diff --git a/Decode.py b/Decode.py index 2a9b386..e6ff57d 100644 --- a/Decode.py +++ b/Decode.py @@ -32,7 +32,7 @@ class Decode: Site = 0 # 按照基因的MS部分按工件序号划分 for S_i in self.J.values(): - Ms_decompose.append(MS[Site:Site + S_i]) + Ms_decompose.append(MS[Site:Site + S_i]) #对MS按照每道工序数进行切分 Site += S_i for i in range(len(Ms_decompose)): # len(Ms_decompose)表示工件数 JM_i = [] diff --git a/Encode.py b/Encode.py index 34c718b..4f27a1e 100644 --- a/Encode.py +++ b/Encode.py @@ -22,12 +22,12 @@ class Encode: self.RS_num = int(0.2 * Pop_size) # 随机选择初始化 self.Len_Chromo = 0 for i in J.values(): - self.Len_Chromo += i + self.Len_Chromo += i # 遍历字典J的所有值并求和，即工序数之和 # 生成工序准备的部分 def OS_List(self): OS_list = [] - for k, v in self.J.items(): + 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 OS_add = [k - 1 for j in range(v)] OS_list.extend(OS_add) return OS_list @@ -36,7 +36,7 @@ class Encode: def CHS_Matrix(self, C_num): return np.zeros([C_num, self.Len_Chromo], dtype=int) - # 定位每个工件的每道工序的位置 + # 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 def Site(self, Job, Operation): O_num = 0 for i in range(len(self.J)): @@ -84,7 +84,7 @@ class Encode: OS_list = self.OS_List() OS = self.CHS_Matrix(self.LS_num) for i in range(self.LS_num): - random.shuffle(OS_list) # 生成工序排序部分 + random.shuffle(OS_list) # (随机打乱)生成工序排序部分 OS[i] = np.array(OS_list) # 随机打乱后将其赋值给OS的某一行（因为有一个种群，第i则是赋值在OS的第i行，以此生成完整的OS） GJ_List = [i_1 for i_1 in range(self.J_num)] # 生成工件集 for g in GJ_List: # 选择第一个工件（注意：不用随机打乱了） diff --git a/GA.py b/GA.py index 2bec7a4..0c9f003 100644 --- a/GA.py +++ b/GA.py @@ -9,18 +9,18 @@ from Instance import * class GA(): def __init__(self): - self.Pop_size = 400 # 种群数量 + self.Pop_size = 300 # 种群数量 self.Pc = 0.8 # 交叉概率 self.Pm = 0.3 # 变异概率 self.Pv = 0.5 # 选择何种方式进行交叉的概率阈值 self.Pw = 0.95 # 选择何种方式进行变异的概率阈值 - self.Max_Itertions = 100 # 最大迭代次数 + self.Max_Itertions = 20 # 最大迭代次数 # 适应度 def fitness(self, CHS, J, Processing_time, M_num, Len): Fit = [] for i in range(len(CHS)): - d = Decode(J, Processing_time, M_num) + d = Decode(J, Processing_time, M_num) #实例化一个解码器，传入问题参数 Fit.append(d.decode(CHS[i], Len)) return Fit @@ -34,7 +34,7 @@ class GA(): """ 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) + random.shuffle(T_r) #直接打乱T_r，原地修改 R = T_r[0:r] # 按照随机数r产生r个互不相等的整数 OS_1 = CHS1[O_num:2 * T0] OS_2 = CHS2[O_num:2 * T0] diff --git a/Instance.py b/Instance.py index b375463..45dc2aa 100644 --- a/Instance.py +++ b/Instance.py @@ -5,7 +5,7 @@ M_num：加工机器数 O_num：加工工序数 J_num：工件个数 """ - +""" CKS201 = [[10, 9, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999], [9999, 9999, 14, 16, 9999, 9999, 9999, 9999, 9999, 9999, 9999, 9999], [9999, 9999, 9999, 9999, 15, 25, 21, 9999, 9999, 9999, 9999, 9999], @@ -24,3 +24,63 @@ J = {i: (5 if i < 4 else 8) for i in range(1, 8)} M_num = 12 O_num = 3 * 5 + 4 * 8 J_num = 7 +""" +CKS201 = [ + [5, 9999, 7, 9999, 9999, 9999], # 工序1: M1=5, M3=7 + [9999, 6, 9999, 8, 9999, 9999], # 工序2: M2=6, M4=8 + [10, 9999, 9999, 9999, 12, 9999], # 工序3: M1=10, M5=12 + [9999, 9999, 15, 9999, 9999, 18], # 工序4: M3=15, M6=18 + [9999, 9, 9999, 11, 9999, 9999], # 工序5: M2=9, M4=11 + [14, 9999, 9999, 9999, 9999, 16] # 工序6: M1=14, M6=16 +] + +CKS301 =[ + [9999, 4, 9999, 6, 9999, 9999], # 工序1: M2=4, M4=6 + [9999, 9999, 5, 9999, 7, 9999], # 工序2: M3=5, M5=7 + [9999, 8, 9999, 9999, 9999, 10], # 工序3: M2=8, M6=10 + [12, 9999, 9999, 14, 9999, 9999], # 工序4: M1=12, M4=14 + [9999, 9999, 16, 9999, 18, 9999], # 工序5: M3=16, M5=18 + [9999, 20, 9999, 9999, 9999, 22] # 工序6: M2=20, M6=22 +] + +CKS401 =[ + [3, 9999, 9999, 9999, 5, 9999], # 工序1: M1=3, M5=5 + [9999, 7, 9999, 9999, 9999, 