.current_solution = self.initialize_solution()
        self.current_cost = self.calculate_total_cost(self.current_solution)

    def initialize_solution(self):
        # 随机初始化解
        initial_solution = [list(range(1, len(self.customers) + 1))]
        random.shuffle(initial_solution[0])
        return initial_solution

    def calculate_total_cost(self, solution):
        total_cost = 0
        for route in solution:
            route_cost = 0
            current_time = 0
            current_load = 0
            prev_customer_id = 0  # 分拨中心
            
            for customer_id in route:
                customer = self.customers[customer_id - 1]
                travel_time = self.distance_matrix[prev_customer_id][customer_id] / vehicle_speed * 60
                arrival_time = current_time + travel_time
                
                if arrival_time < customer['start_time']:
                    waiting_time = customer['start_time'] - arrival_time
                    route_cost += waiting_time * self.early_penalty_cost
                    current_time = customer['start_time']
                elif arrival_time > customer['end_time']:
                    delay_time = arrival_time - customer['end_time']
                    route_cost += delay_time * self.late_penalty_cost
                    current_time = arrival_time
                else:
                    current_time = arrival_time
                
                current_time += customer['service_time']
                current_load += customer['demand']
                
                if current_load > self.vehicle_capacity:
                    raise ValueError("Exceeded vehicle capacity")
                
                if current_time > self.max_working_time:
                    route_cost += (current_time - self.max_working_time) * self.vehicle_overtime_cost
                
                route_cost += self.distance_matrix[prev_customer_id][customer_id] * 5  # 单位运输成本
                prev_customer_id = customer_id
            
            route_cost += self.distance_matrix[prev_customer_id][0] * 5  # 返回分拨中心的成本
            route_cost += self.vehicle_start_cost
            total_cost += route_cost
        
        return total_cost

    def generate_neighbors(self, solution):
        neighbors = []
        for i in range(len(solution)):
            for j in range(i + 1, len(solution)):
                neighbor = solution.copy()
                neighbor[i], neighbor[j] = neighbor[j], neighbor[i]
                if neighbor not in self.tabu_list:
                    neighbors.append(neighbor)
        return neighbors

    def update_tabu_list(self, solution):
        if len(self.tabu_list) >= self.tabu_tenure:
            self.tabu_list.pop(0)
        self.tabu_list.append(solution)

    def run(self):
        for iteration in range(self.max_iterations):
            neighbors = self.generate_neighbors(self.current_solution)
            best_neighbor = min(neighbors, key=self.calculate_total_cost)
            best_neighbor_cost = self.calculate_total_cost(best_neighbor)
            
            if best_neighbor_cost < self.best_cost:
                self.best_solution = best_neighbor
                self.best_cost = best_neighbor_cost
            
            self.update_tabu_list(self.current_solution)
            self.current_solution = best_neighbor
            self.current_cost = best_neighbor_cost
        
        return self.best_solution, self.best_cost

### 5. 运行禁忌搜索算法

ts = TabuSearch(distance_matrix, customers, num_vehicles, vehicle_capacity, vehicle_start_cost, vehicle_overtime_cost, early_penalty_cost, late_penalty_cost, max_working_time)
best_solution, best_cost = ts.run()

print(f"Best Solution: {best_solution}")
print(f"Best Cost: {best_cost}")

### 6. 可视化结果

def plot_solution(solution, customers, depot):
    plt.figure(figsize=(10, 10))
    plt.scatter(depot['x'], depot['y'], color='red', label='Depot')
    for customer in customers:
        plt.scatter(customer['x'], customer['y'], color='blue')
        plt.text(customer['x'], customer['y'], f"{customer['id']}")

    for route in solution:
        x_coords = [depot['x']] + [customers[customer_id - 1]['x'] for customer_id in route] + [depot['x']]
        y_coords = [depot['y']] + [customers[customer_id - 1]['y'] for customer_id in route] + [depot['y']]
        plt.plot(x_coords, y_coords, color='green')

    plt.xlabel('X Coordinate')
    plt.ylabel('Y Coordinate')
    plt.title('Optimal Delivery Routes')
    plt.legend()
    plt.show()

plot_solution(best_solution, customers, depot)

以上代码展示了如何使用Python实现禁忌搜索算法来解决带软时间窗的车辆路径问题。你可以根据具体需求调整参数和逻辑，以获得更好的解决方案。