Simultaneous grouping of parts and machines with an integrated fuzzy clustering method
An integrated fuzzy clustering method that is capable of simultaneous classification of parts and their processing machines in a manufacturing system is presented. The cores of the proposed method are two existing fuzzy clustering algorithms: fuzzy c-means (Bezdek) and fuzzy k-nearest neighbors (Keller et al., Trans. Systems, Man, Cybernet. 15 (4) (1985) 580). It is shown that the proposed approach performs better than using fuzzy c-means alone or FACT (Kamal and Burke, Int. J. Prod. Res. 34 (4) (1996) 919) in terms of some commonly used measures such as grouping efficacy. The influences of distance measure, number of training data, and number of neighbors on the performance were also investigated. Test results show that: (1) overall the weighted distance performs better than the Euclidean distance; (2) for the weighted distance, as the density of the machine-part data structure decreases, the number of training data should be increased; and (3) the number of neighbors has a minimal impact on the result. (C) 2002 Elsevier Science B.V. All rights reserved.
fuzzy clustering weighted distance cellular manufacturing performance measure