Learning to schedule job-shop problems: representation and policy learning using graph neural network and reinforcement learning
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DOI: 10.1080/00207543.2020.1870013
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Cited by:
- Aykut Uzunoglu & Christian Gahm & Axel Tuma, 2024. "A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem," Annals of Operations Research, Springer, vol. 338(1), pages 407-428, July.
- Jie Fang & Yunqing Rao & Qiang Luo & Jiatai Xu, 2023. "Solving One-Dimensional Cutting Stock Problems with the Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(4), pages 1-16, February.
- Kandula, Shanthan & Krishnamoorthy, Srikumar & Roy, Debjit, 2021. "Learning to Play the Box-Sizing Game: A Machine Learning Approach for Solving the E-commerce Packaging Problem," IIMA Working Papers WP 2021-11-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
- Xuan Jing & Xifan Yao & Min Liu & Jiajun Zhou, 2024. "Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 75-93, January.
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