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127 lines (119 loc) · 5.52 KB
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# project : GA_FJSP
# file : Encode.py
# author:yasuoman
# datetime:2021/4/4 15:59
# software: PyCharm
"""
description：种群初始化编码
说明：参考书籍：柔性作业车间调度智能算法及其应用 3.2.3FJSP的初始化
"""
'''
说明：参考书籍：柔性作业车间调度智能算法及其应用 3.2.3FJSP的初始化
'''
import numpy as np
import random
class Encode:
def __init__(self,Pop_size,p_table,job_op_num):
#Pop_size为种群个数
self.GS_num = int(0.6 * Pop_size) # 全局选择的个数
self.LS_num = int(0.2 * Pop_size) # 局部选择的个数
self.RS_num = int(0.2 * Pop_size) # 随机选择的个数
self.p_table = p_table
self.half_chr = p_table.shape[0]
self.job_op_num = job_op_num
self.m = p_table.shape[1]
self.n = len(job_op_num)
#得到初始有序的一维os
def order_os(self):
order_os=[(index+1) for index,op in enumerate(self.job_op_num) for i in range(op)]
# for index,op in enumerate(self.job_op_num):
# for i in range(op):
# order_OS.append(index+1)
#
return order_os
def random_selection(self):
MS=np.empty((self.RS_num,self.half_chr),dtype=int)
#随机选择OS
OS = np.empty((self.RS_num, self.half_chr),dtype=int)
order_os = self.order_os()[:]
# 随机选择MS
for episode in range(self.RS_num):
#打乱os的顺序
np.random.shuffle(order_os)
#随机选择os
OS[episode]=order_os[:]
for op_index,p in enumerate(self.p_table):
#找出该工件能够加工的机器的序号
ava_m = [(index+1) for index in range(len(p)) if p[index]!=-1]
#随机选择,先这样写着
MS[episode][op_index]=np.random.choice(np.arange(len(ava_m)))+1
chr = np.hstack((MS, OS))
return chr
def global_selection(self):
MS = np.empty((self.GS_num, self.half_chr), dtype=int)
# 随机选择OS
OS = np.empty((self.GS_num, self.half_chr), dtype=int)
order_os = self.order_os()[:]
# 随机选择MS
for episode in range(self.GS_num):
# 打乱os的顺序
np.random.shuffle(order_os)
# 随机选择os
OS[episode] = order_os[:]
# 用于随机选择的工件集合
job_list = [i for i in range(self.n)]
# 初始化值为0的长度为m的负荷数组
M_load = np.zeros(self.m, dtype=int)
for i in range(self.n):
#随机选择一个工件
job_num=np.random.choice(job_list)
#在这个工件的所有工序上进行遍历
for op in range(sum(self.job_op_num[:job_num]), sum(self.job_op_num[:job_num])+self.job_op_num[job_num]):
#得到临时的机器负荷数组
temp_load = np.array([pro + load for (pro, load) in zip(self.p_table[op], M_load) if pro != -1])
#得到临时的机器负荷索引
temp_index = [index for (index, pro) in enumerate(self.p_table[op]) if pro != -1]
#选取临时的机器符合最小的索引
ava_min_index = np.argmin(temp_load)
#将最小的索引+1放入MS中，即最好的可用的机器号,注意这里的下标是op,因为是随机找的工件
MS[episode][op]=ava_min_index+1
#更新机器负荷列表
M_load[temp_index[ava_min_index]] = temp_load[ava_min_index]
#删除刚刚随机的工件号，继续随机进行
job_list.remove(job_num)
chr = np.hstack((MS, OS))
return chr
def local_selection(self):
MS = np.empty((self.LS_num, self.half_chr), dtype=int)
# 随机选择OS
OS = np.empty((self.LS_num, self.half_chr), dtype=int)
order_os = self.order_os()[:]
# 随机选择MS
for episode in range(self.LS_num):
# 打乱os的顺序
np.random.shuffle(order_os)
# 随机选择os
OS[episode] = order_os[:]
# 因为不能直接得到二维矩阵的列索引，这里手工设置一个
chr_index = 0
#依次遍历整个工件
for i in range(self.n):
# 初始化值/重新置为0的长度为m的负荷数组
M_load = np.zeros(self.m, dtype=int)
# 在这个工件的所有工序上进行遍历
for op in range(sum(self.job_op_num[:i]),
sum(self.job_op_num[:i]) + self.job_op_num[i]):
# 得到临时的机器负荷数组
temp_load = np.array([pro + load for (pro, load) in zip(self.p_table[op], M_load) if pro != -1])
# 得到临时的机器负荷索引
temp_index = [index for (index, pro) in enumerate(self.p_table[op]) if pro != -1]
# 选取临时的机器符合最小的索引
ava_min_index = np.argmin(temp_load)
# 将最小的索引+1放入MS中，即最好的可用的机器号
MS[episode][chr_index] = ava_min_index + 1
# 列索引加1
chr_index += 1
# 更新机器负荷列表
M_load[temp_index[ava_min_index]] = temp_load[ava_min_index]
chr = np.hstack((MS, OS))
return chr