Article ID: | iaor1997512 |
Country: | United Kingdom |
Volume: | 34 |
Issue: | 9 |
Start Page Number: | 2587 |
End Page Number: | 2611 |
Publication Date: | Sep 1996 |
Journal: | International Journal of Production Research |
Authors: | Veeramani D., Mani K. |
Keywords: | statistics: multivariate, artificial intelligence |
The process of forming group technology based families for cellular manufacturing applications often entails the identification of clusters in {0,1}-matrices. Most of the methods developed to date for cluster formation in this context employ heuristics that typically generate sub-optimal solutions (in terms of the number of exceptional elements). In this paper, we will describe a polynomial-time algorithm, based on a graph-theoretic approach, for optimal cluster formation in a class of {0,1}-matrices called vertex-tree graphic matrices. A comparison of the performance of this algorithm with popular heuristics is also provided. The algorithm can be used as a banchmarking tool for cluster formation heuristics.