Conference
Open access
2026
LLM-enhanced Dynamic Fleet Planning with Hierarchical Multi-agent Reinforcement Learning Framework
The proposed hierarchical multi-agent proximal policy optimization framework can reduce total airlines' operational costs—including direct operating cost and capital cost and achieves a computation speedup in comparison with a conventional optimization baseline.
Li-Jing Liu, James M. Shihua, Qi-Yu Yan et al.
· MATEC Web of Conferences · 0 citations