A fuzzy-genetic algorithm for a re-entrant job shop scheduling problem with sequence-dependent setup times

Job shop scheduling problem (JSP) with sequence-dependent setup time and re-entrant work flows is considered in this paper. This is an NP-hard problem which needs to be solved using (meta)heuristic methods (e.g. genetic algorithm (GA)), especially for relatively large instances. However, the GA may face premature convergence (i.e. converging to a local optima), especially for rough solution spaces. In this paper, a fuzzy genetic algorithm (FGA) is proposed to overcome this issue. The objective is to minimize makespan of such problem. Research results show that the FGA outperforms the standard GA and offers better solutions in the same number of runs.