Hybrid Beluga Whale Optimization Algorithm for Flexible Job Shop Scheduling Problem

In response to the flexible job-shop scheduling problem (FJSP), this paper proposes a hybrid beluga whale optimization algorithm (HBWO) to solve it, with the objective of minimizing the maximum completion time. Firstly, the standard beluga whale optimization algorithm (BWO) is improved with existing strategies to accelerate its convergence speed. Secondly, a two-level encoding scheme is designed based on the machine selection and operation sequencing problems to address the discretization issue of FJSP. Then, an active encoding and population initialization strategy is employed to enhance the solution quality. Subsequently, key paths and blocks are determined based on the start and end times of processes, with emphasis on various process time dimensions. The introduction of a greedy approach into the key-path-based hybrid variable neighborhood search strategy is aimed at expanding the exploration of the search space while reducing ineffective searches. Additionally, genetic operators are introduced to prevent the algorithm from being trapped in local optima. Finally, through simulation experiments and analysis on 35 standard instances, the effectiveness of the proposed algorithm in solving the FJSP problem is demonstrated. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.