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Acta Aeronautica et Astronautica Sinica ›› 2025, Vol. 46 ›› Issue (1): 330615.doi: 10.7527/S1000-6893.2024.30615

• Electronics and Electrical Engineering and Control • Previous Articles     Next Articles

Adaptive batch matching decision method for carrier-based aircraft support operations

Guang LIU1,2, Hua WANG3,4,5, Youfang LIN1,2, Shuo HE3,4,5, Yafei LI3,4,5(), Mingliang XU3,4,5   

  1. 1.School of Computer and Information Technology,Beijing Jiaotong University,Beijing 100091,China
    2.Beijing Key Laboratory of Traffic Data Analysis and Mining,Beijing 100044,China
    3.School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China
    4.Engineering Research Center of Intelligent Swarm Systems,Ministry of Education,Zhengzhou 450001,China
    5.National Supercomputing Center in Zhengzhou,Zhengzhou 450001,China
  • Received:2024-04-28 Revised:2024-05-20 Accepted:2024-06-20 Online:2025-01-15 Published:2024-07-12
  • Contact: Yafei LI E-mail:ieyfli@zzu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(62372416);Henan Natural Science Foundation(242300421215);National Key Research and Development Program of China(2021YFB3301504)

Abstract:

The key indicator for measuring the combat performance of an aircraft carrier is the sortie rate of carrier-based aircraft, which depends on the support station matching strategy of carrier-based aircraft. Existing works mainly use sequence matching and batch matching methods to match suitable stations for carrier-based aircraft. However, both methods have certain limitations, and it is difficult for the methods to ensure both real-timeliness and quality of station matching at the same time. Facing the complex and time-varying support environment, it becomes extremely difficult to determine a reasonable support operation matching strategy. In this paper, we propose a novel adaptive batch matching decision-making method for carrier-based aircraft support operations based on the batch matching method. First, the optimal time window division strategy is solved by constructing a reinforcement learning method for multi-dimensional environmental state encoding. Then, a highly efficient batch matching algorithm is applied within each time window to find the best matching solution for support operations and support stations. The results of multiple sets of simulation experiments based on the publicly available Nimitz aircraft carrier data show that our proposed method can effectively respond to dynamic changes in the support environment, and can quickly solve high-quality support operation assignment plans while meeting real-time requirements.

Key words: carrier-based aircraft, support operation, real-time scheduling, reinforcement learning, adaptive decision

CLC Number: