Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search

作者：Tong Guo, Yi Mei, Wenbo Du, Yisheng Lv, Yumeng Li, Tao Song · 发表于：IEEE Transactions on Artificial Intelligence · 年份：2025 · DOI：10.1109/tai.2025.3528381 · 被引用次数：12 · 研究领域：Vehicle Routing Optimization Methods、Robotic Path Planning Algorithms、Smart Parking Systems Research

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.