ASGAN-based Scheduling Approach for Aircraft Sortie Operation

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

doi:10.12382/bgxb.2025.0869

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ASGAN-based Scheduling Approach for Aircraft Sortie Operation

更新时间：2026-07-06

- ASGAN-based Scheduling Approach for Aircraft Sortie Operation
- Acta Armamentarii Vol. 47, Issue 6, Pages: 250869(2026)
- 作者机构：
  
  清华大学精密仪器系，北京 100084
  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，
  
  Published：2026-06
- 稿件说明：
移动端阅览
ZHANG Yong, ZHU Jihong, LI Changjiu, et al. ASGAN-based Scheduling Approach for Aircraft Sortie Operation[J]. Acta Armamentarii, 2026, 47(6): 250869.
DOI：

ZHANG Yong, ZHU Jihong, LI Changjiu, et al. ASGAN-based Scheduling Approach for Aircraft Sortie Operation[J]. Acta Armamentarii, 2026, 47(6): 250869. DOI： 10.12382/bgxb.2025.0869.

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 aircrafts for takeoff is critical for enhancing the operational effectiveness of aircraft carriers. The traditional scheduling algorithms have difficulty in ensuring the quality of optimization when dealing with complex process sequencing and resource allocation. Therefore

an intelligent scheduling approach using a staged graph attention network (SGAN) is introduced. The sortie scheduling problem is formulated as a Markov decision process for the interaction between the scheduling agent and the departure environment

and a scheduling agent is designed

which leverages a graph attention network to extract key features from a disjunctive graph that represents the scheduling enviroment. The scheduling agent makes staged decisions for operation selection and resource assignment and is trained using the proximal policy optimization algorithm. The proposed approach significantly improves scheduling performance compared to the conventional dispatching rules

reducing the average makespan by approximately 19% . With a decision time of approximately 0.1 seconds

the proposed approach demonstrates real-time decision-making capability and excellent generalization

offering a robust solution for dynamic sortie operation scheduling.

关键词

Keywords

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