A Scheduling Framework for Aircraft Sortie Operation Using Staged Graph Attention Network

ZHANG Yong; ZHU Jihong; LI Changjiu; SU Xichao; YANG Yunjie

doi:10.12382/bgxb.2025.0869

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A Scheduling Framework for Aircraft Sortie Operation Using Staged Graph Attention Network

更新时间：2026-04-10

- A Scheduling Framework for Aircraft Sortie Operation Using Staged Graph Attention Network
- Acta Armamentarii (2026)
- 作者机构：
  
  1. 清华大学精密仪器系,北京,100084
  2. 海军航空大学航空作战勤务学院,山东,烟台,264001
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0869
  CLC： O157
- Received：22 September 2025，
  
  Online First：10 April 2026，
- 稿件说明：
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张勇，朱纪洪，李常久，等. 基于阶段图注意力网络的出动离场作业调度方法[J/OL]. 兵工学报, 2026(2026-04-10). https://doi.org/10.12382/bgxb.2025.0869.

ZHANG Y, ZHU J H, LI C, et al. A scheduling framework for aircraft sortie operation using staged graph attention network[J/OL]. Acta Armamentarii, 2026(2026-04-10). https://doi.org/10.12382/bgxb.2025.0869. (in Chinese)
张勇，朱纪洪，李常久，等. 基于阶段图注意力网络的出动离场作业调度方法[J/OL]. 兵工学报, 2026(2026-04-10). https://doi.org/10.12382/bgxb.2025.0869. DOI：

ZHANG Y, ZHU J H, LI C, et al. A scheduling framework for aircraft sortie operation using staged graph attention network[J/OL]. Acta Armamentarii, 2026(2026-04-10). https://doi.org/10.12382/bgxb.2025.0869. (in Chinese) DOI：

摘要

高效的舰载机出动离场调度是提升航母作战能力的关键，传统调度算法在处理复杂的工序排序与资源分配时难以保证优化质量。为此提出一种基于阶段图注意力网络的智能调度方法，将该问题构建为调度智能体与出动离场环境交互的马尔可夫决策过程。所设计的智能体采用双网络架构，集成的图注意力网络从表征调度环境的析取图中高效提取关键特征，分阶段进行工序选择与设备分配决策，并通过近端策略优化算法与仿真环境进行迭代交互以完成训练。实验结果表明，所提算法相较传统调度优先规则显著提升调度性能，平均完工时间缩短约19%，平均单次决策耗时仅约0.1s，具备的实时决策能力与跨规模泛化性能，为舰载机出动离场调度提供了高效决策支持。

Abstract

Efficient scheduling of carrier-based aircraft sorties is critical for the operational effectiveness of aircraft carriers

yet traditional algorithms face challenges with the problem's complexity.Weintroducean intelligent scheduling approach using a staged graph attention network within a deep reinforcement learning framework.We formulate the problem as a Markov decision process and design a scheduling agent that leverages a graph attention network to extract features from a disjunctive graph representation. The scheduling agent makes staged decisions for operation selection and resource assignment and is trained using proximal policy optimization. Experiments show our approach significantly improves scheduling performance compared to conventional dispatching rules

reducing the averagemakespanby approximately 19%. With a decision time of just ~0.1 seconds

our approach demonstrates real-time capability and excellent generalization

offering a robust solution for dynamic sortie operation scheduling.

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references

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