帮我写一个Python 车间调度算法程序

以下是一个Python的车间调度算法程序，实现了一种基于遗传算法的车间调度方案。

import random

# 定义任务类
class Task:
    def __init__(self, id, processing_time):
        self.id = id
        self.processing_time = processing_time

# 定义车间类
class Workshop:
    def __init__(self, tasks):
        self.num_tasks = len(tasks)
        self.tasks = tasks
        self.machines = [0] * self.num_tasks
        self.time_slots = [0] * self.num_tasks

    # 计算完成时间
    def get_completion_time(self):
        machine_time = [0] * self.num_tasks
        for i in range(self.num_tasks):
            machine_id = self.machines[i]
            machine_time[machine_id] += self.tasks[i].processing_time
            self.time_slots[i] = machine_time[machine_id]
        return max(machine_time)

# 定义遗传算法类
class GeneticAlgorithm:
    def __init__(self, population_size, mutation_rate, elitism_rate):
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.elitism_rate = elitism_rate

    # 初始化种群
    def init_population(self, num_tasks, num_machines):
        population = []
        for i in range(self.population_size):
            machines = [random.randint(0, num_machines-1) for _ in range(num_tasks)]
            population.append(Workshop([Task(j, random.randint(1, 10)) for j in range(num_tasks)]))
            population[-1].machines = machines
        return population

    # 选择父代
    def select_parents(self, population):
        fitness_scores = [self.get_fitness_score(workshop) for workshop in population]
        fitness_sum = sum(fitness_scores)
        fitness_probs = [score/fitness_sum for score in fitness_scores]
        parents = []
        for i in range(2):
            parent_index = self.roulette_wheel_selection(fitness_probs)
            parents.append(population[parent_index])
        return parents

    # 轮盘赌选择
    def roulette_wheel_selection(self, fitness_probs):
        r = random.random()
        sum_probs = 0
        for i in range(len(fitness_probs)):
            sum_probs += fitness_probs[i]
            if sum_probs >= r:
                return i
        return len(fitness_probs)-1

    # 获取适应度评分
    def get_fitness_score(self, workshop):
        return 1/workshop.get_completion_time()

    # 交叉操作
    def crossover(self, parent1, parent2, num_tasks, num_machines):
        machines1 = parent1.machines
        machines2 = parent2.machines
        child = Workshop([Task(i, parent1.tasks[i].processing_time) for i in range(num_tasks)])
        for i in range(num_tasks):
            if random.random() < 0.5:
                child.machines[i] = machines1[i]
            else:
                child.machines[i] = machines2[i]
        return child

    # 变异操作
    def mutate(self, workshop, num_machines):
        for i in range(workshop.num_tasks):
            if random.random() < self.mutation_rate:
                workshop.machines[i] = random.randint(0, num_machines-1)

    # 进化
    def evolve(self, population, num_tasks, num_machines):
        sorted_population = sorted(population, key=lambda workshop: self.get_fitness_score(workshop), reverse=True)
        elite_size = int(self.elitism_rate * self.population_size)
        new_population = sorted_population[:elite_size]
        while len(new_population) < self.population_size:
            parent1, parent2 = self.select_parents(population)
            child = self.crossover(parent1, parent2, num_tasks, num_machines)
            self.mutate(child, num_machines)
            new_population.append(child)
        return new_population

# 测试程序
if __name__ == "__main__":
    num_tasks = 6
    num_machines = 3
    population_size = 10
    mutation_rate = 0.1
    elitism_rate = 0.2
    num_generations = 10

    ga = GeneticAlgorithm(population_size, mutation_rate, elitism_rate)
    population = ga.init_population(num_tasks, num_machines)

    for i in range(num_generations):
        population = ga.evolve(population, num_tasks, num_machines)
        best_workshop = max(population, key=lambda workshop: ga.get_fitness_score(workshop))
        print("Generation {} Best Completion Time: {}".format(i+1, best_workshop.get_completion_time()))

在此程序中，我们定义了`Task`和`Workshop`类来表示任务和车间。`GeneticAlgorithm`类实现了遗传算法的主要逻辑，包括初始化种群、选择父代、交叉和变异等操作。在测试程序中，我们使用了一个简单的调度问题，其中有6个任务和3台机器。我们设置种群大小为10，并运行10代，每一代都会输出最优完成时间。通过运行程序，我们可以得到一组最优的调度方案。