基于改进遗传算法的柔性流水车间调度研究  

Improved genetic algorithm for flexible flow shop scheduling

作　　者：徐嘉琦 田野[1,2] XU Jiaqi;TIAN Ye(College of Computer Technology,Changchun University of Science and Technology,Changchun 130022,CHN;College of Artificial Intelligence,Changchun University of Science and Technology,Changchun 130022,CHN)

机构地区：[1]长春理工大学计算机学院,吉林长春130022 [2]长春理工大学人工智能学院,吉林长春130022

出　　处：《制造技术与机床》2024年第4期181-187,共7页Manufacturing Technology & Machine Tool

摘　　要：针对最小化最大完工时间的柔性流水车间调度问题,文章提出了多目标选择的改进的遗传算法(MTGA),设计了针对该问题的一维的编码与解码方法,采用对立的方法进行种群的初始化。针对遗传算法,交叉操作进行整个工序的交叉向最优解靠拢加快了算法的收敛速度,变异操作中对所有的工序操作顺序进行整体变异,选择操作将种群分成多份做到向多个较优解靠拢,扩大了算法的搜索范围,降低了陷入局部最优的概率,并应用了两套交叉和变异概率增加算法灵活性。通过多个已有算法进行对比验证了算法的有效性。Aiming at the flexible flow shop scheduling problem that minimizes the maximum completion time,this paper proposes an improved genetic algorithm bases on multiple target of selection(MTGA).A one-dimensional encoding and decoding method for this problem is designed,and an opposing method is used to initialize the population.For the genetic algorithm,the crossover operation of the whole process is closer to the optimal solution,which accelerates the convergence speed of the algorithm,the overall variation of the operation sequence of all processes in the mutation operation,and the selection operation divides the population into multiple parts to achieve multiple optimal solutions,which increases the search range of the algorithm and reduces the probability of falling into the local optimal.Two sets of crossover and variation probabilities are applied to increase the flexibility of the algorithm.The effectiveness of the algorithm is verified by comparison with multiple existing algorithms.

关 键 词：柔性流水车间调度 改进遗传算法 对立方法 整体变异 多目标选择 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]