The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network
Abstract
Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.
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Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Deep Reinforcement Learning with Double Q-Learning
Van Hasselt, H., Guez, A., & Silver, D. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10295 · Cited by 3,318
Real-time scheduling for a smart factory using a reinforcement learning approach
Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225
Cited by
Resource Scheduling in Edge Computing: A Survey
Luo, Q., Hu, S., Li, C., Li, G., & Shi, W. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3106401 · Cited by 588
Machine Learning for industrial applications: A comprehensive literature review
Bertolini, M., Mezzogori, D., Neroni, M., & Zammori, F. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114820 · Cited by 553
Deep reinforcement learning in production systems: a systematic literature review
Panzer, M., & Bender, B. (2022) · International Journal of Production Research · doi:10.1080/00207543.2021.1973138 · Cited by 282
Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102412 · Cited by 259
Privacy-Encoded Federated Learning Against Gradient-Based Data Reconstruction Attacks
Liu, H., Li, B., Gao, C., Xie, P., & Zhao, C. (2023) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2023.3309095 · Cited by 27
Metadata
- Authors
- Lin, C.-C., Deng, D.-J., Chih, Y.-L., & Chiu, H.-T.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2019-07
- Volume
- 15
- Issue
- 7
- Pages
- 4276-4284
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2932097000
- Cited by
- 342
- References
- 5
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lin, C.-C., Deng, D.-J., Chih, Y.-L., & Chiu, H.-T. (2019). Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2019.2908210
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