基于近亲变异NSGA-Ⅱ算法的多目标柔性作业车间调度  

Multi-objective Flexible Job-shop Scheduling Problem Based on NSGA-Ⅱwith Close Relative Variation

作　　者：陈鸿海[1] 蒋增强[2] 左乐[2] 张彦如[1] 

机构地区：[1]合肥工业大学机械与汽车工程学院,合肥230009 [2]北京交通大学机械与电子控制工程学院,北京100044

出　　处：《农业机械学报》2015年第4期344-350,共7页Transactions of the Chinese Society for Agricultural Machinery

基　　金："十二五"国家科技支撑计划资助项目(2012BAF12B03);中央高校基本科研业务费专项资金资助项目(2012JBM090)

摘　　要：在研究柔性作业车间多目标调度问题现状和不足的基础上,提出了包括最大完工时间、加工成本以及加工质量3个优化目标的多目标柔性作业车间调度模型;并针对上述模型,提出了基于近亲变异的改进非支配排序遗传算法(Non-dominated sorting genetic algorithm,NSGA-Ⅱ),该算法根据计算交叉染色体的血缘关系,确定子代个体的变异率,优化了NSGA-Ⅱ的变异策略,避免了算法的早熟问题;最后,针对实际案例,构建相应的调度模型和算法,计算结果证实了模型和算法的可行性和有效性。At present, the non-dominated sorting genetic algorithm-Ⅱ（NSGA-Ⅱ） is used widely in researches of flexible job-shop multi-objective rescheduling problem. Because of the excessive elite reservation, the algorithm is easily precocious, thus the performance of multi-objective optimization algorithm could be improved. By analyzing the research status and insufficiency of multi-objective flexible job-shop scheduling problem （FJSP）, a multi-objective FJSP optimization model was put forward, in which the makespan, processing cost and processing quality were considered. According to the above model, a modified non-dominated sorting genetic algorithm （NSGA-Ⅱ ） with close relative variation was designed. In this algorithm, the chromosome mutation rate was determined after calculating the blood relationship between the two cross chromosomes. Crossover and mutation strategies of NSGA-Ⅱwere optimized, and the prematurity of population was overcome. Finally, the performance of the proposed model and algorithm was evaluated through a case study, and the results demonstrated the efficiency and feasibility of the proposed model and algorithm.

关 键 词：多目标调度 柔性作业车间 改进非支配排序遗传算法 近亲变异 

分 类 号：TH18[机械工程—机械制造及自动化]