A self-learning discrete salp swarm algorithm based on deep reinforcement learning for dynamic job shop scheduling problem

• Published in: Applied intelligence (Dordrecht, Netherlands) Netherlands), 2023-08, Vol.53 (15), p.18925-18958
• Main Authors: Gu, Yiming, Chen, Ming, Wang, Liang
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Computer Science; Deep learning; Exploitation; Job shop scheduling; Job shops; Machine learning; Machines; Manufacturing; Mechanical Engineering; Partitioning; Processes; Strategy
• ISSN: 0924-669X; 1573-7497
• DOI: 10.1007/s10489-023-04479-7

For the dynamic job shop scheduling problem (DJSP), an improved variant of salp swarm algorithm (SSA) named self-learning discrete salp swarm algorithm (SLDSSA) is proposed to minimize makespan. The primary intentions are to enhance SSA’s exploration, exploitation, diversity, and dynamic balance of exploration and exploitation. SLDSSA benefits from three new improvement strategies: hybrid initialization, discrete position update strategy, and self-learning population partitioning mechanism. The hybrid initialization significantly improves the overall quality of the initial population. The proposed discrete update strategy enhances the exploration and exploitation capability of the algorithm. The self-learning population partitioning mechanism achieves a dynamic balance of exploration/exploitation rate according to the population state. The SlDSSA algorithm is tested on 25 test functions, 27 job shop benchmark instances , and composite DJSP instances to evaluate the performance of SLDSSA. Furthermore, the results of SLDSSA are compared with 13 existing algorithms. The results show that the SLDSSA algorithm can provide competitive results to the comparative algorithms, effectively solve job shop scheduling problems and deal with the interference caused by dynamic events.