关键词: 柔性作业车间分批调度问题, 改进灰狼优化算法, 流体模型, 自适应邻域搜索

Abstract: Aiming at the Flexible Job shop Batch Scheduling Problem (FJBSP) with the goal of minimizing the makespan, an Improved Grey Wolf Optimization (IGWO) algorithm was proposed to optimize the job batching scheme. For the first time, the fluid model was applied to solve FJBSP through proposing a decoding method based on fluid model to get a better batch scheduling scheme. Secondly, the hierarchical system of the wolf pack was improved to avoid premature convergence of the algorithm. Thirdly, a new crossover method that adapts to the variable length code was designed to deeply exchange the batch information between two individuals, and it can enhance the search ability and stability of algorithm. Then, a self-adaptive grey wolf wandering strategy was proposed to dynamically update the individual′s wandering rate,and it can balance the search quality and convergence speed of algorithm. In addition, the self-adaptive neighborhood search was used on the leader wolves to dynamically adjust the selection probability of each neighborhoods for each kind of job and improve the local search ability of the algorithm. Finally, nine examples and three sets of experiments in total were designed to verify the effectiveness and superiority of the proposed IGWO algorithm.

Key words: Flexible Job shop Batch Scheduling Problem (FJBSP), Improved Grey Wolf Optimization (IGWO) algorithm, fluid model, self-adaptive neighborhood search