带不相关机的可重入柔性流水车间问题的改进灾变遗传算法  

An Improved Catastrophic GeneticAlgorithm for Re-entrant Flexible Flowshop Problem with Unrelated ParallelMachines

作　　者：轩华[1] 刘淑燕 王薛苑[1] 李冰[1] XUAN Hua;LIU Shuyan;WANG Xueyuan;LI Bing(School of Management Engineering,Zhengzhou University,Zhengzhou,Henan 450001,China)

机构地区：[1]郑州大学管理工程学院,河南郑州450001

出　　处：《工业工程与管理》2021年第5期161-171,共11页Industrial Engineering and Management

基　　金：国家自然科学基金资助项目(U1804151,U1604150);河南省科技攻关计划项目(202102310310)。

摘　　要：针对实际工业生产中存在的带不相关并行机的可重入柔性流水车间问题,以工件的最大完工时间最小化为目标,建立了整数规划模型,并提出了改进灾变遗传算法以求解该模型。在算法中采用了与加工时间相关的机器选择机制以获得初始种群;运用自适应交叉和变异算子改善解的质量;引入灾变算子提高种群的多样性。通过仿真实验说明了改进灾变遗传算法能够在较短的计算时间内获得较高质量的近优解。The reentrant flexible flowshop problem with unrelated parallel machines in real-world industrial production was studied.The integer programming model was established with the goal of minimizing the maximum completion time of the jobs,and an improved catastrophic genetic algorithm(ICGA)was proposed to solve the model.In the algorithm,a machine selection mechanism related to processing time was adopted to find the initial population and the adaptive crossover and mutation operators were applied to improve solution quality.The catastrophe operator was introduced to increase the diversity of the population.Experimental results show that ICGA can obtain near-optimal solutions with higher quality in a shorter computational time.

关 键 词：不相关并行机 可重入柔性流水车间 改进灾变遗传算法 灾变算子 

分 类 号：TB49[一般工业技术]