job = [[0,1,2],[3,2,5],[2,5,4]] machines = [[2,3,4],[8,5,6],[5,6,3]],def calculate_fitness(a): end_time = [0] * len(job) for i in a: machines_time = [0] * len(machines[i]) for j in range(len(job[i])): m_id = job[i][j] start_time = max(machines_time[m_id], end_time[i]) end_time[i] = start_time + machines[m_id][i] makespan = max(end_time) return 1 / makespan,就索引方面进行改错
时间: 2023-11-30 22:23:36 浏览: 25
在这段代码中,变量`i`在`machines`列表中应该是索引而不是元素,因此需要将`machines[i]`改为`machines[a[i]]`。
下面是修改后的代码:
```python
def calculate_fitness(a):
end_time = [0] * len(job)
for i in range(len(a)):
machines_time = [0] * len(machines[a[i]])
for j in range(len(job[i])):
m_id = job[i][j]
start_time = max(machines_time[m_id], end_time[a[i]])
end_time[a[i]] = start_time + machines[m_id][a[i]]
makespan = max(end_time)
return 1.0 / makespan
```
在修改后的代码中,`i`被用作循环计数器,而`a[i]`表示第`i`个任务在哪台机器上进行加工,因此需要将`machines[i]`改为`machines[a[i]]`。
相关问题
machines = [0] * len(job_sequence[0])
这行代码的作用是创建一个长度为 `job_sequence` 中第一个元素的长度的列表,并将其初始化为全 0。可以简单地理解为创建一个由数字 0 组成的列表。
具体来说,`job_sequence` 是一个二维列表,假设它的第一个元素是 `seq`,则 `len(job_sequence[0])` 表示 `seq` 的长度。这个长度也就是 `machines` 列表的长度。
`[0] * len(job_sequence[0])` 表示将数字 0 重复 `seq` 的长度次,构成一个列表。例如,假设 `seq` 的长度为 3,则 `[0] * len(job_sequence[0])` 的结果为 `[0, 0, 0]`。通过这种方式,就创建了一个长度为 `seq` 的长度的列表,并将其初始化为 0。
最终,将这个列表赋值给了变量 `machines`。这个变量可以在后续的代码中使用,例如修改列表中的元素值、遍历列表等。
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[:])
```