9], # 工序2: M2=7, M6=9 + [9999, 9999, 11, 13, 9999, 9999], # 工序3: M3=11, M4=13 + [15, 9999, 9999, 9999, 17, 9999], # 工序4: M1=15, M5=17 + [9999, 19, 9999, 9999, 9999, 21], # 工序5: M2=19, M6=21 + [9999, 9999, 23, 25, 9999, 9999] # 工序6: M3=23, M4=25 +] + +CKS501 =[ + [9999, 9999, 9999, 6, 9999, 8], # 工序1: M4=6, M6=8 + [10, 9999, 12, 9999, 9999, 9999], # 工序2: M1=10, M3=12 + [9999, 14, 9999, 9999, 16, 9999], # 工序3: M2=14, M5=16 + [9999, 9999, 9999, 18, 9999, 20], # 工序4: M4=18, M6=20 + [22, 9999, 24, 9999, 9999, 9999], # 工序5: M1=22, M3=24 + [9999, 26, 9999, 9999, 28, 9999] # 工序6: M2=26, M5=28 +] + +CKS601 =[ + [9999, 9999, 5, 9999, 9999, 7], # 工序1: M3=5, M6=7 + [9, 9999, 9999, 11, 9999, 9999], # 工序2: M1=9, M4=11 + [9999, 13, 9999, 9999, 15, 9999], # 工序3: M2=13, M5=15 + [9999, 9999, 17, 9999, 9999, 19], # 工序4: M3=17, M6=19 + [21, 9999, 9999, 23, 9999, 9999], # 工序5: M1=21, M4=23 + [9999, 25, 9999, 9999, 27, 9999] # 工序6: M2=25, M5=27 +] + +CKS701 =[ + [4, 6, 9999, 9999, 9999, 9999], # 工序1: M1=4, M2=6 + [9999, 9999, 8, 10, 9999, 9999], # 工序2: M3=8, M4=10 + [9999, 9999, 9999, 9999, 12, 14], # 工序3: M5=12, M6=14 + [16, 18, 9999, 9999, 9999, 9999], # 工序4: M1=16, M2=18 + [9999, 9999, 20, 22, 9999, 9999], # 工序5: M3=20, M4=22 + [9999, 9999, 9999, 9999, 24, 26] # 工序6: M5=24, M6=26 +] + +Processing_time = [CKS201] + [CKS301] + [CKS401]+ [CKS501]+ [CKS601]+ [CKS701] +J = {i: 6 for i in range(1, 7)} +M_num = 6 +O_num = 6 * 6 +J_num = 6 \ No newline at end of file diff --git a/main.py b/main.py index f1da84d..fc689c2 100644 --- a/main.py +++ b/main.py @@ -1,5 +1,4 @@ import random - import matplotlib.pyplot as plt import numpy as np @@ -13,7 +12,7 @@ from Instance import * def Gantt(Machines): M = ['red', 'blue', 'yellow', 'orange', 'green', 'palegoldenrod', 'purple', 'pink', 'Thistle', 'Magenta', 'SlateBlue', 'RoyalBlue', 'Cyan', 'Aqua', 'floralwhite', 'ghostwhite', 'goldenrod', 'mediumslateblue', - 'navajowhite', 'navy', 'sandybrown', 'moccasin'] + 'navajowhite', 'nawy', 'sandybrown', 'moccasin'] for i in range(len(Machines)): Machine = Machines[i] Start_time = Machine.O_start @@ -49,13 +48,8 @@ if __name__ == '__main__': if best_fitness < Optimal_fit: Optimal_fit = best_fitness Optimal_CHS = Best - Best_fit.append(Optimal_fit) - print('best_fitness', best_fitness) - d = Decode(J, Processing_time, M_num) - Fit.append(d.decode(Optimal_CHS, O_num)) - Gantt(d.Machines) - else: - Best_fit.append(Optimal_fit) + print('iter_{}: new best_fitness = {}'.format(i, best_fitness)) + Best_fit.append(Optimal_fit) # 始终记录当前最优适应度 for j in range(len(C)): Cafter = [] if random.random() < g.Pc: @@ -76,10 +70,26 @@ if __name__ == '__main__': if Cafter != []: Fit = g.fitness(Cafter, J, Processing_time, M_num, O_num) C[j] = Cafter[Fit.index(min(Fit))] - x = np.linspace(0, 50, g.Max_Itertions) + + # 迭代结束后输出最终结果 + print("\n=== 优化结果 ===") + print("最优适应度 (最少完成时间):", Optimal_fit) + + # 解码最优解并绘制甘特图 + d = Decode(J, Processing_time, M_num) + final_fitness = d.decode(Optimal_CHS, O_num) + print("解码验证适应度:", final_fitness) + + # 绘制最优甘特图 + Gantt(d.Machines) + + # 绘制收敛曲线 + x = np.linspace(0, g.Max_Itertions, g.Max_Itertions) + plt.figure() plt.plot(x, Best_fit, '-k') - plt.title('the maximum completion time of each iteration') + plt.title('The maximum completion time of each iteration') plt.ylabel('Cmax') - plt.xlabel('Test Num') + plt.xlabel('Iteration') + plt.grid(True) plt.savefig('最大完成时间的优化过程.png') - plt.show() + plt.show() \ No newline at end of file diff --git a/优化后排程方案的甘特图.png b/优化后排程方案的甘特图.png index dfd8cd0..241626b 100644 Binary files a/优化后排程方案的甘特图.png and b/优化后排程方案的甘特图.png differ