Distributional reinforcement learning with the independent learners for flexible job shop scheduling problem with high variability. Issue 4 (6th July 2022)

• Record Type: Journal Article
• Authors: Oh, Seung Heon; Cho, Young In; Woo, Jong Hun
• Is Part Of: Journal of computational design and engineering. Volume 9:Issue 4(2022)
• Page Start: 1157
• Page End: 1174
• Publication Date: 2022-07-06
• Subjects: variability -- flexible job shop -- implicit quantile networks -- independent learners; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwac044
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: Multi-agent scheduling algorithm is a useful method for the flexible job shop scheduling problem (FJSP). Also, the variability of the target system has to be considered in the scheduling problem that includes the machine failure, the setup change, etc. This study proposes the scheduling method that combines the independent learners with the implicit quantile network by modeling of the FJSP with high variability to the form of the multi-agent. The proposed method demonstrates superior performance compared to the several known heuristic dispatching rules. In addition, the trained model exhibits superior performance compared to the reinforcement learning algorithms such as proximal policy optimization and deep Q-network. Graphical Abstract: