Abstract: To improve the quality of the optimal scheduling solution set, a quantum particle swarm algorithm with multi-strategy fusion is proposed for the multi-objective fuzzy flexible job shop scheduling problem with fuzzy maximum completion time, fuzzy total machine load, and fuzzy bottleneck machine load as optimization objectives. Chaotic mapping is used to improve the initial population quality, and a Lévy flight strategy is introduced to enhance the algorithm's ability to jump out of the local optimum. The neighborhood search strategy based on machine mutation is designed for local search. Cross operation is used to maintain the diversity of elite individuals, and simulated annealing is combined for the deep optimization search. Considering fuzzy production cost and introducing a multi-indicator weighted grey target decision model to solve the scheduling scheme decision problem. Simulation experiments verify the superiority and effectiveness of the algorithm and decision model.

Key words: fuzzy scheduling, quantum particle swarm optimization, Lévy flight, neighborhood search, simulated annealing, grey target decision model