Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

• Published in: Applied soft computing 2014-09, Vol.22, p.528-543
• Main Authors: Chattopadhyay, Manojit, Dan, Pranab K., Mazumdar, Sitanath
• Format: Article
• Language: English
• Subjects: Cellular manufacturing system; Group technology efficiency; Growing hierarchical self-organizing map; Operation sequence; Self-organizing map; Visual clustering
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.04.027

•We model visual clustering of machine-part cell formation using GHSOM model.•We examine the optimal GHSOM map that helps manager to visualize optimum cell formation.•We compare SOM and GHSOM models based on the network architecture and goodness of cell formation to find the efficacy of the performance on a set of 15 benchmarked problems.•GHSOM algorithm concludes as the best model as it improves the GTE performance measure for 75% of the cell formation problems than the SOM model and the other best models from the literature. The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so f