GA(遗传算法)求解JSP(车间调度问题)python项目源码+项目说明.zip

import numpy as np import random from Decode_for_JSP import Decode from Encode_for_JSP import Encode import itertools class JSPGAInput: def __init__(self, dict) -> None: self.ProcessingTime = [] self.ProcessingGroup = [] self.MachineStartTime = [] self.TimeEfficent = [] self.MachineBuffer = [] self.Itertion = 10 self.__dict__.update(dict) class JSPGAResult: def __init__(self, decode: Decode, best_fit: list, worst_fit: list, avg_fit: list) -> None: self.Decode = decode self.BestFit = best_fit self.WorstFit = worst_fit self.AvgFit = avg_fit class GA: def __init__(self): self.Pop_size=300 #种群数量 self.P_c=0.8 #交叉概率 self.P_m=0.1 #变异概率 self.P_v=0.1 #选择何种方式进行交叉 self.P_w=0.0 #采用何种方式进行变异 self.Max_Itertions=10 #最大迭代次数 #适应度 def fitness(self,CHS,J,Processing_time,M_num,Len, Machine_start_time, Time_efficent, Machine_buffer): Fit=[] for i in range(len(CHS)): d = Decode(J, Processing_time, M_num, Machine_start_time, Time_efficent, Machine_buffer) Fit.append(d.Decode_1(CHS[i],Len)) return Fit #机器部分交叉 def Crossover_Machine(self,CHS1,CHS2,T0): T_r=[j for j in range(T0)] r = random.randint(1, 10) # 在区间[1,T0]内产生一个整数r random.shuffle(T_r) R = T_r[0:r] # 按照随机数r产生r个互不相等的整数 # 将父代的染色体复制到子代中去，保持他们的顺序和位置 OS_1=CHS1[T0:2*T0] OS_2 = CHS2[T0:2 * T0] C_1 = CHS2[0:T0] C_2 = CHS1[0:T0] for i in R: K,K_2 = C_1[i],C_2[i] C_1[i],C_2[i] = K_2,K CHS1=np.hstack((C_1,OS_1)) CHS2 = np.hstack((C_2, OS_2)) return CHS1,CHS2 #工序交叉部分 def Crossover_Operation(self,CHS1, CHS2, T0, J_num, Processing_group): OS_1 = CHS1[T0:2 * T0] OS_2 = CHS2[T0:2 * T0] MS_1 =CHS1[0:T0] MS_2 = CHS2[0:T0] Set1 = [] Set2 = [] if (Processing_group is not None and len(Processing_group) > 0): #工件组合（加工顺序必须固定） Job_list = [i for i in range(J_num)] random.shuffle(Job_list) tmp_group = [] for group in Processing_group: if (len(group) > 0): tmp_group.append(group) for i in group: Job_list.remove(i) for i in Job_list: tmp_group.append([i]) random.shuffle(tmp_group) r = random.randint(1, len(tmp_group) - 1) for group in tmp_group[0:r]: for i in group: Set1.append(i) for group in tmp_group[r:len(tmp_group)]: for i in group: Set2.append(i) else: Job_list = [i for i in range(J_num)] random.shuffle(Job_list) r = random.randint(1, J_num - 1) Set1 = Job_list[0:r] Set2 = Job_list[r:J_num] new_os1 = [] new_os2 = [] for i in OS_1: if i in Set1: new_os1.append(i) if i in Set2: new_os2.append(i) for i in OS_2: if i not in Set1: new_os1.append(i) if i not in Set2: new_os2.append(i) CHS1=np.hstack((MS_1,new_os1)) CHS2 = np.hstack((MS_2, new_os2)) return CHS1,CHS2 def reduction(self,num,J,T0): T0=[j for j in range(T0)] K=[] Site=0 for k,v in J.items(): K.append(T0[Site:Site+v]) Site+=v for i in range(len(K)): if num in K[i]: Job=i O_num=K[i].index(num) break return