Principal component analysis model for machine-part cell formation problem in group technology

TL;DR

This paper introduces a PCA-based multivariate approach for machine-part cell formation in cellular manufacturing, using correlation analysis and scatter plot clustering to improve efficiency and effectiveness.

Contribution

It presents a novel PCA-based method for machine cell formation, combining correlation analysis and scatter plot clustering, which enhances traditional techniques.

Findings

Effective in forming machine cells with high correlation.

Outperforms some existing methods in efficiency.

Practical and suitable for real-world applications.

Abstract

In this paper, we consider the problem of forming machine cell in cellular manufacturing (CM). The major problem in the design of a CM system is to identify the part families and machine groups and consequently to form manufacturing cells. The aim of this article is to formulate a multivariate approach based on a correlation analysis for solving cell formation problem. The proposed approach is carried out in two phases. In the first phase, the correlation matrix is used as an original similarity coefficient matrix. In the second phase, Principal Component Analysis (PCA) is applied to find the eigenvalues and eigenvectors on the correlation similarity matrix. A scatter plot analysis as a cluster analysis is applied to make machine groups while maximizing correlation between elements. A numerical example for the design of cell structures is provided in order to illustrate the proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical