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An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data
Abraham P. George, , Soumyadip Ghosh
Published in
Volume: 22
Issue: 1-2
Pages: 125 - 133
In this paper, the problem of clustering machines into cells and components into part-families with the consideration of ratio-level and ordinal-level data is dealt with. The ratio-level data is characterized by the use of workload information obtained both from per-unitprocess times and production quantity of components, and from machine capacity. In the case of ordinal-level data, we consider the sequence of operations for every component. These data sets are used in place of conventional binary data for arriving at clusters of cells and part-families. We propose a new approach to cell formation by viewing machines, and subsequently components, as 'points' in multi-dimensional space, with their coordinates defined by the corresponding elements in a Machine-Component Incidence Matrix (MCIM). An iterative algorithm that improves upon the seed solution is developed. The seed solution is obtained by formulating the given clustering problem as a Traveling Salesman Problem (TSP). The solutions yielded by the proposed clustering algorithm are found to be good and comparable to those reported in the literature.
About the journal
JournalInternational Journal of Advanced Manufacturing Technology
Open AccessNo
Concepts (11)
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    Boolean algebra
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    Graph theory
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    Group technology
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    Iterative methods
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    Mathematical programming
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    Matrix algebra
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    Clustering algorithms
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    Cellular manufacturing