- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | MOU Qifeng, ZHANG Yi, WU Yuankai. Dynamic Gate Assignment Method for Improving Flight Bridge Rate[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250069
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Flight bridge rate is a key indicator for evaluating aviation service quality and airport operational efficiency. To improve the flight bridge rate, an optimized dynamic gate assignment method based on existing operational workflows and towing conditions was proposed. By arranging the full-day gate assignment plan in stages, and comprehensively considering constraints such as parking time limits for towed flights, towing eligibility conditions, and towing gate type restrictions, a gate assignment model taking the improvement of the flight bridge rate as the core objective was constructed. The number of flights assigned to contact gates was optimized by the model based on the towing penalty cost to maximize the overall efficiency. Meanwhile, to address the problem that the traditional pre-flight gate assignment is difficult to adapt to dynamic changes in flight operations, a multi-stage dynamic assignment algorithm based on an improved genetic algorithm was designed. In this algorithm, a continuous assignment mechanism was adopted to lock the completed assignment results, and the gate assignment for newly added flights was continuously optimized. Finally, simulation experiments were carried out using the actual operational data of a busy large-scale airport in Southwest China to verify the effectiveness of the proposed model and method. The results show that compared with the traditional gate assignment model without considering towing, the model considering the towing strategy increases the flight bridge rate by 1.1%; on this basis, compared with the traditional genetic algorithm, the adopted multi-stage dynamic assignment method increases the flight bridge rate by 4.0% and decreases the flight towing frequency by 16.6%. The proposed method can effectively balance the assignment of contact gates and aircraft towing costs, further optimize the flight bridge rate on the premise of controllable towing costs, and provide a feasible optimization method for the improvement of the flight bridge rate and dynamic gate assignment at airports.
| [1] |
Liu Z Y, Xiang Q J. A branch-and-price algorithm for the airport gate assignment problem considering the trade-off between robustness and efficiency[J]. Transportation Research Part C: Emerging Technologies, 2023, 154: 104232. doi: 10.1016/j.trc.2023.104232
|
| [2] |
Skorupski J, Żarów P. Dynamic management of aircraft stand allocation[J]. Journal of Air Transport Management, 2021, 90: 101964. doi: 10.1016/j.jairtraman.2020.101964
|
| [3] |
Li H N, Wu X, Ribeiro M, et al. Deep reinforcement learning approach for real-time airport gate assignment[J]. Operations Research Perspectives, 2025, 14: 100338. doi: 10.1016/j.orp.2025.100338
|
| [4] |
Du W H, Qu H. Research on the apron operation control system in Chinese airport[J]. Journal of Frontiers of Society, Science and Technology, 2021, 1(3): 53-56.
|
| [5] |
刘颖俪, 胡明华, 苏佳明, 等. 基于多目标优化的机场机坪管制区域划分方法研究[J]. 中国安全生产科学技术, 2023, 19(8): 150-156. doi: 10.11731/j.issn.1673-193x.2023.08.022
Liu Yingli, Hu Minghua, Su Jiaming, et al. Division method of airport apron control areas based on multi-objective optimization[J]. Journal of Safety Science and Technology, 2023, 19(8): 150-156. doi: 10.11731/j.issn.1673-193x.2023.08.022
|
| [6] |
Hagspihl T, Kolisch R, Fontaine P, et al. Apron layout planning–Optimal positioning of aircraft stands[J]. Transportation Research Part B: Methodological, 2024, 179: 102854. doi: 10.1016/j.trb.2023.102854
|
| [7] |
Jiang Y, Wang Y S, Hu Z T, et al. Airport gate assignment problem with harbor constraints based on Branch-and-Price algorithm[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 176: 103192. doi: 10.1016/j.tre.2023.103192
|
| [8] |
Marinelli M, Dell’orco M, Sassanelli D. A metaheuristic approach to solve the flight gate assignment problem[J]. Transportation Research Procedia, 2015, 5: 211-220. doi: 10.1016/j.trpro.2015.01.013
|
| [9] |
王超, 任云鸿. 面向节油减排的平行多跑道混合运行机场停机位分配模型[J]. 交通信息与安全, 2021, 39(5): 144-152.
Wang Chao, Ren Yunhong. A model of gate allocation for parallel multi-runway hybrid operation from the perspective of fuel-saving and carbon emission reduction[J]. Journal of Transport Information and Safety, 2021, 39(5): 144-152.
|
| [10] |
Zhang C L, Jin Z Y, Ng K K H, et al. Predictive and prescriptive analytics for robust airport gate assignment planning in airside operations under uncertainty[J]. Transportation Research Part E: Logistics and Transportation Review, 2025, 195: 103963. doi: 10.1016/j.tre.2025.103963
|
| [11] |
Benlic U, Burke E K, Woodward J R. Breakout local search for the multi-objective gate allocation problem[J]. Computers & Operations Research, 2017, 78: 80-93. doi: 10.1016/j.cor.2016.08.010
|
| [12] |
Deng W, Sun M, Zhao H M, et al. Study on an airport gate assignment method based on improved ACO algorithm[J]. Kybernetes, 2018, 47(1): 20-43. doi: 10.1108/K-08-2017-0279
|
| [13] |
Karsu ö, Azizoğlu M, Alanlı K. Exact and heuristic solution approaches for the airport gate assignment problem[J]. Omega, 2021, 103: 102422. doi: 10.1016/j.omega.2021.102422
|
| [14] |
Dell’orco M, Marinelli M, Altieri M G. Solving the gate assignment problem through the Fuzzy Bee Colony Optimization[J]. Transportation Research Part C: Emerging Technologies, 2017, 80: 424-438. doi: 10.1016/j.trc.2017.03.019
|
| [15] |
姜伟华, 王雅莎, 姜雨, 等. 面向港湾机坪的停机位指派优化[J]. 南京航空航天大学学报, 2023, 55(6): 1072-1080. doi: 10.16356/j.1005-2615.2023.06.014
Jiang Weihua, Wang Yasha, Jiang Yu, et al. Optimization of gate assignment with harbor apron[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2023, 55(6): 1072-1080. doi: 10.16356/j.1005-2615.2023.06.014
|
| [16] |
姜雨, 胡志韬, 童楚, 等. 面向航班延误的停机位实时指派优化模型[J]. 交通运输系统工程与信息, 2020, 20(5): 185-190, 217.
