考虑顺序相关调整时间的混合流水车间调度问题的求解代码用python写出来

以下是一个简单的基于遗传算法的混合流水车间调度问题的求解代码，用Python实现：

import random

# 定义工件流程
jobs = {
    'J1': ['A', 'B', 'C'],
    'J2': ['B', 'C', 'A'],
    'J3': ['C', 'A', 'B'],
    'J4': ['B', 'A', 'C']
}

# 定义机器数量和工件数量
num_machines = 3
num_jobs = 4

# 定义每个工件在每个机器上的加工时间
processing_times = {
    'A': [2, 3, 4],
    'B': [3, 2, 1],
    'C': [4, 1, 3]
}

# 定义每个工件在每个机器之间的转移时间
transfer_times = {
    'A': [0, 1, 2],
    'B': [1, 0, 3],
    'C': [2, 3, 0]
}

# 定义遗传算法的参数
population_size = 50
mutation_rate = 0.1
num_generations = 100

# 初始化种群
def initialize_population():
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(num_jobs):
            chromosome.append(random.randint(1, num_machines))
        population.append(chromosome)
    return population

# 计算染色体的适应度
def calculate_fitness(chromosome):
    fitness = 0
    for i, job in enumerate(chromosome):
        machine = job - 1
        if i == 0:
            fitness += processing_times[jobs['J'+str(job)][0]][machine]
        else:
            prev_machine = chromosome[i-1] - 1
            transfer_time = transfer_times[jobs['J'+str(prev_job)][prev_machine]][machine]
            fitness += transfer_time + processing_times[jobs['J'+str(job)][i]][machine]
        prev_job = job
    return fitness

# 选择操作
def selection(population):
    fitnesses = [calculate_fitness(chromosome) for chromosome in population]
    total_fitness = sum(fitnesses)
    probabilities = [fitness / total_fitness for fitness in fitnesses]
    selected_indices = random.choices(range(len(population)), weights=probabilities, k=2)
    return population[selected_indices[0]], population[selected_indices[1]]

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(1, num_jobs-1)
    child1 = parent1[:crossover_point] + parent2[crossover_point:]
    child2 = parent2[:crossover_point] + parent1[crossover_point:]
    return child1, child2

# 变异操作
def mutation(chromosome):
    for i in range(num_jobs):
        if random.random() < mutation_rate:
            chromosome[i] = random.randint(1, num_machines)
    return chromosome

# 遗传算法求解
def genetic_algorithm():
    population = initialize_population()
    for i in range(num_generations):
        new_population = []
        for j in range(population_size // 2):
            parent1, parent2 = selection(population)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutation(child1)
            child2 = mutation(child2)
            new_population.append(child1)
            new_population.append(child2)
        population = new_population
        best_fitness = min([calculate_fitness(chromosome) for chromosome in population])
        print('Generation', i+1, '- Best Fitness:', best_fitness)

    best_chromosome = min(population, key=calculate_fitness)
    print('Best Solution:', best_chromosome)
    print('Best Fitness:', calculate_fitness(best_chromosome))

# 执行遗传算法求解
genetic_algorithm()

该代码使用遗传算法来求解混合流水车间调度问题。在代码中，首先定义了工件流程、机器数量、工件数量、加工时间和转移时间等参数。然后，使用初始化函数随机生成了一个初始种群，然后使用选择、交叉和变异等操作来更新种群并求解最优解。最后，输出了最优解和最优解的适应度。