把我写一下利用遗传算法解决柔性车间调度问题的代码

好的，这里是一个简单的利用遗传算法解决柔性车间调度问题的Python代码示例：

import random

# 初始化参数
POP_SIZE = 100       # 种群大小
CROSS_RATE = 0.8     # 交叉率
MUTATION_RATE = 0.05 # 变异率
N_GENERATIONS = 50   # 迭代次数

# 定义调度问题，这里以一个简单的例子为例
JOBS = [[2, 1, 2], [1, 2, 3], [3, 2, 1]]  # 三个工件，每个工件有三个任务，每个任务包含机器编号和加工时间
MACHINES = 3  # 三台机器

# 编码方式：每个染色体表示一个调度方案，每个基因表示一个任务
def encode_chromosome():
    chromosome = []
    for i in range(len(JOBS)):
        chromosome.extend(JOBS[i])
    random.shuffle(chromosome)
    return chromosome

# 解码方式：将染色体解码成调度方案
def decode_chromosome(chromosome):
    jobs = [[] for i in range(len(JOBS))]
    for i in range(len(chromosome)):
        job_index = i // MACHINES
        jobs[job_index].append((chromosome[i], JOBS[job_index][i % MACHINES]))
    for i in range(len(jobs)):
        jobs[i] = sorted(jobs[i], key=lambda x: x[1])
    return jobs

# 适应度函数：计算调度方案的完成时间和机器利用率等指标
def fitness(chromosome):
    jobs = decode_chromosome(chromosome)
    machines = [0] * MACHINES
    time = 0
    for i in range(len(jobs)):
        for j in range(len(jobs[i])):
            machine_index = jobs[i][j][0] - 1
            time = max(time, machines[machine_index]) + jobs[i][j][1]
            machines[machine_index] = time
    return 1 / time

# 选择算子：轮盘赌选择
def select(population):
    fitness_list = [fitness(chromosome) for chromosome in population]
    total_fitness = sum(fitness_list)
    roulette_wheel = [fitness_list[i] / total_fitness for i in range(len(population))]
    return random.choices(population, weights=roulette_wheel)

# 交叉算子：单点交叉
def crossover(parents):
    child1, child2 = parents
    if random.random() < CROSS_RATE:
        cross_point = random.randint(1, len(child1) - 1)
        child1[cross_point:], child2[cross_point:] = child2[cross_point:], child1[cross_point:]
    return child1, child2

# 变异算子：随机交换两个基因
def mutate(child):
    if random.random() < MUTATION_RATE:
        mutate_point1 = random.randint(0, len(child) - 1)
        mutate_point2 = random.randint(0, len(child) - 1)
        child[mutate_point1], child[mutate_point2] = child[mutate_point2], child[mutate_point1]
    return child

# 遗传算法主函数
def genetic_algorithm():
    # 初始化种群
    population = [encode_chromosome() for i in range(POP_SIZE)]
    for i in range(N_GENERATIONS):
        # 选择操作
        parents = [select(population) for j in range(2)]
        # 交叉操作
        offspring = [crossover(parents) for j in range(POP_SIZE // 2)]
        # 变异操作
        offspring = [mutate(child) for child in offspring]
        # 环境选择
        population = parents + offspring
        population = sorted(population, key=lambda x: -fitness(x))
        population = population[:POP_SIZE]
    # 返回最优解
    best_chromosome = max(population, key=lambda x: fitness(x))
    best_jobs = decode_chromosome(best_chromosome)
    return best_jobs

# 测试
best_jobs = genetic_algorithm()
print(best_jobs)

这个示例代码简单实现了遗传算法的主要组成部分，包括初始化种群、编码方式、适应度函数、选择算子、交叉算子、变异算子和环境选择等。在实际应用中，你需要根据具体的柔性车间调度问题进行调整和优化。