遗传算法 python 车间调度

遗传算法是一种优化算法，常用于解决车间调度等问题。通过模拟生物进化的过程，遗传算法可以搜索到问题的最优解。在Python中，可以使用遗传算法库进行车间调度问题的求解。以下是一个使用遗传算法的Python代码示例，供您参考学习：

import random

# 定义车间调度问题的适应度函数
def fitness_function(schedule):
    # 计算每个工件的完工时间，并返回总体完工时间
    total_completion_time = 0
    for job in schedule:
        completion_time = sum(job)
        total_completion_time += completion_time
    return total_completion_time

# 定义遗传算法的初始化函数
def initialize_population(population_size):
    # 随机生成初始种群
    population = []
    for _ in range(population_size):
        schedule = []
        for _ in range(num_jobs):
            operation_times = [random.randint(1, 10) for _ in range(num_machines)]
            schedule.append(operation_times)
        population.append(schedule)
    return population

# 定义遗传算法的选择函数
def selection(population, num_parents):
    # 选择适应度最高的父代个体作为后代的父母
    parents = sorted(population, key=fitness_function)[:num_parents]
    return parents

# 定义遗传算法的交叉函数
def crossover(parents, num_offspring):
    # 使用单点交叉生成指定数量的后代
    offspring = []
    while len(offspring) < num_offspring:
        parent1 = random.choice(parents)
        parent2 = random.choice(parents)
        crossover_point = random.randint(1, num_jobs - 1)
        child = parent1[:crossover_point] + parent2[crossover_point:]
        offspring.append(child)
    return offspring

# 定义遗传算法的变异函数
def mutation(offspring):
    # 对后代个体的某些基因进行随机变异
    for child in offspring:
        for job in child:
            for i in range(num_machines):
                if random.random() < mutation_rate:
                    job[i] = random.randint(1, 10)
    return offspring

# 定义遗传算法的主函数
def genetic_algorithm(population_size, num_generations):
    population = initialize_population(population_size)
    for _ in range(num_generations):
        parents = selection(population, num_parents)
        offspring = crossover(parents, num_offspring)
        offspring = mutation(offspring)
        population = parents + offspring
    best_schedule = min(population, key=fitness_function)
    return best_schedule

# 设置问题的参数
num_jobs = 5  # 工件数量
num_machines = 3  # 机器数量
mutation_rate = 0.1  # 变异概率
population_size = 50  # 种群规模
num_parents = 10  # 父代数量
num_offspring = 40  # 后代数量
num_generations = 100  # 迭代次数

# 调用遗传算法求解车间调度问题
best_schedule = genetic_algorithm(population_size, num_generations)

# 输出最优解
print("Best schedule:", best_schedule)