Abstract: Aiming at the processing time for fuzzy flexible job shop scheduling problem and considering minimizing the fuzzy maximal makespan, fuzzy total workload and fuzzy critical workload as optimization objectives, an effective multiobjective evolutionary algorithm is proposed. A method of mixing different machine allocations and operation sequencing strategies is adopted to generate initial population and a well-designed greedy inserting algorithm is adopted for chromosome decoding. A Pareto dominant relation based on possibility degree and a modified crowding distance measure in decision space are defined and further employed to improve the fast nondominated sorting. Moreover, a novel local search based on fuzzy critical path theory is first incorporated into MOEA. Afterwards, the influence of key parameters is investigated by designing of the Taguchi method of design of experiment. Finally, extensive comparison with three existing algorithms is carried out, and the results demonstrate the effectiveness of the proposed MOEA in solving multiobjective flexible job shop scheduling problem (MOfFJSP).

Key words: multiobjective fuzzy, flexible job shop scheduling, local search, multiobjective evolutionary algorithm, fuzzy critical operation