def __init__(self, machines): self.machines = machines

As an AI language model, I do not have access to the context in which this code is being used. However, based on the code provided, it appears to be defining an __init__ method for a class. The method takes in a parameter called "machines" and assigns it to the "self.machines" attribute of the class. The purpose of this method is likely to initialize instances of the class with a list of machines.

相关问题

import numpy as np from platypus import NSGAII, Problem, Real, Integer # 定义问题 class JobShopProblem(Problem): def __init__(self, jobs, machines, processing_times): num_jobs = len(jobs) num_machines = len(machines[0]) super().__init__(num_jobs, 1, 1) self.jobs = jobs self.machines = machines self.processing_times = processing_times self.types[:] = Integer(0, num_jobs - 1) self.constraints[:] = [lambda x: x[0] == 1] def evaluate(self, solution): job_order = np.argsort(np.array(solution.variables[:], dtype=int)) machine_available_time = np.zeros(len(self.machines)) job_completion_time = np.zeros(len(self.jobs)) for job_idx in job_order: job = self.jobs[job_idx] for machine_idx, processing_time in zip(job, self.processing_times[job_idx]): machine_available_time[machine_idx] = max(machine_available_time[machine_idx], job_completion_time[job_idx]) job_completion_time[job_idx] = machine_available_time[machine_idx] + processing_time solution.objectives[:] = [np.max(job_completion_time)] # 定义问题参数 jobs = [[0, 1], [2, 0], [1, 2]] machines = [[0, 1, 2], [1, 2, 0], [2, 0, 1]] processing_times = [[5, 4], [3, 5], [1, 3]] # 创建算法实例 problem = JobShopProblem(jobs, machines, processing_times) algorithm = NSGAII(problem) algorithm.population_size = 100 # 设置优化目标 problem.directions[:] = Problem.MINIMIZE # 定义算法参数 algorithm.population_size = 100 max_generations = 100 mutation_probability = 0.1 # 设置算法参数 algorithm.max_iterations = max_generations algorithm.mutation_probability = mutation_probability # 运行算法 algorithm.run(max_generations) # 输出结果 print("最小化的最大完工时间：", algorithm.result[0].objectives[0]) print("工件加工顺序和机器安排方案：", algorithm.result[0].variables[:]) 请检查上述代码

代码的语法没有问题，但是在定义问题时，约束条件中的 lambda 函数有误。应该将 x[0] == 1 改为 x[0] == 0，因为变量的范围是 0 到 num_jobs - 1。另外，应该在 lambda 函数前加上 @staticmethod，表示这是一个静态方法。修改后的代码如下：

import numpy as np
from platypus import NSGAII, Problem, Real, Integer

# 定义问题
class JobShopProblem(Problem):
    def __init__(self, jobs, machines, processing_times):
        num_jobs = len(jobs)
        num_machines = len(machines[0])
        super().__init__(num_jobs, 1, 1)
        self.jobs = jobs
        self.machines = machines
        self.processing_times = processing_times
        self.types[:] = Integer(0, num_jobs - 1)
        self.constraints[:] = [self.check_order]
        
    @staticmethod
    def check_order(x):
        return x[0] == 0

    def evaluate(self, solution):
        job_order = np.argsort(np.array(solution.variables[:], dtype=int))
        machine_available_time = np.zeros(len(self.machines))
        job_completion_time = np.zeros(len(self.jobs))
        for job_idx in job_order:
            job = self.jobs[job_idx]
            for machine_idx, processing_time in zip(job, self.processing_times[job_idx]):
                machine_available_time[machine_idx] = max(machine_available_time[machine_idx], job_completion_time[job_idx])
                job_completion_time[job_idx] = machine_available_time[machine_idx] + processing_time
        solution.objectives[:] = [np.max(job_completion_time)]
        
# 定义问题参数
jobs = [[0, 1], [2, 0], [1, 2]]
machines = [[0, 1, 2], [1, 2, 0], [2, 0, 1]]
processing_times = [[5, 4], [3, 5], [1, 3]]

# 创建算法实例
problem = JobShopProblem(jobs, machines, processing_times)
algorithm = NSGAII(problem)
algorithm.population_size = 100

# 设置优化目标
problem.directions[:] = Problem.MINIMIZE

# 定义算法参数
algorithm.population_size = 100
max_generations = 100
mutation_probability = 0.1

# 设置算法参数
algorithm.max_iterations = max_generations
algorithm.mutation_probability = mutation_probability

# 运行算法
algorithm.run(max_generations)

# 输出结果
print("最小化的最大完工时间：", algorithm.result[0].objectives[0])
print("工件加工顺序和机器安排方案：", algorithm.result[0].variables[:])

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。