Job,O_num #机器变异部分 def Variation_Machine(self,CHS,O,T0,J): Tr=[i_num for i_num in range(T0)] MS=CHS[0:T0] OS=CHS[T0:2*T0] # 机器选择部分 r = random.randint(1, T0 - 1) # 在变异染色体中选择r个位置 random.shuffle(Tr) T_r = Tr[0:r] for i in T_r: Job=self.reduction(i,J,T0) O_i=Job[0] O_j =Job[1] Machine_using = O[O_i][O_j] Machine_time = [] for j in Machine_using: if j is not None and len(j) > 0: Machine_time.append(j) Min_index = Machine_time.index(min(min(Machine_time))) MS[i] = Min_index CHS=np.hstack((MS,OS)) return CHS #工序变异部分 def Variation_Operation(self, CHS,T0,J_num,J,Processing_time,M_num): return CHS #暂不进行工序变异 MS=CHS[0:T0] OS=list(CHS[T0:2*T0]) r=random.randint(1,J_num-1) Tr=[i for i in range(J_num)] random.shuffle(Tr) Tr=Tr[0:r] J_os=dict(enumerate(OS)) #随机选择r个不同的基因 J_os = sorted(J_os.items(), key=lambda d: d[1]) Site=[] for i in range(r): Site.append(OS.index(Tr[i])) A=list(itertools.permutations(Tr, r)) A_CHS=[] for i in range(len(A)): for j in range(len(A[i])): OS[Site[j]]=A[i][j] C_I=np.hstack((MS,OS)) A_CHS.append(C_I) Fit = [] for i in range(len(A_CHS)): d = Decode(J, Processing_time, M_num) Fit.append(d.Decode_1(CHS, T0)) return A_CHS[Fit.index(min(Fit))] def Select(self,Fit_value): Fit=[] for i in range(len(Fit_value)): fit=1/Fit_value[i] Fit.append(fit) Fit=np.array(Fit) idx = np.random.choice(np.arange(len(Fit_value)), size=len(Fit_value), replace=True, p=(Fit) / (Fit.sum())) return idx def start(self, inputParam: JSPGAInput) -> JSPGAResult: self.Max_Itertions = inputParam.Itertion J = {} J_num = 0 M_num = 0 for a in inputParam.ProcessingTime: J_num += 1 J[J_num] = len(a) M_num = len(a[0]) e = Encode(inputParam.ProcessingTime, self.Pop_size, J, J_num, M_num, inputParam.ProcessingGroup) OS_List=e.OS_List() Len_Chromo=e.Len_Chromo CHS1=e.Global_initial() CHS2 = e.Random_initial() CHS3 = e.Local_initial() C=np.vstack((CHS1,CHS2,CHS3)) Optimal_fit=9999 Optimal_CHS=0 Best_fit=[] Worst_fit = [] Avg_fit = [] for i in range(self.Max_Itertions): Fit = self.fitness(C, J, inputParam.ProcessingTime, M_num, Len_Chromo, inputParam.MachineStartTime, inputParam.TimeEfficent, inputParam.MachineBuffer) Best = C[Fit.index(min(Fit))] best_fitness = min(Fit) worst_fit = max(Fit) avg_fit = np.average(Fit) Worst_fit.append(worst_fit) Avg_fit.append(avg_fit) # d = Decode(J, Processing_time, M_num) # Optimal_CHS = C[Fit.index(max(Fit))] # Fit.append(d.Decode_1(Optimal_CHS, Len_Chromo)) # d.Gantt(d.Machines) if best_fitness < Optimal_fit: Optimal_fit = best_fitness Optimal_CHS = Best Best_fit.append(Optimal_fit) print('best_fitness', best_fitness) else: Best_fit.append(Optimal_fit) Select = self.Select(Fit) for j in range(len(C)): offspring = [] if random.random()<self.P_c: N_i = random.choice(np.arange(len(C))) if random.random()<self.P_v: