Integrated use of fuzzy c-means and fuzzy k-nearest neighbours for group technology part family and machine cell formation

This paper presents a new approach for GT part family and machine cell formation. It involves the integrated use of two fuzzy clustering algorithms: fuzzy c-means and fuzzy k-nearest neighbours. It is shown that the proposed approach performs better than using fuzzy c-means alone or FACT (Kamal and Burke) in terms of some commonly used measures such as grouping efficacy, grouping index, number of voids, number of exceptional elements, and number of bottleneck machines. The approach is developed a result of our quest for a better clustering algorithm to process non-binary data and to produce a non-binary classification in the domain of GT applications. These features are deemed important to handle imprecise data and to provide a higher degree of flexibility in the operation stage.