Title: Hybrid principal component analysis technique to machine-part grouping problem in cellular manufacturing system

Authors: Tamal Ghosh; Manojit Chattopadhyay; Pranab K. Dan

Addresses: Department of Industrial Engineering, West Bengal University of Technology, BF 142, Sector-1, Salt Lake City, Kolkata 700064, India ' Department of Computer Applications, Pailan College of Management and Technology, Sector-1, Bengal Pailan Park, Joka, Kolkata 700104, India ' Department of Industrial Engineering, West Bengal University of Technology, BF 142, Sector-1, Salt Lake City, Kolkata 700064, India

Abstract: This article portrays a hybrid principal component analysis (PCA)-based technique to construct production cells in cellular manufacturing system (CMS). The key problem in CMS is to recognise the machine cells and corresponding part families and subsequently the formation of production cells. A novel approach is considered in this study to systematise a hybrid multivariate clustering technique based on covariance analysis to form the machine cells in CMS. The intended technique is demonstrated in three segments. Firstly, a similarity matrix is developed by exploiting the covariance analysis procedure. In the second stage, the PCA is utilised to identify the potential clusters in CMS with the assistance of eigenvalue and eigenvector computation. In the last stage, an adjustment heuristic is adopted to improve the solution quality and consequently the clustering efficiency. This article states that, the addition of the adjustment heuristic approach into a traditional multivariate PCA-based clustering technique not only enhances the solution quality significantly, but also downgrades the inconsistency of the solutions achieved. The hybrid technique is tested on 24 test datasets available in published articles and it is shown to outperform other published methodologies by enhancing the solution quality on the test problems.

Keywords: cell formation; cellular manufacturing systems; CMS; covariance analysis; principal component analysis; hybrid PCA; adjustment heuristics; machine-part grouping; manufacturing cells; multivariate clustering.

DOI: 10.1504/IJAOM.2013.055868

International Journal of Advanced Operations Management, 2013 Vol.5 No.3, pp.237 - 260

Received: 10 Sep 2011
Accepted: 17 Jun 2012
Published online: 28 Apr 2014 *

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