Mechanical Sciences
Mechanical Sciences
Mechanical Sciences
Articles | Volume 17, issue 1
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

Viewed

Total article views: 39 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
29 7 3 39 2 2
  • HTML: 29
  • PDF: 7
  • XML: 3
  • Total: 39
  • BibTeX: 2
  • EndNote: 2
Views and downloads (calculated since 25 Jun 2026)
Cumulative views and downloads (calculated since 25 Jun 2026)

Viewed (geographical distribution)

Total article views: 25 (including HTML, PDF, and XML) Thereof 25 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Jul 2026
Download
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.
Read more
Share