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Evolutionary Trainer-Based Deep Q-Network for Dynamic Flexible Job-Shop Scheduling

教练 计算机科学 作业车间调度 进化计算 调度(生产过程) 进化算法 流水车间调度 人工智能 分布式计算 工业工程 数学优化 运筹学 工程类 操作系统 数学 地铁列车时刻表
作者
Yun Liu,Fangfang Zhang,Yanan Sun,Mengjie Zhang
出处
期刊:IEEE Transactions on Evolutionary Computation [Institute of Electrical and Electronics Engineers]
卷期号:29 (3): 749-763 被引量:12
标识
DOI:10.1109/tevc.2024.3367181
摘要

Dynamic flexible job shop scheduling (DFJSS) aims to achieve the optimal efficiency for production planning in the face of dynamic events. In practice, deep Q-network (DQN) algorithms have been intensively studied for solving various DFJSS problems. However, these algorithms often cause moving targets for the given job-shop state. This will inevitably lead to unstable training and severe deterioration of the performance. In this paper, we propose a training algorithm based on genetic algorithm to efficiently and effectively address this critical issue. Specifically, a state feature extraction method is first developed, which can effectively represent different job shop scenarios. Furthermore, a genetic encoding strategy is designed, which can reduce the encoding length to enhance search ability. In addition, an evaluation strategy is proposed to calculate a fixed target for each job-shop state, which can avoid the parameter update of target networks. With the designs, the DQNs could be stably trained, thus their performance is greatly improved. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art peer competitors in terms of both effectiveness and generalizability to multiple scheduling scenarios with different scales. In addition, the ablation study also reveals that the proposed algorithm can outperform the DQN algorithms with different updating frequencies of target networks.
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