Jiang Yu, Hu Zhitao, Tong Chu, et al. An optimization model for gate re-assignment under flight delays[J]. Journal of Transportation Systems Engineering and Information Technology, 2020, 20(5): 185-190,217.
|
| [17] |
盛政, 蔡碧金, 王岩华. 考虑组合机位的停机位预指派问题研究[J]. 武汉理工大学学报(信息与管理工程版), 2015, 37(5): 616-620. doi: 10.3963/j.issn.2095-3852.2015.05.020
Sheng Zheng, Cai Bijin, Wang Yanhua. Gate assignment problem under consideration of combined gate restriction[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2015, 37(5): 616-620. doi: 10.3963/j.issn.2095-3852.2015.05.020
|
| [18] |
马亮, 胡宸瀚, 金福才, 等. 铁水运输调度双层多目标约束优化模型[J]. 西南交通大学学报, 2023, 58(2): 357-366, 397. doi: 10.3969/j.issn.0258-2724.20220008
Ma Liang, Hu Chenhan, Jin Fucai, et al. Double-layer and multi-objective constraint optimization model for transportation scheduling of molten iron[J]. Journal of Southwest Jiaotong University, 2023, 58(2): 357-366,397. doi: 10.3969/j.issn.0258-2724.20220008
|
| [19] |
朱新平, 韩松臣. 过站保障车辆集中式调度的单亲遗传算法[J]. 西南交通大学学报, 2018, 53(2): 406-413. doi: 10.3969/j.issn.0258-2724.2018.02.026
Zhu Xinping, Han Songchen. Centralized scheduling of service vehicles for aircraft turnaround based on partheno-genetic algorithm[J]. Journal of Southwest Jiaotong University, 2018, 53(2): 406-413. doi: 10.3969/j.issn.0258-2724.2018.02.026
|
| [20] |
Li Y J, Clarke J P, Dey S S. Using submodularity within column generation to solve the flight-to-gate assignment problem[J]. Transportation Research Part C: Emerging Technologies, 2021, 129: 103217. doi: 10.1016/j.trc.2021.103217
|
| [21] |
Liang B J, Li Y L, Bi J, et al. An improved adaptive parallel genetic algorithm for the airport gate assignment problem[J]. Journal of Advanced Transportation, 2020, 2020: 8880390. doi: 10.1155/2020/8880390
|
| [22] |
Deng W, Xu J J, Song Y J, et al. Differential evolution algorithm with wavelet basis function and optimal mutation strategy for complex optimization problem[J]. Applied Soft Computing, 2021, 100: 106724. doi: 10.1016/j.asoc.2020.106724
|
| [23] |
Li M J, Hao J K, Wu Q H. Learning-driven feasible and infeasible tabu search for airport gate assignment[J]. European Journal of Operational Research, 2022, 302(1): 172-186. doi: 10.1016/j.ejor.2021.12.019
|
| [24] |
高艺哲. 过夜航班机位分配算法研究[D]. 武汉: 华中科技大学, 2019.
|
| [25] |
Dorndorf U, Jaehn F, Pesch E. Flight gate assignment and recovery strategies with stochastic arrival and departure times[J]. OR Spectrum, 2017, 39(1): 65-93. doi: 10.1007/s00291-016-0443-1
|
| [26] |
Cao F, Tang T Q, Gao Y Q, et al. Predicting flight arrival times with deep learning: a strategy for minimizing potential conflicts in gate assignment[J]. Transportation Research Part C: Emerging Technologies, 2024, 169: 104866. doi: 10.1016/j.trc.2024.104866
|
| [27] |
Jin Z Y, Zhang C L, Ng K K H. An improved variable neighbourhood search for the gate assignment problem with time windows[C]//AIAA Aviation Forum and Ascend 2024. Reston: AIAA, 2024: 1-13.
|
| [28] |
高强, 周覃, 陈欣. 基于波及延误的航班过站松弛时间重分配[J]. 华南理工大学学报(自然科学版), 2019, 47(10): 151-156. doi: 10.12141/j.issn.1000-565X.180471
Gao Qiang, Zhou Qin, Chen Xin. Redistribution method for slack time of flight based on propagated delay[J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(10): 151-156. doi: 10.12141/j.issn.1000-565X.180471
|
| [29] |
中国民用航空局. 民航航班正常统计办法[S]. 北京: 中国民用航空局, 2014.
|
| [30] |
中国民用航空局. 航班安全运行保障标准[S]. 北京: 中国民用航空局, 2020.
|
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