This paper presents a correlation analysis-based heuristic for the machine-part cell formation in the context of cellular manufacturing systems. Two new indices, viz. “mean correlation index” for forming the part families and “relevance index-modified” for identifying the appropriate machine cells are proposed. The machine-part cells formed by the proposed heuristic resulted in a higher grouping efficacy (GE) for 14.3% of the test instances gathered from the literature, and it performed equal to the best in class heuristics available in the literature for 80% of the test instances. The method presented in this paper has set a new benchmark GE for 5 of the 35 test instances used by the researchers in the context of machine-part cell formation without singletons.

本文提出了一种基于相关分析的启发式方法，用于在细胞制造系统的情况下对机器零件的细胞进行形成。两个新索引，即。用于形成零件族的“平均相关指数”和“相关指数-修改”提出了用于识别适当机器单元的方法。拟议的启发式方法形成的机器部分单元对于从文献中收集的14.3％的测试实例具有更高的分组功效（GE），并且其性能与文献中80％的同类最佳启发式方法相同。测试实例。本文介绍的方法为研究人员在没有单例的机器零件细胞形成的情况下使用的35个测试实例中的5个设置了新的基准GE。