Abstract | ||
---|---|---|
Group Technology (GT) is a useful tool in manufacturing systems. Cell formation (CF) is a part of a cellular manufacturing system that is the implementation of GT. It is used in designing cellular manufacturing systems using the similarities between parts in relation to machines so that it can identify part families and machine groups. Spectral clustering had been applied in CF, but, there are still several drawbacks to these spectral clustering approaches. One of them is how to get an optimal number of clusters/cells. To address this concern, we propose a spectral clustering algorithm for machine-part CF using minimum dissimilarities distance. Some experimental examples are used to illustrate its efficiency. In summary, the proposed algorithm has better efficiency to be used in CF with a wide variety of machine/part matrices. |
Year | DOI | Venue |
---|---|---|
2017 | 10.1007/978-3-319-59060-8_13 | ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT II |
Keywords | Field | DocType |
Spectral clustering, Group technology, Cell formation, Minimum dissimilarity, Number of cells, Grouping efficacy | Cell formation,Cluster (physics),Spectral clustering,Spectral clustering algorithm,Pattern recognition,Cellular manufacturing,Computer science,Matrix (mathematics),Manufacturing systems,Group technology,Artificial intelligence | Conference |
Volume | ISSN | Citations |
10246 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yessica Nataliani | 1 | 47 | 4.00 |
Miin-Shen Yang | 2 | 2025 | 129.12 |