| 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.