A Multi-objective Genetic Algorithm for Optimization of Cellular Manufacturing System

Abstract

Cellular manufacturing (CM) is an important application of group technology (GT) in which families of parts is produced in manufacturing cells or in a group of various machines. In this paper, a genetic algorithm approach is proposed for solving multi-objective cell formation problem. The objectives are the minimization of both total moves (intercell as well as intracell moves) and the cell load Variation. In this paper, authors used a SPEA-II method as well known and efficient standard evolutionary multi-objective optimization technique. This hybrid method presents the large set of non-dominance solutions for decision makers to making best solution. The efficiency of multi-objective GA-SPEA II is illustrated on a large-sized test problem taken from the literature.