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- W3152192037 abstract "In the last few decades, dynamic job scheduling problems (DJSPs) has received more attention from researchers and practitioners. However, the potential of reinforcement learning (RL) methods has not been exploited adequately for solving DJSPs. In this work deep Q-network (DQN) model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs. The DQN model is trained based on a discrete event simulation experiment. The model is tested by comparing the trained DQN model against two popular dispatching rules, shortest processing time and earliest due date. The obtained results indicate that the DQN model has a better performance than these dispatching rules." @default.
- W3152192037 created "2021-04-13" @default.
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- W3152192037 creator A5065594133 @default.
- W3152192037 date "2020-12-14" @default.
- W3152192037 modified "2023-10-06" @default.
- W3152192037 title "Deep Q-Network Model for Dynamic Job Shop Scheduling Problem Based on Discrete Event Simulation" @default.
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- W3152192037 doi "https://doi.org/10.1109/wsc48552.2020.9383986" @default.
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