Faculty Publications

Performance Of Selected Part‐Machine Grouping Techniques For Data Sets Of Wide Ranging Sizes And Imperfection

Document Type

Article

Keywords

Decision Support Systems and Plant Design

Journal/Book/Conference Title

Decision Sciences

Volume

25

Issue

4

First Page

515

Last Page

539

Abstract

This study addresses the part‐machine grouping problem in group technology, and evaluates die performance of several cell formation methods for a wide range of data set sizes. Algorithms belonging to four classes are evaluated: (1) array‐based methods: bond energy algorithm (BEA), direct clustering analysis (DCA) and improved rank order clustering algorithm (ROC2); (2) non‐hierarchical clustering method: ZODIAC; (3) augmented machine matrix methods: augmented p‐median method (APM) and augmented linear clustering algorithm (ALC); and (4) neural network algorithms: ART1 and variants: ART1/KS, ART1/KSC, and Fuzzy ART. The experimental design is based on a mixture‐model approach, utilizing replicated clustering. The performance measures include Rand Index and bond energy recovery ratio, as well as computational requirements for various algorithms. Experimental factors include problem size, degree of data imperfection, and algorithm tested. The results show that, among the algorithms applicable for large, industry‐size data sets, ALC and neural networks are superior to ZODIAC, which in turn is generally superior to array‐based methods of ROC2 and DCA. Copyright © 1994, Wiley Blackwell. All rights reserved

Department

Department of Management

Original Publication Date

1-1-1994

DOI of published version

10.1111/j.1540-5915.1994.tb01858.x

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