An efficient hybrid meta-heuristic approach for cell formation problem

Abstract

The cellular manufacturing technology, an application of group technology in manufacturing, has been a widely studied combinatorial optimization problem where the entire production system is divided into many cells and part families. In this paper, a novel clonal selection algorithm (CSA) that uses a new affinity function and part assignment heuristic for solving a multi-objective cell formation problem is studied. The proposed CSA has been hybridized with genetic algorithm for generating feasible cell sequences that fulfill both mutual exhaustivity and exclusion properties of machine cells prior to the initial population generation. Additionally, a new part assignment heuristic function that maps parts to machine cells and a novel basic affinity function have been built into the proposed CSA so that it can act as the utility function to solve the multi-objective cell formation problem. This hybrid CSA (HCSA) has been presented and computational results have been obtained for the proposed scheme with a set of 52 benchmark instances collected from literature. The results presented herein demonstrate that overall proposed HCSA is much more promising in comparison with existing approaches available in recent literatures. Extensive statistical and convergence tests have been carried out to ratify the superiority of the proposed HCSA. The improvements can be attributed to the collaborative interactions in the CSA mechanism, the proposed hybridization for initial population generation and so forth.