帮我写一个aps排产算法

好的，我可以为您提供一个基于遗传算法的aps排产算法的代码实现。以下是示例代码：

import random

# 定义工件类
class Job:
    def __init__(self, name, duration):
        self.name = name
        self.duration = duration

# 定义工序类
class Operation:
    def __init__(self, job, machine):
        self.job = job
        self.machine = machine

# 定义机器类
class Machine:
    def __init__(self, name):
        self.name = name
        self.operations = []

    def add_operation(self, operation):
        self.operations.append(operation)

# 定义遗传算法类
class GeneticAlgorithm:
    def __init__(self, jobs, machines, population_size, generations):
        self.jobs = jobs
        self.machines = machines
        self.population_size = population_size
        self.generations = generations

    def run(self):
        population = self.initialize_population()
        for i in range(self.generations):
            population = self.evolve_population(population)
        return self.get_best_schedule(population)

    def initialize_population(self):
        population = []
        for i in range(self.population_size):
            schedule = self.create_random_schedule()
            population.append(schedule)
        return population

    def create_random_schedule(self):
        schedule = []
        for job in self.jobs:
            operations = []
            for machine in self.machines:
                operation = Operation(job, machine)
                operations.append(operation)
            random.shuffle(operations)
            schedule += operations
        return schedule

    def evolve_population(self, population):
        new_population = []
        for i in range(self.population_size):
            parent1 = self.select_parent(population)
            parent2 = self.select_parent(population)
            child = self.crossover(parent1, parent2)
            child = self.mutate(child)
            new_population.append(child)
        return new_population

    def select_parent(self, population):
        tournament_size = 5
        tournament = random.sample(population, tournament_size)
        tournament.sort(key=lambda x: self.get_fitness(x))
        return tournament[0]

    def crossover(self, parent1, parent2):
        child = parent1[:]
        for i in range(len(parent2)):
            if random.random() < 0.5:
                operation = parent2[i]
                if operation not in child:
                    index = child.index(parent1[i])
                    child[index] = operation
        return child

    def mutate(self, schedule):
        mutation_rate = 0.1
        for i in range(len(schedule)):
            if random.random() < mutation_rate:
                j = random.randint(0, len(schedule) - 1)
                schedule[i], schedule[j] = schedule[j], schedule[i]
        return schedule

    def get_best_schedule(self, population):
        population.sort(key=lambda x: self.get_fitness(x))
        return population[0]

    def get_fitness(self, schedule):
        makespan = self.get_makespan(schedule)
        return 1 / makespan

    def get_makespan(self, schedule):
        machine_times = {}
        for machine in self.machines:
            machine_times[machine] = 0
        for operation in schedule:
            job = operation.job
            machine = operation.machine
            start_time = machine_times[machine]
            end_time = start_time + job.duration
            machine_times[machine] = end_time
        return max(machine_times.values())

# 示例用法
jobs = [Job('A', 3), Job('B', 2), Job('C', 4)]
machines = [Machine('M1'), Machine('M2'), Machine('M3')]
ga = GeneticAlgorithm(jobs, machines, 10, 100)
schedule = ga.run()
print([operation.job.name for operation in schedule])

希望这个代码能够帮助您实现aps排产算法。