A Basic Study on Cell Formation with Fuzzy Linguistics

Abstract

Cellular manufacturing is an application of group technology to manufacturing, and is a well-known strategy for reducing lead times in batch production systems. In essence, most of the group technology methods related to cell formation require crisp data for parts' and machines' attributes and hence dichotomously force parts and machines to fall into the classification scheme. However, in many situations, the parts' and machines' attributes are too obscure and are too complex to be susceptible to analysis by traditional methods of group technology. In such cases, this information will be described well using phrases in a natural language. Recently, Gill, et al. have proposed an approach, based on fuzzy linguistics, to deal with data quantification and similarity measure for part-family formation problems which are traditionally addressed through conventional binary coding structures. However, they only proposed the part-part similarity measure. In this paper, we newly add a method for making the part-family formation, part-machine similarity measure, and clustering method to allot the machines to the part-families. In addition, a performance measure and algorithm for finding the best cell formation are also proposed.