基于阶段图注意力网络的出动离场作业调度方法

张勇; 朱纪洪; 李常久; 苏析超; 杨赟杰

doi:10.12382/bgxb.2025.0869

您当前的位置：

首页 >

文章列表页 >

基于阶段图注意力网络的出动离场作业调度方法

更新时间：2026-07-06

- 基于阶段图注意力网络的出动离场作业调度方法
- ASGAN-based Scheduling Approach for Aircraft Sortie Operation
- 兵工学报 2026年47卷第6期页码：250869
- 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：
  
  通信作者邮箱：jiu.haha@hrbeu.edu.cn
- 基金信息：
- DOI：10.12382/bgxb.2025.0869
  中图分类号： O157
- 收稿：2025-09-22，
  
  网络首发：2026-04-10，
  
  纸质出版：2026-06
- 稿件说明：
移动端阅览
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]. 兵工学报, 2026,47(6):250869.

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

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

references

郭放，韩维，刘玉杰，等. 基于可变作业流程的舰载机机务勤务保障作业调度[J].航空学报，2025，46（13）:531195.

GUO F，HAN W，LIU Y J，et al. Scheduling for maintenance and service support of carrier-based aircraft based on variable operation process [J]. Acta Aeronautica et Astronautica Sinica，2025，46（13）:531195.（in Chinese）

GUO X Y,LI J X,WANG H,et al. Recognition of carrier-based aircraft flight deck operations based on dynamic graph[J]. Chinese Journal of Aeronautics,2025,38（3）:103256.

薛均晓，孔祥燕，董博威，等. 航母甲板上舰载机的混合避障和仿真[J].系统仿真学报，2023，35（3）:592-603.

XUE J X，KONG X Y，DONG B W，et al. Obstacle avoidance and simulation of carrier-based aircraft on the deck of aircraft carrier [J]. Journal of System Simulation，2023，35（3）: 592-603.（in Chinese）

饶运清，彭灯，杜冰，等. 超边界约束条件下异形件排样问题的求解算法研究[J]. 计算机集成制造系统，2023，29（12）:4063-4072.

RAO Y Q，PENG D，DU B，et al. Algorithm for solving irregular parts packing problem with beyond boundary constraint [J]. Computer Integrated Manufacturing System，2023，29（12）:4063-4072.（in Chinese）

徐柱国，余明晖，吴靳，等. 基于基本空间组合关系的舰载机机库布列算法[J].中国舰船研究，2021，16（3）:9-16.

XU Z G，YU M H，WU J，et al. Layout algorithm for carrier aircraft in hangar based on basic spatial combination relationship [J]. Chinese Journal of Ship Research，2021，16（3）: 9-16.（in Chinese）

司维超，韩维，史玮韦. 基于PSO算法的舰载机舰面布放调度方法研究[J].航空学报，2012，33（11）:2048-2056.

SI W C，HAN W，SHI W W. Research on deck-disposed scheduling method of carrier planes based on PSO algorithm [J]. Acta Aeronautica et Astronautica Sinica，2012，33（11）:2048-2056.（in Chinese）

WANG X W,LIU J,SU X C,et al. A review on carrier aircraft dispatch path planning and control on deck[J]. Chinese Journal of Aeronautics,2020,33（12）:3039-3057.

WANG X W, PENG H J, LIU J, et al. Optimal control based coordinated taxiing path planning and tracking for multiple carrier aircraft on flight deck [J]. Defence Technology, 2022, 18（2）:238-248.

WANG X W,LI B,SU X C,et al. Autonomous dispatch trajectory planning on flight deck: a search-resampling-optimization framework[J]. Engineering Applications of Artificial Intelligence, 2023,119:105792.

LIU J, DONG X Z, WANG X W, et al. A homogenizationplanning-tracking method to solve cooperative autonomous motion control for heterogeneous carrier dispatch systems[J]. Chinese Journal of Aeronautics,2021,35（9）:293-305.

LIU J, HAN W, WANG X W, et al. Research on cooperative trajectory planning and tracking problem for multiple carrier aircraft on the deck [J]. IEEE Systems Journal,2019,14（2）:3027-3038.

LIU J,HAN W,PENG H J,et al. Trajectory planning and tracking control for towed carrier aircraft system [J]. Aerospace Science and Technology,2019,84:830-838.

王政，王华，崔可可，等. 局部引导强化学习的舰载机自主调运方法[J].航空学报，2025，46（13）:318-331.

WANG Z，WANG H，CUI K K，et al. Locally guided reinforcement learning for autonomous dispatching of carrier-based aircraft [J]. Acta Aeronautica et Astronautica Sinica，2025，46（13）:531333.（in Chinese）

WANG X W,DENG Z L,LI H X,et al. Safe dispatch corridor:toward efficient trajectory planning for carrier aircraft traction system on flight deck[J]. IEEE Transactions on Aerospace and Electronic Systems,2025,61（2）:1997-2010.

