聚类和NSGA-Ⅱ联合算法在混合流水车间的应用研究
Application Research of Clustering and NSGA-Ⅱ Joint Algorithm in Hybrid Flow Shop
韩树贤 1赵文普 1闫华1
作者信息
- 1. 中国空空导弹研究院 洛阳 471009
- 折叠
摘要
为了改善某高端装备制造企业总装车间混流生产调度困难、批处理阶段产品组批困难的问题,以及实现车间多个目标的同步联合优化,研究了含批处理机的混合流水车间多目标优化问题.首先根据车间运行情况建立了多目标优化模型,之后提出了基于K-means聚类算法和非支配排序遗传算法(NSGA-Ⅱ)的联合方法,设计了能够对不相容产品进行分组的聚类流程,以及基于产品组编号和组内产品编号的双层编码方式,为批处理工序设计了完整的组批流程.最后,使用车间生产案例进行测试,并将测试结果同仅使用NSGA-Ⅱ得到的结果进行对比,验证了所提方法的有效性.
Abstract
In order to improve the scheduling difficulty of mixed-flow production in the final assembly shop of a high-end equipment manufacturing enterprise,the difficulty of product group batch in the batch processing stage and realize the joint optimi-zation of multi-objective in the workshop,this paper investigates the multi-objective optimization problem of hybrid flow shop with batch processors.Firstly,a multi-objective optimization model is established according to the operation situation of the workshop,and then a joint method based on K-means clustering algorithm and non-dominated sorting Genetic Algorithm ⅱ(NSGA-Ⅱ)is proposed.A clustering process is designed to group incompatible products,and a double-layer coding method based on product group number and product number within the group is designed.A complete group batch process is designed for batch operations.Fi-nally,the workshop production case is used to test,and the results are compared with the results obtained by only using NSGA-Ⅱ,which verifies the effectiveness of the proposed method.
关键词
混合流水车间/并行批处理机/非支配排序遗传算法/K-means算法Key words
hybrid flow shop/parallel batch processing machine/non-dominated sorting genetics algorithm/K-means al-gorithm引用本文复制引用
基金项目
国家基础科研重点项目(JCKY2019205B012)
出版年
2024
国家科技期刊平台
NSTL
万方数据