Application of principal component analysis in machine-part cell formation
| Autor: | Manojit Chattopadhyaya, Sitanath Mazumdar, Pranab K Dan, Partha Sarathi Chakraborty |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Measure (data warehouse)
Principal Component Analysis Group Technology lcsh:HF5735-5746 Principal (computer security) Machine-Part Cell Cell formation Incidence matrix Eigen Analysis lcsh:Business records management computer.software_genre General Business Management and Accounting Plot (graphics) grouping efficacy Group technology Component (UML) Statistics Principal component analysis Data mining computer Mathematics |
| Zdroj: | Management Science Letters, Vol 2, Iss 4, Pp 1175-1188 (2012) |
| ISSN: | 1923-9343 1923-9335 |
| Popis: | Article history: Received October 1, 2011 Received in Revised form November, 14, 2011 Accepted 15 February 2012 Available online 5 March 2012 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. © 2012 Growing Science Ltd. All rights reserved. |
| Databáze: | OpenAIRE |
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