Deep Q Network Method for Dynamic Job Shop Scheduling Problem
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 771 LNNS, Page: 137-155
2023
- 2Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Nowadays, rule-based heuristic methods for scheduling planning in production environments are commonly used, but their effectiveness is heavily dependent on expert domain expertise. In this manner, decision-making performance cannot be assured, nor can the dynamic scheduling demand in the job-shop production environment be met. Therefore, Dynamic Job Shop Scheduling Problems (DJSSPs) have received increased interest from researchers in recent decades. However, the development of reinforcement learning (RL) approaches for solving DJSSPs has not been fully realized. In this paper, we used Deep Reinforcement Learning (DRL) approach on DJSSP to minimize the Makespan. A Deep Q Network (DQN) algorithm is designed with state features, actions, and rewards. Finally, the performance of the proposed solution is compared to other algorithms and benchmark research using two categories of benchmark instances. The empirical results show that the proposed DRL approach outperforms other DRL methods and dispatching rules (heuristics).
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85174437189&origin=inward; http://dx.doi.org/10.1007/978-3-031-43524-9_10; https://link.springer.com/10.1007/978-3-031-43524-9_10; https://dx.doi.org/10.1007/978-3-031-43524-9_10; https://link.springer.com/chapter/10.1007/978-3-031-43524-9_10
Springer Science and Business Media LLC
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