Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

Tong Guo, Yi Mei, Wenbo Du, Yisheng Lv, Yumeng Li*, Tao Song

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

The thriving advances in autonomous vehicles and aviation have enabled the efficient implementation of aerial last-mile delivery services to meet the pressing demand for urgent relief supply distribution. Variable Neighborhood Search (VNS) is a promising technique for aerial emergency scheduling. However, the existing VNS methods usually exhaustively explore all considered neighborhoods with a prefixed order, leading to an inefficient search process and slow convergence speed. To address this issue, this paper proposes a novel graph neural neighborhood search algorithm, which includes an online reinforcement learning (RL) agent that guides the search process by selecting the most appropriate low-level local search operators based on the search state. We develop a dual-graph neural representation learning method to extract comprehensive and informative feature representations from the search state. Besides, we propose a reward-shaping policy learning method to address the decaying reward issue along the search process. Extensive experiments conducted across various benchmark instances demonstrate that the proposed algorithm significantly outperforms the state-of-the-art approaches. Further investigations validate the effectiveness of the newly designed knowledge guidance scheme and the learned feature representations.

源语言英语
期刊IEEE Transactions on Artificial Intelligence
DOI
出版状态已接受/待刊 - 2025

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引用此

Guo, T., Mei, Y., Du, W., Lv, Y., Li, Y., & Song, T. (已接受/印刷中). Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2025.3528381