遗传算法求解车间调度问题python

遗传算法可以用来求解车间调度问题。下面是一个使用Python实现的简单示例代码：

import random

# 初始化种群
def init_population(population_size, gene_size):
    population = []
    for _ in range(population_size):
        chromosome = [i+1 for i in range(gene_size)]
        random.shuffle(chromosome)
        population.append(chromosome)
    return population

# 计算染色体适应度
def calculate_fitness(chromosome):
    fitness = 0
    for i in range(len(chromosome)-1):
        if chromosome[i] != chromosome[i+1]:
            fitness += 1
    return fitness

# 选择操作
def selection(population):
    fitness_values = [calculate_fitness(chromosome) for chromosome in population]
    total_fitness = sum(fitness_values)
    probabilities = [fitness/total_fitness for fitness in fitness_values]
    selected_chromosomes = random.choices(population, probabilities, k=len(population))
    return selected_chromosomes

# 交叉操作
def crossover(parent1, parent2):
    point = random.randint(0, len(parent1)-1)
    child1 = parent1[:point] + parent2[point:]
    child2 = parent2[:point] + parent1[point:]
    return child1, child2

# 变异操作
def mutation(chromosome):
    point1, point2 = random.sample(range(len(chromosome)), 2)
    chromosome[point1], chromosome[point2] = chromosome[point2], chromosome[point1]
    return chromosome

# 遗传算法求解车间调度问题
def solve_job_shop_scheduling(population_size, gene_size, max_generations):
    population = init_population(population_size, gene_size)
    best_fitness = float('inf')
    best_chromosome = None

    for generation in range(max_generations):
        selected_chromosomes = selection(population)
        new_population = []

        while len(new_population) < population_size:
            parent1, parent2 = random.sample(selected_chromosomes, 2)
            child1, child2 = crossover(parent1, parent2)
            child1 = mutation(child1)
            child2 = mutation(child2)
            new_population.extend([child1, child2])

        population = new_population

        for chromosome in population:
            fitness = calculate_fitness(chromosome)
            if fitness < best_fitness:
                best_fitness = fitness
                best_chromosome = chromosome

        print(f"Generation {generation+1}: Best Fitness = {best_fitness}")
    return best_chromosome

# 示例使用
population_size = 100
gene_size = 10
max_generations = 100

best_chromosome = solve_job_shop_scheduling(population_size, gene_size, max_generations)

print("Best Chromosome:", best_chromosome)

这是一个简单的遗传算法示例，用于求解车间调度问题。其中，种群的大小、基因的大小以及最大迭代次数可以根据具体问题进行调整。代码中的适应度函数根据车间调度问题的实际需求进行定义和修改。