A differential evolution approach for machine cell formation

This paper presents a new approach based on differential evolution algorithms to solve cell formation problems. The proposed approach handles the problem in a way of data clustering and can form machine cells and part families concurrently. Differential evolution is simple to implement and has fewer parameters needed to set. The proposed approach applies differential evolution to find machine cluster centers and part cluster centers at the same time. A number of test problems had been selected from literature and the experimental results reveal that the proposed approach is able to solve cell formation problems effectively.