Landing scheduling for carrier aircraft fleet considering bolting probability and aerial refueling
image: Flight route of carrier aircraft during recovery process under Case III
Credit: Genlai Zhang, et al
With the high risk and complexity of carrier aircraft recovery, reasonable landing scheduling that accounts for special situations is critical to enhancing operational safety and efficiency. To that end, a team led by Associate Professor Xinwei Wang from Dalian University of Technology reported an important research outcome in Defence Technology on carrier aircraft landing scheduling considering bolting probability and aerial refueling.
“Existing research often overlooks key factors like bolting and fuel shortage, leading to inadequate scheduling efficiency and poor adaptability,” says Wang. “In this study we established a combinatorial optimization model with fuel and wake interval constraints, integrating aircraft integrity, task priority, and fuel status into a compound objective function.”
The team designed an improved firefly algorithm (IFA) by incorporating genetic algorithm crossover and mutation operations, significantly reducing computation time. “A dynamic replanning mechanism was also introduced to handle bolter and aerial refueling in real time, enabling millisecond-level adaptive scheduling adjustments,” shares Wang.
Validated through numerical simulations under sufficient and insufficient fuel scenarios, the proposed algorithm outperforms others. Results confirm that considering bolting and aerial refueling reduces recovery time by 5% on average, with the IFA maintaining high efficiency even for large fleets (90 aircraft) and completing dynamic replanning within 52 ms. A simulation animation is available at bilibili.com/video/BV1QprKY2EwD/."
“This research provides valuable theoretical and technical support for optimizing carrier aircraft recovery operations,” says Wang. “We will further refine the model parameters using real flight data and extend the framework to simultaneous sortie and recovery scenarios.”
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Contact the author: Xinwei Wang, Department of Engineering Mechanics, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian University of Technology, Dalian 116024, China. wangxinwei@dlut.edu.cn
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