基于遗传算法实现流水车间调度问题用python实现代码生成

基于遗传算法（Genetic Algorithm, GA）解决流水车间调度问题是优化问题的一种常见应用。这种算法模拟自然选择过程，通过迭代的方式寻找最优解。以下是用Python实现的一个简化版的遗传算法流水线调度问题的基本框架：

import random
import operator

# 定义遗传算法相关的函数
def fitness_function(scheduling):  # 调度方案的适应度评估函数
    total_cost = calculate_total_cost(scheduling)  # 计算总成本
    return 1 / total_cost if total_cost else float('inf')  # 较低的成本意味着更好的适应度

def crossover(parent1, parent2):  # 配对交叉
    start = random.randint(0, len(parent1) - 1)
    end = min(start + random.randint(1, len(parent1) // 2), len(parent1))
    offspring = [parent1[i] for i in range(start, end)] + parent2[end:]
    offspring.extend(parent1[start:end])
    return offspring

def mutation(chromosome):  # 突变操作
    index = random.randint(0, len(chromosome) - 1)
    chromosome[index], chromosome[(index + 1) % len(chromosome)] = chromosome[(index + 1) % len(chromosome)], chromosome[index]
    return chromosome

# 流水车间模型相关的类和函数
class Job:
    # ... (定义任务类，包含工件ID、加工时间等)

class Factory:
    def __init__(self, jobs):
        self.jobs = jobs
        self.schedules = []

    def generate_initial_population(self, size):
        # ... (生成初始种群，如随机排列的任务顺序)

    def genetic_algorithm(self, population_size, generations, mutation_rate):
        best_schedule = None
        for _ in range(generations):
            # tournament selection
            parents = select_tournament(population, size=2)
            child = crossover(parents[0], parents[1])
            child = mutation(child) if random.random() < mutation_rate else child
            # replace worst with new candidate
            population = elitism(population, child)
            current_best = max(population, key=fitness_function)
            if not best_schedule or fitness_function(current_best) > fitness_function(best_schedule):
                best_schedule = current_best
        return best_schedule

# 主函数
def main():
    factory = Factory(jobs_list)  # 初始化工厂实例
    best_schedule = factory.genetic_algorithm(
        population_size, generations, mutation_rate
    )
    print(f"最优调度: {best_schedule}")