FAG-scheduler: Privacy-Preserving Federated Reinforcement Learning with GRU for Production Scheduling on Automotive Manufacturing
Document Type
Conference Proceeding
Publication Title
Proceedings - IEEE Global Communications Conference, GLOBECOM
Abstract
The automotive manufacturing industry faces challenges in production planning, but current heuristic algorithms and solvers have limitations in scalability and local optima. Moreover, data security concerns are often overlooked. To address these issues, this paper introduces the FAG-Scheduler, a federated reinforcement learning approach integrating asynchronous advantage actor-critic, gated recurrent unit algorithms, and federated learning. By sharing model parameters instead of raw data, data security is ensured among participants. The FAG-Scheduler achieves optimal solutions in under 5 seconds and demonstrates high adaptability to other manufacturing contexts. It presents potential applications with significant improvements over conventional methods.
First Page
5147
Last Page
5152
DOI
10.1109/GLOBECOM54140.2023.10437362
Publication Date
1-1-2023
Keywords
Automotive Manufacturing Optimization, Federated Reinforcement Learning, Job Shop Scheduling Problem, Privacy-Preserving
Recommended Citation
J. Chen et al., "FAG-scheduler: Privacy-Preserving Federated Reinforcement Learning with GRU for Production Scheduling on Automotive Manufacturing," Proceedings - IEEE Global Communications Conference, GLOBECOM, pp. 5147 - 5152, Jan 2023.
The definitive version is available at https://doi.org/10.1109/GLOBECOM54140.2023.10437362