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- W4386736747 abstract "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)." @default.
- W4386736747 created "2023-09-15" @default.
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- W4386736747 date "2023-01-01" @default.
- W4386736747 modified "2023-09-27" @default.
- W4386736747 title "Deep Q Network Method for Dynamic Job Shop Scheduling Problem" @default.
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- W4386736747 doi "https://doi.org/10.1007/978-3-031-43524-9_10" @default.
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