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Gate Assignment Based on Deep Reinforcement Learning
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Zheng XIANG, Qiu-yue WU, Tong CHU, Yi-yang YUE
Science Technology and Engineering | 2025, 25(16) : 6977 - 6984
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Science Technology and Engineering | 2025, 25(16): 6977-6984
• Papers·Aeronautics and Astronautics •
Gate Assignment Based on Deep Reinforcement Learning
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Zheng XIANG, Qiu-yue WU, Tong CHU, Yi-yang YUE
Affiliations
  • College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618000, China
Published: 2025-06-08 doi: 10.12404/j.issn.1671-1815.2405560
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A systematic study was conducted on the issue of gate assignment, with the goal of minimizing the number of remote gate assignments and the idle time of near gates. A multi-objective mathematical model was proposed to address the multi-objective and multi-constraint characteristics of the problem. The model was designed to minimize the number of remote gate assignments and the idle time of near gates while taking into account parameters such as actual flight arrival and departure times, aircraft types, and the interrelationships among gates. The gate assignment process was optimized using the deep reinforcement learning method, specifically the deep deterministic policy gradient(DDPG) algorithm. To enhance the optimization ability and performance of the algorithm, an improved DDPG algorithm was developed by incorporating prioritized experience replay and multi-strategy exploration mechanisms. Comparative experiments were conducted, and the results show that the improved algorithm significantly reduces the number of remote gate assignments and optimized time utilization. The algorithm also achieves faster convergence and stronger global optimization capabilities, confirming its effectiveness.

gate assignment  /  deep-learning  /  reinforcement-learning  /  deep deterministic policy gradient (DDPG)algorithm
Zheng XIANG, Qiu-yue WU, Tong CHU, Yi-yang YUE. Gate Assignment Based on Deep Reinforcement Learning[J]. Science Technology and Engineering, 2025 , 25 (16) : 6977 -6984 . DOI: 10.12404/j.issn.1671-1815.2405560
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Year 2025 volume 25 Issue 16
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doi: 10.12404/j.issn.1671-1815.2405560
  • Receive Date：2024-07-24
  • Online Date：2025-07-09
  • Published：2025-06-08
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  • Received：2024-07-24
  • Revised：2025-03-10
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    College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618000, China
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