Curriculum-learning-driven hierarchical multi-agent deep reinforcement learning for collaborative scheduling in complex supply chain networks

Dong, Jingya; Zhao, Han; Zhao, Suyi; Wang, Yijie; Guo, Mengfan; Song, Chunhe; Xu, Mingliang

doi:10.5194/ms-17-671-2026

Articles | Volume 17, issue 1

https://doi.org/10.5194/ms-17-671-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ms-17-671-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 17, issue 1

Research article

|

25 Jun 2026

Research article |

| 25 Jun 2026

Curriculum-learning-driven hierarchical multi-agent deep reinforcement learning for collaborative scheduling in complex supply chain networks

Jingya Dong, Han Zhao, Suyi Zhao, Yijie Wang, Mengfan Guo, Chunhe Song, and Mingliang Xu

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Short summary

Modern supply chains must coordinate customer orders, factory choices, and delivery routes while conditions change quickly. This study developed a learning-based scheduling method that breaks the whole task into connected decisions and trains them step by step. Tests in simulated networks showed faster learning, shorter completion times, and better performance in unfamiliar settings. The results suggest a practical way to improve coordination in production and delivery systems.