A Multivariate Simultaneous Part Family Identification & Machine Part Cell Formation

Abstract

A multivariate approach based on correlation analysis to solve optimal cell formation problem of group technology in which exceptional machines and parts are considered. 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 eigen values and eigen vectors 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 Extending the proposed approach to identify the role of part family in lean manufacturing system, role of design conformance in lean manufacturing system, role of GT Management & automated factor in lean manufacturing systems direction is our interesting research perspective. The outline of the paper is as follows: Section1 describes the introduction, Section 2 describes literature review, Section 3 describes exiting methods manufacturing CF problem which use similarity coefficients approach, CF problem with exceptional machines and parts and performance criteria. Then the proposed approach is presented in Section 4, give presentation for each proposed methodology phase. Lastly, conclusion is made in Section 5.