机场混编摆渡车队高峰小时服务调度优化

doi:10.16097/j.cnki.1009-6744.2026.04.028

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (4): 321-331.DOI: 10.16097/j.cnki.1009-6744.2026.04.028

机场混编摆渡车队高峰小时服务调度优化

马千里^{a, b}，焦乙恩^a，周屹恒^a，贾鹏^{*a, b}

大连海事大学，a. 综合交通运输协同创新中心；b. 航运经济与管理学院，辽宁大连 116026

收稿日期:2026-01-25 修回日期:2026-03-13 接受日期:2026-03-30 出版日期:2026-08-25 发布日期:2026-08-21
作者简介:马千里（1989— ），男，河南许昌人，副教授
基金资助:
国家自然科学基金 (72204034)；中国博士后科学基金 (2024T170083)

Optimization of Peak-Hour Service Scheduling for Airport Mixed Ferry Fleets

MA Qianli^{a, b}, JIAO Yi'en^a, ZHOU Yiheng^a, JIA Peng^{*a, b}

a. Collaborative Innovation Center for Comprehensive Transportation; b. School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, Liaoning, China

Received:2026-01-25 Revised:2026-03-13 Accepted:2026-03-30 Online:2026-08-25 Published:2026-08-21
Supported by:
National Natural Science Foundation of China (72204034); China Postdoctoral Science Foundation (2024T170083)

摘要/Abstract

摘要： 随着机场混合运行模式日益复杂化和不同作业单元能源需求的多样化，优化异构混编摆渡车队调度的重要性日益凸显。本文以最小化总行驶距离为核心目标，构建一个融合车辆特定能源限制与无人车辆临时停靠策略的混合整数线性规划模型，针对异构车队高峰小时路径优化问题，设计量子行为粒子群优化（QPSO）算法以实现高效求解。以北京大兴国际机场典型高峰期运行数据为背景的实例分析表明，量子行为粒子群优化算法在减少车队总行驶距离，保持车辆工作量平衡和提升机场场面整体运行效率方面均显著优于其他启发式算法及传统先到先服务规则，该算法将总行驶距离从625.58 km降低至229.27 km，降幅达63.3%；同时将所需车辆数从55辆减少至35辆，优化比例为36.4%，充分体现了该算法在收敛速度与求解精度方面的显著优势。

关键词: 智能交通, 摆渡服务调度, 量子行为粒子群优化, 机场综合枢纽, 地面交通智能网联

Abstract: As the airport mixed operations grow increasingly complex and the energy demands across various operational units become more diverse, the importance of optimizing the scheduling of heterogeneous shuttle fleets becomes increasingly prominent. This study constructs a mixed integer linear programming model that integrates vehicle-specific energy constraints and a temporary stopping strategy for unmanned vehicles, with the core objective of minimizing the total travel distance. To address the optimization problem of peak-hour routing for heterogeneous fleets, a Quantum-behaved Particle Swarm Optimization algorithm was designed for efficient solutions. An empirical analysis based on typical peak-hour operational data from Beijing Daxing International Airport demonstrates that the Quantum-behaved Particle Swarm Optimization algorithm significantly outperforms other heuristic algorithms and the traditional First-Come-First-Served rule in reducing total travel distance, balancing vehicle workloads, and enhancing overall airport surface operational efficiency. Specifically, the algorithm reduces the total travel distance from 625.58 kilometers to 229.27 kilometers, with a reduction rate of approximately 63.3%. While it also decreases the required number of vehicles from 55 to 35, with an optimization rate of 36.4%. These results fully demonstrate the notable advantages of the algorithm in convergence speed and solution accuracy.

Key words: intelligent transportation, ferry service scheduling, quantum-behaved particle swarm optimization (QPSO), airport integrated hub, intelligent and connected ground transportation

中图分类号:

U116.2

马千里, 焦乙恩, 周屹恒, 贾鹏. 机场混编摆渡车队高峰小时服务调度优化[J]. 交通运输系统工程与信息, 2026, 26(4): 321-331.

MA Qianli, JIAO Yi'en, ZHOU Yiheng, JIA Peng. Optimization of Peak-Hour Service Scheduling for Airport Mixed Ferry Fleets[J]. Journal of Transportation Systems Engineering and Information Technology, 2026, 26(4): 321-331.

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参考文献

[1] HOERWICK M, SIEDERSBERGER K. Strategy and architecture of a safety concept for fully automatic and autonomous driving assistance systems[C]//2010 IEEE Intelligent Vehicles Symposium, 2010: 955-960.

[2] MATIJEVIĆ L. General variable neighborhood search for electric vehicle routing problem with time-dependent speeds and soft time windows[J]. International Journal of Industrial Engineering Computations, 2023, 14(2): 275- 293.

