POLAR COORDINATES APPLIED FOR SOLVING PART-MACHINE CELL FORMATION PROBLEM

Abstract

Cell Formation CF is regarded as critical aspect of a Cellular Manufacturing System CMS. It focuses on the creation of part families and machine cells. There are numerous methods, algorithms and models suggested to deal with solving the CF problem. However, there are several drawbacks to some existing CF methods such as, their inability deal with large size datasets, ill structured datasets, the simultaneous creation of clusters of parts and machines, lack of ability to find optimum solutions, and increased material handling costs. Thus, the suggested polar coordinates effectively address the CF problem by clustering parts and machines to reduce the material handling costs. It offers efficient initial clustering, and adapts to different production environments. While not globally optimal, it provides good starting solutions for further optimization, excelling in cost reduction and efficiency. A polar coordinates-based approach combines the repetition of parts and machines with angles and then the resulting pattern is used for checking similarity criteria. The purpose is to improve the Grouping Efficacy GC, decrease the travel time of parts inside and outside cells and enhance Machine Utilization MU. GC is applied as a performance measure to identify the quality of the suggested CF approach. After the polar-based approach is applied, it enhanced the proportion of exceptional elements and machine utilization.  On the other hand, it showed better results for GC when compared with Rank Order Clustering (ROC). For some datasets, it recorded (60, 90) compared with (53.33, 71.93) for the traditional ROC