Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications

Group technology (GT) is a useful way to increase productivity with high quality in flexible manufacturing systems. Cell formation (CF) is a key step in GT. It is used to design a good cellular manufacturing system that uses the similarity measure between parts and machines so that it can identify part families and machine groups. Recently, fuzzy clustering has been applied in GT because the fuzzy clustering algorithm can present partial memberships for part-machine cells so that it is suitably used in cellular manufacturing systems for a variety of real cases. However, these fuzzy clustering algorithms are only used for numeric data of parts and machines. In this paper, we apply a mixed-variable fuzzy clustering algorithm, called mixed-variable fuzzy c-means (MVFCM), to CF in GT. According to real application examples, the MVFCM algorithm gives good results when it is applied to the mixed-variable types of CF data.