Numerical taxonomy applied to group technology and plant layout

Carrie, Allan S.

doi:10.1080/00207547308929988

Cited by 305 publications

(68 citation statements)

References 4 publications

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“…The proposal for such a way of proceeding was forwarded, largely independently, by Burbridge [1], McAuley [2], and Carrie [3]. This started an ample literature on the subject.…”

Section: The Domain Fundamental Notions and Issuesmentioning

confidence: 99%

Machine-part grouping and cluster analysis: similarities, distances and grouping criteria

Owsiński¹

2009

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The paper considers the machine-part grouping problem, as equivalent to partitioning the set of machines and operations into subsets, corresponding to block diagonalisation with constraints. The attempts to solve the problem with clustering methods are outlined. The difficulties encountered are presented, related to (i) ambiguity of formulations; (ii) selection of criteria; and (iii) lack of effective algorithms. These are illustrated in more detail with a limited survey of similarity and distance definitions, and of criteria used, constituting the main body of the paper. The return is proposed to the basic paradigm of cluster analysis, as providing simple and fast algorithms, which, even if not yielding optimal solutions, can be controlled in a simple manner, and their solutions improved.

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“…The proposal for such a way of proceeding was forwarded, largely independently, by Burbridge [1], McAuley [2], and Carrie [3]. This started an ample literature on the subject.…”

Section: The Domain Fundamental Notions and Issuesmentioning

confidence: 99%

Machine-part grouping and cluster analysis: similarities, distances and grouping criteria

Owsiński¹

2009

Bulletin of the Polish Academy of Sciences: Technical Sciences

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“…It uses machine part incident matrix in the form of 0's and 1's as input and provide results in block diagonalized shape. McAuley [45], Carrie [41], and Miltenburg and Zhang [46] used clustering techniques in their models. In similarity coefficient approach similarity among parts is developed based on their design and manufacturing attributes and on the basis of these similarities grouping is carried out.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Javaid

Tariq

Hussain

2014

Advances in Mechanical Engineering

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Though there are a number of benefits associated with cellular manufacturing systems, its implementation (identification of part families and corresponding machine groups) for real life problems is still a challenging task. To handle the complexity of optimizing multiple objectives and larger size of the problem, most of the researchers in the past two decades or so have focused on developing genetic algorithm (GA) based techniques. Recently this trend has shifted from standard GA to hybrid GA (HGA) based approaches in the quest for greater effectiveness as far as convergence on to the optimum solution is concerned. In order to prove the point, that HGAs possess better convergence abilities than standard GAs, a methodology, initially based on standard GA and later on hybridized with a local search heuristic (LSH), has been developed during this research. Computational experience shows that HGA maintains its accuracy level with increase in problem size, whereas standard GA looses its effectiveness as the problem size grows.

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“…In CF problem the later technique is more useful than the other two. Similarity coefficient approach reported to specify the similarity among parts (Carrie, 1973;Offodile, 1991;Kini, 1991). The part families based on the cells identified after identification of the machine-cells (Witte, 1980;Seifoddini & Wolfe, 1987).…”

Section: Introductionmentioning

confidence: 99%

Application of principal component analysis in machine-part cell formation

Chattopadhyaya¹,

Mazumdar²,

Dan³

et al. 2012

MSL

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The present work applies Principal Component Analysis (PCA) for grouping of machines and parts so that the part families could be processed in the cells formed by those associated machines. An incidence matrix with binary entries has been chosen to apply this methodology. After performing the eigenanalysis of the principal component and observing the component loading plot of the principal components, the machine groups and part families are identified and arranged to form machine-part cells. Later, the same methodology is extended and it is applied to nine other machine-part matrices collected from literature for the validation of the proposed methodology. The goodness of cell formation is compared using the grouping efficacy and the potential of eigenanalysis in cell formation is established over the best available results using the various established methodologies. The result shows that in 70% of the problems there is an increase in grouping efficacy and in 30% problem, the performance measure of cell formation is as good as the best result from literature.

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Numerical taxonomy applied to group technology and plant layout

Cited by 305 publications

References 4 publications

Machine-part grouping and cluster analysis: similarities, distances and grouping criteria

Machine-part grouping and cluster analysis: similarities, distances and grouping criteria

A Comparison of a Standard Genetic Algorithm with a Hybrid Genetic Algorithm Applied to Cell Formation Problem

Application of principal component analysis in machine-part cell formation

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