Clustering algorithms for the design of a cellular manufacturing system—an analysis of their performance

Abstract

The heuristic clustering methods based on similarity coefficient are considered to be very efficient for providing modularity and flexibility in a cellular manufacturing systems (CMS's). Various algorithms have been implemented in these heuristic methods. However, these algorithms suffer from string effect which is also known as “chaining”. Sedveral studies have reported this problem, yet not much research has been conducted to investigate its impact on actual clustering process. This paper presents results from an analytical study performed to determine the severity of chaining problem and other characteristics associated with the clustering process of four selected algorithms. The four algorithms are Single linkage clustering (SLINK), Average linkage clustering (ALINK), Weighted average linkage clustering (WLINK), and Complete linkage clustering (CLINK). A sample of fifty problems with randomly generated data sets was used to determine feasible solutions consisting of machine cells and corresponding part families from each of the four algorithms. A quantitative measure is proposed for evaluating the performance of different algorithms. The study concludes that the chaining effect for CLINK, WLINK, ALINK and SLINK progresively worsens from CLINK to SLINK in the same order. The study also provides important guidelines to designers of a CMS in selecting the most efficient algorithm for a given problem data. Several important statistical results are also presented.