121 lines
4.5 KiB
Python
121 lines
4.5 KiB
Python
import random
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import numpy as np
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class Encode:
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def __init__(self, Matrix, Pop_size, J, J_num, M_num):
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"""
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:param Matrix: 机器加工时间矩阵
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:param Pop_size: 种群数量
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:param J: 各工件对应的工序数字典
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:param J_num: 工件数
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:param M_num: 机器数
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"""
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self.Matrix = Matrix
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self.J = J
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self.J_num = J_num
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self.M_num = M_num
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self.CHS = []
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# 调整初始化比例：增加随机选择比例到50%
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self.GS_num = int(0.3 * Pop_size) # 全局选择初始化
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self.LS_num = int(0.2 * Pop_size) # 局部选择初始化
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self.RS_num = int(0.5 * Pop_size) # 随机选择初始化（提高比例）
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self.Len_Chromo = 0
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for i in J.values():
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self.Len_Chromo += i # 遍历字典J的所有值并求和，即工序数之和
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# 生成工序准备的部分（工件排列，天然满足工序顺序约束）
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def OS_List(self):
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OS_list = []
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for k, v in self.J.items(): # 遍历字典J的所有键值对，初始化工序矩阵
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OS_add = [k - 1 for j in range(v)] # 工件索引从0开始
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OS_list.extend(OS_add)
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return OS_list
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# 生成初始化矩阵
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def CHS_Matrix(self, C_num):
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return np.zeros([C_num, self.Len_Chromo * 2], dtype=int) # 工件排列+机器索引
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# 定位每个工件的每道工序的位置，Job第几个工件，Operation第几道工序
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def Site(self, Job, Operation):
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O_num = 0
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for i in range(len(self.J)):
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if i == Job:
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return O_num + Operation
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else:
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O_num = O_num + self.J[i + 1]
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return O_num
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# 全局初始化
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def Global_initial(self):
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CHS = self.CHS_Matrix(self.GS_num)
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OS_list = self.OS_List()
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for i in range(self.GS_num):
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random.shuffle(OS_list)
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OS = OS_list.copy()
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MS = [0] * self.Len_Chromo
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Machine_time = np.zeros(self.M_num, dtype=int)
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GJ_list = [i_1 for i_1 in range(self.J_num)]
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random.shuffle(GJ_list)
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for g in GJ_list:
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h = self.Matrix[g]
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for j in range(len(h)):
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D = h[j]
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List_Machine_weizhi = [k for k, val in enumerate(D) if val != 9999]
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Machine_Select = [Machine_time[m] + D[m] for m in List_Machine_weizhi]
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Min_time = min(Machine_Select)
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K = Machine_Select.index(Min_time)
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MS[self.Site(g, j)] = K
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Machine_time[List_Machine_weizhi[K]] += D[List_Machine_weizhi[K]]
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CHS[i] = np.hstack((OS, MS)) # 工件排列 + 机器索引（紧凑编码）
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return CHS
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# 局部初始化
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def Local_initial(self):
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CHS = self.CHS_Matrix(self.LS_num)
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OS_list = self.OS_List()
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for i in range(self.LS_num):
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random.shuffle(OS_list)
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OS = OS_list.copy()
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MS = [0] * self.Len_Chromo
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GJ_List = [i_1 for i_1 in range(self.J_num)]
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for g in GJ_List:
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Machine_time = np.zeros(self.M_num, dtype=int)
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h = self.Matrix[g]
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for j in range(len(h)):
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D = h[j]
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List_Machine_weizhi = [k for k, val in enumerate(D) if val != 9999]
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Machine_Select = [Machine_time[m] + D[m] for m in List_Machine_weizhi]
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Min_time = min(Machine_Select)
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K = Machine_Select.index(Min_time)
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MS[self.Site(g, j)] = K
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Machine_time[List_Machine_weizhi[K]] += D[List_Machine_weizhi[K]]
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CHS[i] = np.hstack((OS, MS))
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return CHS
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# 随机初始化（提高比例到50%）
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def Random_initial(self):
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CHS = self.CHS_Matrix(self.RS_num)
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OS_list = self.OS_List()
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for i in range(self.RS_num):
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random.shuffle(OS_list)
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OS = OS_list.copy()
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MS = [0] * self.Len_Chromo
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for g in range(self.J_num):
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h = self.Matrix[g]
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for j in range(len(h)):
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D = h[j]
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List_Machine_weizhi = [k for k, val in enumerate(D) if val != 9999]
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# 随机选择可用机器
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selected = random.choice(List_Machine_weizhi)
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K = List_Machine_weizhi.index(selected)
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MS[self.Site(g, j)] = K
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CHS[i] = np.hstack((OS, MS))
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return CHS |