多自动导引车调度的代码

多自动导引车调度的代码实现是一个相对复杂的问题，需要考虑任务分配、路径规划、资源利用等多个方面，下面是一个简单的示例代码，供参考：

# 导入必要的库
import numpy as np
import random

# 定义导引车的类
class AGV:
    def __init__(self, id, capacity, speed):
        self.id = id
        self.capacity = capacity
        self.speed = speed
        self.position = [0, 0]  # 导引车的当前位置
        self.task_list = []  # 导引车当前的任务列表

# 定义任务的类
class Task:
    def __init__(self, id, start, end, priority):
        self.id = id
        self.start = start
        self.end = end
        self.priority = priority

# 定义一个简单的遗传算法来进行任务分配和调度
class GA:
    def __init__(self, agvs, tasks, population_size, crossover_rate, mutation_rate, max_generation):
        self.agvs = agvs
        self.tasks = tasks
        self.population_size = population_size
        self.crossover_rate = crossover_rate
        self.mutation_rate = mutation_rate
        self.max_generation = max_generation
        self.population = []  # 种群
        self.best_fitness = 0
        self.best_solution = []

    # 初始化种群
    def init_population(self):
        for i in range(self.population_size):
            solution = []
            for task in self.tasks:
                solution.append(random.choice(self.agvs))
            self.population.append(solution)

    # 计算适应度
    def calc_fitness(self, solution):
        fitness = 0
        for agv in self.agvs:
            task_list = []
            for i in range(len(solution)):
                if solution[i] == agv:
                    task_list.append(self.tasks[i])
            fitness += self.calc_agv_fitness(agv, task_list)
        return fitness

    # 计算单个导引车的适应度
    def calc_agv_fitness(self, agv, task_list):
        fitness = 0
        position = agv.position
        for task in task_list:
            fitness += 1 / self.calc_distance(position, task.start)
            position = task.end
        return fitness

    # 计算两点之间的距离
    def calc_distance(self, pos1, pos2):
        return np.sqrt((pos1[0] - pos2[0]) ** 2 + (pos1[1] - pos2[1]) ** 2)

    # 选择
    def selection(self):
        fitness_list = []
        for solution in self.population:
            fitness_list.append(self.calc_fitness(solution))
        fitness_sum = sum(fitness_list)
        probability_list = [fitness / fitness_sum for fitness in fitness_list]
        parents = []
        for i in range(self.population_size):
            parent1 = self.population[np.random.choice(range(self.population_size), p=probability_list)]
            parent2 = self.population[np.random.choice(range(self.population_size), p=probability_list)]
            parents.append((parent1, parent2))
        return parents

    # 交叉
    def crossover(self, parent1, parent2):
        if random.random() < self.crossover_rate:
            crossover_point = random.randint(0, len(parent1) - 1)
            child1 = parent1[:crossover_point] + parent2[crossover_point:]
            child2 = parent2[:crossover_point] + parent1[crossover_point:]
            return (child1, child2)
        else:
            return (parent1, parent2)

    # 变异
    def mutation(self, solution):
        if random.random() < self.mutation_rate:
            mutation_point = random.randint(0, len(solution) - 1)
            solution[mutation_point] = random.choice(self.agvs)

    # 进化
    def evolve(self):
        parents = self.selection()
        new_population = []
        for parent1, parent2 in parents:
            child1, child2 = self.crossover(parent1, parent2)
            self.mutation(child1)
            self.mutation(child2)
            new_population.append(child1)
            new_population.append(child2)
        self.population = new_population

    # 寻找最优解
    def find_best_solution(self):
        for solution in self.population:
            fitness = self.calc_fitness(solution)
            if fitness > self.best_fitness:
                self.best_fitness = fitness
                self.best_solution = solution

    # 运行
    def run(self):
        self.init_population()
        for i in range(self.max_generation):
            self.evolve()
            self.find_best_solution()
        return self.best_solution

以上代码实现了一个简单的遗传算法来进行多自动导引车的任务分配和调度，其中包括导引车的类、任务的类、遗传算法的类，以及种群的初始化、适应度计算、选择、交叉、变异、进化等操作。需要注意的是，这个示例代码仅仅是一个简单的示例，实际应用中需要根据具体情况进行修改和优化。