Operator allocation in cellular manufacturing systems by integrated genetic algorithm and fuzzy data envelopment analysis

Allocation of operators is one of the most important factors determining the performance of a cellular manufacturing system (CMS). This paper presents a new integrated method which combines genetic algorithm (GA), simulation, and data envelopment analysis (DEA) in order to select the best operator allocation scenario. The proposed method first generates, by GA, a feasible set of operator allocation scenarios, after which it evaluates the efficiency of those scenarios using DEA with multiple objectives that are extracted from a simulation. The proposed method has two main advantages over the previous DEA-based operator allocation methods. First, whereas the previous methods require the decision maker to predefine all feasible scenarios, the proposed method does not. Second, whereas the previous methods determine only the number of operators assigned to each machine, the proposed method provides not only the number of operators but also additional knowledge on who is assigned to which machine. The results obtained from a practical case study are presented herein to illustrate the effectiveness and superiority of the proposed methodology.