Formation of machine groups and part families in cellular manufacturing systems using a correlation analysis approach

Wafik Hachicha, Faouzi Masmoudi and Mohamed Haddar

MPRA Paper from University Library of Munich, Germany

Abstract: The important step in the design of a cellular manufacturing (CM) system is to identify the part families and machine groups and consequently to form manufacturing cells. The scope 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 three phases. In the first phase, the correlation matrix is used as 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 simultaneously machine groups and part families while maximizing correlation between elements. In the third stage, an algorithm is improved to assign exceptional machines and exceptional parts using respectively angle measure and Euclidian distance. The proposed approach is also applied to the general Group Technology (GT) problem in which exceptional machines and part are considered. Furthermore, the proposed approach has the flexibility to consider the number of cells as a dependent or independent variable. Two numerical examples for the design of cell structures are provided in order to illustrate the three phases of proposed approach. The results of a comparative study based on multiple performance criteria show that the present approach is very effective, efficient and practical.

Keywords: cellular manufacturing; cell formation; correlation matrix; Principal Component Analysis; exceptional machines and parts (search for similar items in EconPapers)
JEL-codes: L60 (search for similar items in EconPapers)
Date: 2006-10-19, Revised 2007-01-04
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (4)

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