Machine-cell and part-family formation in cellular manufacturing using a two-phase clustering algorithm

Yan, Z; Wang, J; Fan, J

Machine-cell and part-family formation in cellular manufacturing using a two-phase clustering algorithm

Yan, Z

Wang, J Fan, J

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Publication Type:: Conference Proceeding
Citation:: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 2605 - 2610
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100	en_US
dc.contributor.author	Wang, J	en_US
dc.contributor.author	Fan, J	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, 19 pp. 2605 - 2610	en_US
dc.identifier.isbn	9783902823625	en_US
dc.identifier.issn	1474-6670	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121303
dc.description.abstract	© IFAC. This paper presents a two-phase clustering algorithm for machine-cell and part-family formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-family and machine-cell formation using the obtained initial cluster centers. The two-phase algorithm can remedy the problem of clustering inconsistency resulting from the fuzzy C-means method with random initializations. Experimental results on many benchmark data sets based on multiple performance criteria substantiate the effectiveness of the proposed algorithm.	en_US
dc.relation.ispartof	IFAC Proceedings Volumes (IFAC-PapersOnline)	en_US
dc.title	Machine-cell and part-family formation in cellular manufacturing using a two-phase clustering algorithm	en_US
dc.type	Conference Proceeding
utslib.citation.volume	19	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	19	en_US

Abstract:

© IFAC. This paper presents a two-phase clustering algorithm for machine-cell and part-family formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-family and machine-cell formation using the obtained initial cluster centers. The two-phase algorithm can remedy the problem of clustering inconsistency resulting from the fuzzy C-means method with random initializations. Experimental results on many benchmark data sets based on multiple performance criteria substantiate the effectiveness of the proposed algorithm.

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http://hdl.handle.net/10453/121303