关键词: 柔性作业车间, 最大完工时间, 蝙蝠算法（BA）, 正反向学习, 变邻域搜索（VNS）

Abstract: In view of the disadvantages of heuristic intelligent algorithm bat algorithm, which is easy to fall into local optimization and insufficient optimization ability, an improved discrete bat algorithm is proposed with the goal of minimizing the maximum completion time. Firstly, the paper combines the selection of local minimum time machines and random selection machines to initialize the population, improving the quality and diversity of the initial population. Secondly, from the perspective of process arrangement and machine selection, selection, superposition, crossover operators, and forward and reverse learning operations are designed to improve the position update mechanism, and use six neighborhood structure operations based on operation arrangement and machine selection to optimize the variable neighborhood search strategy and enhance the algorithm’s ability for global and local search. Finally, the experimental simulation results of benchmark examples show that the improved discrete bat algorithm has better optimal performance.

Key words: flexible job shop, maximum completion time, bat algorithm (BA), positive and negative learning, variable neighborhood search (VNS)