python解决车间调度问题代码

根据提供的引用，车间调度问题是一个经典的NP-hard问题，需要使用复杂的算法进行求解。以下是一个使用遗传算法求解车间调度问题的Python代码示例：

import random

# 定义遗传算法的参数
POPULATION_SIZE = 50  # 种群大小
CROSSOVER_RATE = 0.9  # 交叉概率
MUTATION_RATE = 0.1  # 变异概率
GENERATION_COUNT = 100  # 迭代次数

# 定义车间调度问题的参数
MACHINE_COUNT = 3  # 机器数量
JOB_COUNT = 6  # 工件数量
OPERATION_COUNT = 2  # 工序数量
JOBS = [i for i in range(JOB_COUNT)]
MACHINES = [i for i in range(MACHINE_COUNT)]
OPERATIONS = [i for i in range(OPERATION_COUNT)]

# 定义工件的加工时间
PROCESSING_TIMES = [
    [[3, 2, 1], [2, 1, 2]],
    [[2, 3, 1], [1, 2, 1]],
    [[1, 2, 3], [2, 3, 1]],
    [[3, 1, 2], [1, 3, 2]],
    [[2, 1, 3], [3, 2, 1]],
    [[1, 3, 2], [2, 1, 3]]
]

# 定义种群类
class Population:
    def __init__(self, size):
        self.size = size
        self.individuals = []
        for i in range(size):
            individual = Individual()
            self.individuals.append(individual)

    def get_fittest(self):
        fittest = self.individuals[0]
        for i in range(1, self.size):
            if self.individuals[i].fitness > fittest.fitness:
                fittest = self.individuals[i]
        return fittest

# 定义个体类
class Individual:
    def __init__(self):
        self.genes = []
        for i in range(JOB_COUNT):
            gene = Gene()
            self.genes.append(gene)
        self.fitness = self.calculate_fitness()

    def calculate_fitness(self):
        makespan = 0
        machine_time = [0] * MACHINE_COUNT
        for i in range(JOB_COUNT * OPERATION_COUNT):
            job_index = self.genes[i].job_index
            machine_index = self.genes[i].machine_index
            processing_time = PROCESSING_TIMES[job_index][self.genes[i].operation_index][machine_index]
            start_time = max(machine_time[machine_index], makespan)
            end_time = start_time + processing_time
            machine_time[machine_index] = end_time
            makespan = max(makespan, end_time)
        return 1 / makespan

    def crossover(self, other):
        child1 = Individual()
        child2 = Individual()
        for i in range(JOB_COUNT):
            if random.random() < CROSSOVER_RATE:
                child1.genes[i] = self.genes[i]
                child2.genes[i] = other.genes[i]
            else:
                child1.genes[i] = other.genes[i]
                child2.genes[i] = self.genes[i]
        return child1, child2

    def mutate(self):
        for i in range(JOB_COUNT):
            if random.random() < MUTATION_RATE:
                self.genes[i] = Gene()

# 定义基因类
class Gene:
    def __init__(self):
        self.job_index = random.choice(JOBS)
        self.machine_index = random.choice(MACHINES)
        self.operation_index = random.choice(OPERATIONS)

# 定义遗传算法函数
def genetic_algorithm():
    population = Population(POPULATION_SIZE)
    for i in range(GENERATION_COUNT):
        new_population = Population(POPULATION_SIZE)
        for j in range(POPULATION_SIZE // 2):
            parent1 = roulette_wheel_selection(population)
            parent2 = roulette_wheel_selection(population)
            child1, child2 = parent1.crossover(parent2)
            child1.mutate()
            child2.mutate()
            new_population.individuals[j * 2] = child1
            new_population.individuals[j * 2 + 1] = child2
        population = new_population
    return population.get_fittest()

# 定义轮盘赌选择函数
def roulette_wheel_selection(population):
    total_fitness = sum([individual.fitness for individual in population.individuals])
    random_value = random.uniform(0, total_fitness)
    current_sum = 0
    for individual in population.individuals:
        current_sum += individual.fitness
        if current_sum > random_value:
            return individual

# 运行遗传算法并输出结果
best_individual = genetic_algorithm()
print("Best makespan: ", 1 / best_individual.fitness)

以上代码使用遗传算法求解车间调度问题，其中定义了种群、个体、基因等类，并实现了交叉、变异、选择等操作。在遗传算法函数中，首先初始化种群，然后进行迭代，每次迭代都进行交叉、变异、选择等操作，最终返回最优个体。