Formation of manufacturing cells by cluster-seeking algorithms

Abstract

This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.