Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems-Reference-Cited by-同舟云学术

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Federated deep reinforcement learning for dynamic job scheduling in cloud-edge collaborative manufacturing systems

Published:2024-03-15 Issue: Volume: Page:1-20
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Wang Xiaohan¹²,Zhang Lin¹³,Wang Lihui²,Vincent Wang Xi²^ORCID,Liu Yongkui⁴^ORCID

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University, Beijing, People's Republic of China

2. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

3. State Key Laboratory of Intelligent Manufacturing System Technology, Beijing, People's Republic of China

4. School of Mechano-Electronic Engineering, Xidian University, Xi'an, People's Republic of China

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2328116

Reference44 articles.

1. Deep Reinforcement Learning for Dynamic Flexible Job Shop Scheduling with Random Job Arrival

2. Deng Yuyang Mohammad Mahdi Kamani and Mehrdad Mahdavi. 2020. “Adaptive Personalized Federated Learning.” Preprint arXiv:2003.13461.

3. Duan Zhuoxi Yufei Qiao Sheng Chen Xinying Wang Guoliang Wu and Xiaofei Wang. 2022. “Lightweight Federated Reinforcement Learning for Independent Request Scheduling in Microgrids.” In 2022 IEEE International Conference on Smart Internet of Things (SmartIoT) 208–215. Suzhou: IEEE.

4. Distributed scheduling problems in intelligent manufacturing systems

5. Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System

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