Home Journals Articles Updates About
CN
image
收藏切换
Gate Assignment Based on Deep Reinforcement Learning
收藏切换
PDF
Zheng XIANG, Qiu-yue WU, Tong CHU, Yi-yang YUE
Science Technology and Engineering | 2025, 25(16) : 6977 - 6984
Less
收藏切换
Science Technology and Engineering | 2025, 25(16): 6977-6984
• Papers·Aeronautics and Astronautics •
Gate Assignment Based on Deep Reinforcement Learning
Full
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
Outline
收藏切换

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
Appendix
Less
Year 2025 volume 25 Issue 16
PDF
423
182
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2405560
  • Receive Date：2024-07-24
  • Online Date：2025-07-09
  • Published：2025-06-08
Article Data
Affiliations
History
  • Received：2024-07-24
  • Revised：2025-03-10
Funding
Affiliations
    College of Air Traffic Management, Civil Aviation Flight University of China, Deyang 618000, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2405560
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House