Cell formation using fuzzy relational clustering algorithm

• Published in: Mathematical and computer modelling Vol. 53; no. 9; pp. 1776 - 1787
• Main Authors: Hung, Wen-Liang, Yang, Miin-Shen, Lee, E. Stanley
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.05.2011; Elsevier
• Subjects: Cell formation; Cellular manufacturing systems; Dissimilarity matrix; Exact sciences and technology; FRC algorithm; Mathematical analysis; Mathematics; Methods of scientific computing (including symbolic computation, algebraic computation); Mixed-variable data; Numerical analysis. Scientific computation; Sciences and techniques of general use; Cellular manufacturing systems; Cell formation; Mixed-variable data; Dissimilarity matrix; FRC algorithm; Applied mathematics; System identification; Mathematical model; Algorithm; Computer aided analysis
• ISSN: 0895-7177; 1872-9479
• DOI: 10.1016/j.mcm.2010.12.056

Cellular manufacturing is a useful way to improve overall manufacturing performance. Group technology is used to increase the productivity for manufacturing high quality products and improving the flexibility of manufacturing systems. Cell formation is an important step in group technology. It is used in designing good cellular manufacturing systems. The key step in designing any cellular manufacturing system is the identification of part families and machine groups for the creation of cells that uses the similarities between parts in relation to the machines in their manufacture. There are two basic procedures for cell formation in group technology. One is part-family formation and the other is machine–cell formation. In this paper, we apply a fuzzy relational data clustering algorithm to form part families and machine groups. A real data study shows that the proposed approach performs well based on the grouping efficiency proposed by Chandrasekharan and Rajagopalan.