Manufacturing resource combinatorial optimization for large complex equipment in group manufacturing: A cluster-based genetic algorithm

To address manufacturing resource combinatorial optimization (MRCO) for large complex equipment in group manufacturing and provide high-quality manufacturing resource, a multi-objective optimization model and heuristic approach are investigated in this study. First, considering the extensive manufacturing resource, a four-tuple model with use-cost, trading period, credibility and resource consumption for manufacturing resource clustering is proposed, as well as MRCO model associated with the four indicators. Then, an improved cluster-based genetic algorithm (CGA) has been carried out with a fuzzy c-means algorithm (FCM) for manufacturing resource clustering. The proposed algorithm uses a fuzzy analytic hierarchy process (FAHP) to successfully determine the priority weights of multi-objective. Finally, a case study and comparison of performances with simple genetic algorithm (SGA) and non-cluster-based genetic algorithm (Non-CGA) are presented. Experimental results show the proposed method is more preferable in optimal solution searching and more efficient as the dimension of problem increases.