Application of Visual Clustering Properties of Self Organizing Map in Machine-part Cell Formation

TL;DR

This paper introduces a visual clustering method using Self Organizing Map for machine-part cell formation in cellular manufacturing, improving clustering accuracy and topology preservation over existing methods.

Contribution

It proposes a novel SOM-based visual clustering approach for machine-part cell formation that optimizes cluster quality and topology preservation, with criteria for selecting the best SOM map size.

Findings

The proposed method achieves superior clustering accuracy on benchmark problems.

It outperforms existing methods in several cases, providing better solutions.

Statistical verification confirms the effectiveness of the approach.

Abstract

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, work-in-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using Self Organizing Map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM map size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.