@article{CHEN2025, 
author = {Wei CHEN and Lulu LI and Dong CHEN and Shaohui ZHANG and Yafei LI and Ke WANG and Yuanyuan JIN and Mingliang XU},
title = {Multi-aircraft cooperative decision-making methods driven by differentiated support demands for carrier-based aircraft},
year = {2025},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {46},
number = {13},
keywords = {carrier-based aircraft, deep reinforcement learning, graph neural networks, scheduling optimization, resource allocation},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2024.31274},
doi = {10.7527/S1000-6893.2024.31274},
abstract = {In modern naval warfare, the effectiveness of carrier-borne aircraft operations is crucial, and multi-type carrier-borne aircraft formations have become the fundamental operational paradigm for aircraft carriers. However, collaborative decision making for carrier-borne aircraft support faces significant challenges due to differentiated support processes, deck space limitations, and complex maintenance procedures. To address these scheduling challenges, we propose a novel Dependency-Aware Task Scheduling Decision Module (DATSDM). By leveraging graph neural networks, DATSDM delves into the intricate network structure of support processes, enabling efficient and precise scheduling of support resources across varying scales and multi-type carrier-borne aircraft clusters. Furthermore, DATSDM incorporates the strengths of transformer, harnessing its attention mechanism to parallelly analyze and process carrier-borne aircraft support information, thereby significantly reducing support times. Extensive experiments demonstrate the remarkable superiority of DATSDM over its peers. In various resource allocation scenarios for multiple carrier-borne aircraft types, DATSDM reduces support time by 13.36%. This improvement significantly enhances the overall support efficiency and combat readiness of multi-type carrier-borne aircraft.}
}