Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
January 25, 2026Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture

Dynamic scheduling of human-machine collaborative flexible job shops based on deep reinforcement learning

View Full Paper
Ask AI
Bookmark
Share

Authors

XRXiaohui RenUniversity of Science and Technology BeijingNZN. ZhaoUniversity of Science and Technology Beijing

Discussion

Loading...

Member takes

Implication

Demonstrates improved scheduling outcomes in flexible job shops, suggesting enhanced efficiency in intelligent manufacturing.

Key Points

  • The aim is to develop a dynamic scheduling method that incorporates human factors for flexible job shops using deep reinforcement learning.
  • Proposed a scheduling method based on a multi-proximal policy optimization algorithm.
  • Integrated hybrid prioritized experience replay to enhance training efficiency.
  • Constructed three independent actor networks for parallel learning of job selection, equipment allocation, and worker assignment.
  • The proposed method outperforms traditional scheduling rules and heuristic algorithms in efficiency.
  • Achieved better results across various scale test cases compared to other deep learning algorithms.

Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/6975b306feba4585c2d6e8f3https://doi.org/10.1177/09544054251406569
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Solving the flow-shop scheduling problem with human factors and two competing agents with deep reinforcement learning2024 · 10 citations
  2. 2Deep reinforcement learning for dynamic scheduling of a flexible job shop2022 · 307 citations
  3. 3Green flexible job shop integrated scheduling optimization for machines and AGVs based on the INSGA-II algorithm2025 · 8 citations