Open Access
Issue |
JNWPU
Volume 42, Number 4, August 2024
|
|
---|---|---|
Page(s) | 764 - 773 | |
DOI | https://doi.org/10.1051/jnwpu/20244240764 | |
Published online | 08 October 2024 |
- Civil Aviation Administration of China. 2021 Civil Aviation Industry Development Statistical Bulletin[EB/OL]. (2022-05-18)[2023-04-19]. [Article] (in Chinese) [Google Scholar]
- TANG Z X, HUANG S, ZHU X P, et al. Research on the multilayer structure of flight delay in China air traffic network[J]. Physica A: Statistical Mechanics and its Applications, 2023(609): 128309 [NASA ADS] [CrossRef] [Google Scholar]
- NORIN A, GRANBERG T A, VRBRAND P, et al. Integrating optimization and simulation to gain more efficient airport logistics[C]//The 8th USA/Europe Air Traffic Management Research and Development Seminar, 2009 [Google Scholar]
- BO J, XIAO Y, FEI Q Z. Airport ground services considering work delays[C]//International Conference on Management Science and Safety Engineering, 2010: 632–635 [Google Scholar]
- LIU C, CHEN Y R, CHEN F H, et al. Sliding window change point detection based dynamic network model inference framework for airport ground service process[J]. Konwledge-Based Systems, 2022(238): 107701 [CrossRef] [Google Scholar]
- DU Y, QIAN Z, CHEN Q. ACO-IH: an improved ant colony optimization algorithm for airport ground service scheduling[C]//IEEE International Conference on Industrial Technology, 2008 [Google Scholar]
- PADRÓN S, GUIMARANS D, RAMOS J J, et al. A bi-objective approach for scheduling ground-handling vehicles in airports[J]. Computers & Operations Research, 2016, 71: 34–53 [CrossRef] [Google Scholar]
- HENG Hongjun, QI Xintong. Dynamic refueling vehicle scheduling considering task balance[J]. Computer Engineering & Science, 2020, 42(5): 923–930 (in Chinese) [Google Scholar]
- ZHU S R, SUN H J, GUO X. Cooperative scheduling optimization for ground-handling vehicles by considering flights' uncertainty[J]. Computers & Industrial Engineering, 2022, 169: 1–14 [Google Scholar]
- VOLODYMYR M, KORAY K, DAVID S, et al. Human-level control through deep reinforcement learning[J]. Nature, 2015, 518: 529–533 [NASA ADS] [CrossRef] [Google Scholar]
- LI J W, MA Y N, GAO R Z, et al. Deep reinforcement learning for solving the heterogeneous capacitated vehicle routing problem[J]. IEEE Trans on Cybernetics, 2022, 52: 13572–13585 [CrossRef] [Google Scholar]
- Civil Aviation Administration of China. Airline flight normal operation standard[EB/OL]. (2020-01-16)[2023-04-19]. [Article] (in Chinese) [Google Scholar]
- THANH T N, NGOC D N, PETER V, et al. A multi-objective deep reinforcement learning framework[J]. Engineering Applications of Artificial Intelligence, 2020(96): 103915 [Google Scholar]
- HSDDELT H, GUEZ A, SILVER D. Deep reinforcement learning with double Q-learning[C]//Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2015: 2094–2100 [Google Scholar]
- HENG Hongjun, YAN Xiaodong, WANG Fang, et al. Research on dynamic scheduling of airport fuel filling vehicles[J]. Computer Engineering and Design, 2017, 38(5): 1382–1388 (in Chinese) [Google Scholar]
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