WU Y,QU X J. Path planning for taxi of carrier aircraft launching [J]. Science China Technological Sciences, 2013, 56: 1561-1570.

QI C,WANG D. Dynamic aircraft carrier flight deck task planning based on HTN[J]. IFAC-PapersOnLine,2016,49（12）:1608-1613.

CUI J P,WU Y,SU X C,et al. A task allocation model for a team of aircraft launching on the carrier[J]. Mathematical Problems in Engineering,2018,2018:7920806.

万兵，韩维，梁勇，等. 舰载机出动离场调度优化算法[J].系统工程与电子技术，2021，43（12）:3624-3634.

WAN B，HAN W，LIANG Y，et al. Optimization algorithm of carrier-based aircraft sortie departure scheduling [J]. Systems Engineering and Electronics，2021，43（12）: 3624-3634.（in Chinese）

LIU Z X, HAN W, WU Y, et al. Automated sortie scheduling optimization for fixed-wing unmanned carrier aircraft and unmanned carrier helicopter mixed fleet based on offshore platform[J]. Drones,2022,6（12）:375.

DENG Z L, LIU X B, DOU Y Q, et al. Autonomous sortie scheduling for carrier aircraft fleet under towing mode [J]. Defence Technology,2025,43:1-12.

JIN X, DUAN Z T, SONG W, et al. Container stacking optimization based on deep reinforcement learning [J]. Engineering Applications of Artificial Intelligence, 2023, 123:106508.

LI Y X,LI X Y, GAO L, et al. Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation [J]. Robotics and Computer-Integrated Manufacturing,2025,91:102834.

WAN L J,CUI X Y,ZHAO H X,et al. A novel method for solving dynamic flexible job-shop scheduling problem via DIFFormer and deep reinforcement learning [J]. Computers & Industrial Engineering,2024,198:110688.

HUANG J P,GAO L,LI X Y,et al. A novel priority dispatch rule generation method based on graph neural network and reinforcement learning for distributed job-shop scheduling [J]. Journal of Manufacturing Systems,2023,69:119-134.

ZHAO Y J, LUO X C, ZHANG Y L. The application of heterogeneous graph neural network and deep reinforcement learning in hybrid flow shop scheduling problem[J]. Computers& Industrial Engineering,2024,187:109802.

WAN L J,FU L,LI C Y,et al. Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network [J]. Knowledge-Based Systems, 2024, 296:111940.

SONG W,CHEN X Y,LI Q Q,et al. Flexible job-shop scheduling via graph neural network and deep reinforcement learning [J]. IEEE Transactions on Industrial Informatics,2022,19（2）:1600-1610.

ZHANG L, FENG Y, XIAO Q G, et al. Deep reinforcement learning for dynamic flexible job shop scheduling problem considering variable processing times [J]. Journal of Manufacturing Systems,2023,71:257-273.

WU J W, LIU Y. A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-reentrant hybrid flow shop scheduling problem [J]. Engineering Applications of Artificial Intelligence,2025,140:109688.

LEI K, GUO P, ZHAO W C, et al. A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem [J]. Expert Systems with Applications, 2022, 205:117796.

DU Y,LI J Q,LI C D,et al. A reinforcement learning approach for flexible job shop scheduling problem with crane transportation and setup times[J]. IEEE Transactions on Neural Networks and Learning Systems,2022,35（4）:5695-5709.

WANG X H,ZHANG L,LIU Y K,et al. Solving task scheduling problems in cloud manufacturing via attention mechanism and deep reinforcement learning [J]. Journal of Manufacturing Systems,2022,65:452-468.

HAN B A,YANG J J. Research on adaptive job shop scheduling problems based on dueling double DQN[J]. IEEE Access,2020, 8:186474-186495.

GAMMELLI D,YANG K D,HARRISON J,et al. Graph neural network reinforcement learning for autonomous mobility-ondemand systems:arXiv:2104.11434 [R/OL]. Ithaca,NY,US:Cornell University, 2021（2021-08-16）. https: //arxiv. org/abs/2104.11434.

LI R, GONG W Y, WANG L, et al. Double DQN-based coevolution for green distributed heterogeneous hybrid flowshop scheduling with multiple priorities of jobs[J]. IEEE Transactions on Automation Science and Engineering, 2024, 21（4）:6550-6562.

浏览量

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于迭代式师生训练的协同区域覆盖算法

融合深度强化学习的制导控制一体化自抗扰控制策略

Safe-DRL：无人平台安全深度强化学习决策算法

基于深度强化学习与非线性模型预测方法的四足机器人运动控制

小口径三维枪管系统瞬态热效应及其与热偏关系