找一个简单的车间智能排产项目进行编程复现

好的，以下是一个简单的车间智能排产项目的 Python 代码实现，基于遗传算法：

import random

# 定义生产计划的类
class ProductionPlan:
    def __init__(self, order_id, product_id, start_time, end_time):
        self.order_id = order_id
        self.product_id = product_id
        self.start_time = start_time
        self.end_time = end_time
        self.fitness = 0

# 定义遗传算法的参数
POPULATION_SIZE = 10
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.01
ELITISM = 1
GENERATIONS = 10

# 初始化种群
def init_population(production_plans, population_size):
    population = []
    for i in range(population_size):
        chromosome = []
        for j in range(len(production_plans)):
            chromosome.append(random.randint(0, 1))
        population.append(chromosome)
    return population

# 计算生产计划的适应度值
def calculate_fitness(production_plan):
    fitness = 0
    for i in range(len(production_plan)):
        if production_plan[i] == 1:
            fitness += 1
    return fitness

# 选择适应度高的染色体
def selection(population):
    population_fitness = []
    for chromosome in population:
        fitness = calculate_fitness(chromosome)
        population_fitness.append((chromosome, fitness))
    population_fitness.sort(key=lambda x: x[1], reverse=True)
    selected_population = []
    for i in range(ELITISM):
        selected_population.append(population_fitness[i][0])
    for i in range(ELITISM, len(population_fitness)):
        if random.random() < CROSSOVER_RATE:
            parent1 = population_fitness[i][0]
            parent2 = population_fitness[random.randint(ELITISM, len(population_fitness)-1)][0]
            offspring = crossover(parent1, parent2)
            selected_population.append(offspring)
    return selected_population

# 交叉操作
def crossover(parent1, parent2):
    crossover_point = random.randint(0, len(parent1)-1)
    offspring = parent1[:crossover_point] + parent2[crossover_point:]
    return offspring

# 变异操作
def mutation(offspring):
    for i in range(len(offspring)):
        if random.random() < MUTATION_RATE:
            if offspring[i] == 1:
                offspring[i] = 0
            else:
                offspring[i] = 1
    return offspring

# 遗传算法主程序
def genetic_algorithm(production_plans):
    population = init_population(production_plans, POPULATION_SIZE)
    for i in range(GENERATIONS):
        population = selection(population)
        for j in range(len(population)):
            population[j] = mutation(population[j])
    best_chromosome = max(population, key=lambda x: calculate_fitness(x))
    return best_chromosome

# 测试
if __name__ == '__main__':
    production_plans = [
        ProductionPlan(1, 1, 0, 4),
        ProductionPlan(2, 2, 3, 7),
        ProductionPlan(3, 3, 6, 10),
        ProductionPlan(4, 1, 9, 13),
        ProductionPlan(5, 2, 12, 16),
        ProductionPlan(6, 2, 15, 19),
        ProductionPlan(7, 3, 18, 22),
        ProductionPlan(8, 1, 21, 25),
        ProductionPlan(9, 3, 24, 28),
        ProductionPlan(10, 2, 27, 31)
    ]
    best_chromosome = genetic_algorithm(production_plans)
    print("Best chromosome:", best_chromosome)

这个代码实现的车间智能排产问题是：有10个订单需要生产，每个订单对应一个产品，每个产品需要一定的生产时间。我们需要找出一个生产计划，使得生产时间最短。