An application of fuzzy clustering to manufacturing cell design

Abstract

There are several methods and techniques for manufacturing cell design. Many clustering techniques have focused on operations that have been made on part-machine matrix. Some methods have used array-based techniques while others have used similarity coefficient or distance criteria in order to determine clusters. It has been seen from the recent researches, solutions may be changeable related to using algorithms. In order to investigate the applicability of several techniques it is necessary to obtain in comparison with each other. Artificial intelligence technologies are commonly in use in clustering problems as other manufacturing issues. In this study, fuzzy logic approach is studied in design of part families and machine cells simultaneously. The aim at this study is to compare manufacturing cell design which made of fuzzy clustering algorithm (Fuzzy C-Means) with the crisp methods. It has been seen from the result of the study, fuzzy clustering solutions may be efficient than the crisp method for the selected data sets.