86 lines
3.4 KiB
Python
86 lines
3.4 KiB
Python
import random
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import matplotlib.pyplot as plt
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import numpy as np
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from Decode import Decode
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from Encode import Encode
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from GA import GA
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from Instance import *
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# 绘制甘特图
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def Gantt(Machines):
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M = ['red', 'blue', 'yellow', 'orange', 'green', 'palegoldenrod', 'purple', 'pink', 'Thistle', 'Magenta',
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'SlateBlue', 'RoyalBlue', 'Cyan', 'Aqua', 'floralwhite', 'ghostwhite', 'goldenrod', 'mediumslateblue',
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'navajowhite', 'navy', 'sandybrown', 'moccasin']
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for i in range(len(Machines)):
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Machine = Machines[i]
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Start_time = Machine.O_start
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End_time = Machine.O_end
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for i_1 in range(len(End_time)):
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plt.barh(i, width=End_time[i_1] - Start_time[i_1], height=0.8, left=Start_time[i_1],
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color=M[Machine.assigned_task[i_1][0] - 1], edgecolor='black')
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plt.text(x=Start_time[i_1] + (End_time[i_1] - Start_time[i_1]) / 2 - 0.5, y=i,
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s=Machine.assigned_task[i_1][0])
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plt.yticks(np.arange(len(Machines) + 1), np.arange(1, len(Machines) + 2))
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plt.title('Scheduling Gantt chart')
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plt.ylabel('Machines')
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plt.xlabel('Time(min)')
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plt.savefig('优化后排程方案的甘特图.png')
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plt.show()
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if __name__ == '__main__':
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Optimal_fit = 9999 # 最佳适应度（初始化）
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Optimal_CHS = 0 # 最佳适应度对应的基因个体（初始化）
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g = GA()
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e = Encode(Processing_time, g.Pop_size, J, J_num, M_num)
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CHS1 = e.Global_initial()
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CHS2 = e.Random_initial()
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CHS3 = e.Local_initial()
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C = np.vstack((CHS1, CHS2, CHS3))
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Best_fit = [] # 记录适应度在迭代过程中的变化，便于绘图
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for i in range(g.Max_Itertions):
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print("iter_{} start!".format(i))
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Fit = g.fitness(C, J, Processing_time, M_num, O_num)
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Best = C[Fit.index(min(Fit))]
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best_fitness = min(Fit)
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if best_fitness < Optimal_fit:
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Optimal_fit = best_fitness
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Optimal_CHS = Best
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Best_fit.append(Optimal_fit)
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print('best_fitness', best_fitness)
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d = Decode(J, Processing_time, M_num)
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Fit.append(d.decode(Optimal_CHS, O_num))
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Gantt(d.Machines)
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else:
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Best_fit.append(Optimal_fit)
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for j in range(len(C)):
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Cafter = []
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if random.random() < g.Pc:
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N_i = random.choice(np.arange(len(C)))
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if random.random() < g.Pv:
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Cross = g.machine_cross(C[j], C[N_i], O_num)
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else:
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Cross = g.operation_cross(C[j], C[N_i], O_num, J_num)
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Cafter.append(Cross[0])
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Cafter.append(Cross[1])
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Cafter.append(C[j])
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if random.random() < g.Pm:
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if random.random() < g.Pw:
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Variance = g.machine_variation(C[j], Processing_time, O_num, J)
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else:
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Variance = g.operation_variation(C[j], O_num, J_num, J, Processing_time, M_num)
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Cafter.append(Variance)
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if Cafter != []:
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Fit = g.fitness(Cafter, J, Processing_time, M_num, O_num)
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C[j] = Cafter[Fit.index(min(Fit))]
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x = np.linspace(0, 50, g.Max_Itertions)
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plt.plot(x, Best_fit, '-k')
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plt.title('the maximum completion time of each iteration')
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plt.ylabel('Cmax')
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plt.xlabel('Test Num')
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plt.savefig('最大完成时间的优化过程.png')
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plt.show()
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