[3] 中国民航网. 解读《中国民航四型机场建设行动纲要（2020—2035年)》[EB/OL]. (2020-01-09) [2026-01- 18]. http://www.caacnews.com.cn/1/1/202001/t20200109_ 1289739.html. [China Civil Aviation News. Interpreting the action plan for building four- type airports in China (2020-2035) [EB/OL]. (2020-01-09)[2026-01- 18]. http://www.caacnews.com.cn/1/1/202001/ t20200109_1289739.html.]

[4] SUN J, DUAN K L, LI X S, et al. Real-time scheduling of autonomous driving system with guaranteed timing correctness[C]// IEEE Real-Time and Embedded Technology and Applications Symposium, 2023: 185- 197.

[5] 陈垚, 柏赟, 张安英, 等. 基于滚动时域优化的共享自动驾驶汽车动态调度方法[J]. 交通运输系统工程与信息，2022, 22(3): 45-52, 73. [CHEN Y, BAI Y, ZHANG A Y, et al. Dynamic fleet management of shared autonomous vehicles with rolling horizon optimization[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(3): 45-52, 73.]

[6] LI Q W, BI J, LI Z Y. Research on ferry vehicle scheduling problem within airport operations[C]//2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017: 248-251.

[7] HAN X, ZHAO P X, KONG D X. Two-stage optimization of airport ferry service delay considering flight uncertainty[J]. European Journal of Operational Research, 2023, 307(3): 1103-1116.

[8] HAN X, ZHAO P X, KONG D X. A bi-objective optimization of airport ferry vehicle scheduling based on heuristic algorithm[J]. Advances in Production Engineering & Management, 2022, 17(2): 183-192.

[9] HAN X, ZHAO P X, MENG Q C, et al. Optimal scheduling of airport ferry vehicles based on capacity network[J]. Annals of Operations Research, 2020, 295 (1): 163-182.

[10] JIN Z, NG K H, WANG H Q, et al. Electric airport ferry vehicle scheduling problem for sustainable operation[J]. Journal of Air Transport Management, 2025: 123, 102711.

[11] 蒋阳升, 叶肖甫, 徐尉耀, 等. 考虑转运与拼车的共享自动驾驶电动汽车动态运营[J]. 交通运输系统工程与信息, 2025, 25(2): 58-68. [JIANG Y S, YE X F, XU W Y, et al. Dynamic operation of autonomous shared electric vehicles considering relay and ridesplitting[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(2): 58-68.]

[12] TIAN Q, CHEN X J, GUO R. Optimization of ground service vehicle scheduling in airports for green and digital operations[J]. Frontiers of Science and Technology of Engineering Management, 2025, 44(2): 25-32.

[13] LIU Y H, WU J J, TANG J, et al. Scheduling optimisation of multi-type special vehicles in an airport[J]. Transportmetrica B: Transport Dynamics, 2022, 10(1): 954-970.

[14] FINK M, DESAULNIERS G, FREY M, et al. Column generation for vehicle routing problems with multiple synchronization constraints[J]. European Journal of Operational Research, 2018, 272(2): 699-711.

[15] BAO D W, ZHANG Z Q, KONG D. Charge-discharge coupling strategy for dispatching problems with electric tractors at airports[J]. Journal of Air Transport Management, 2025, 124: 102733.

[16] TAN Z Y, ZHEN L, YANG Z Y, et al. Multi-period emergency vehicle fleet redistribution and dispatching [J]. Transportmetrica A: Transport Science, 2025, 21(2): 34-34.

[17] LV L, DENG Z, SHAO C, et al. A variable neighborhood search algorithm for airport ferry vehicle scheduling problem[J]. Transportation Research Part C: Emerging Technologies, 2023, 154: 104262.

[18] ZHANG H, SHI F F. A multi-objective site selection of electric vehicle charging station based on NSGA-II[J]. International Journal of Industrial Engineering Computations, 2024, 15(1): 293-306.

[19] CAI C. Research on dynamic dispatching method of ferry bus in hub airport[D]. Harbin: Harbin Institute of Technology, 2021.

[20] LIN H, TANG C. Analysis and optimization of urban public transport lines based on multi-objective adaptive particle swarm optimization[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 16786- 16798.

[21] SUN J, XU W B, BI F. A global search strategy of quantum-behaved particle swarm optimization[C]//IEEE Conference on Cybernetics and Intelligent Systems, 2004: 111-116.

[22] LI T, ZHANG J S, WANG S C, et al. Research on route planning based on quantum-behaved particle swam optimization algorithm[C]//2014 IEEE Chinese Guidance, Navigation and Control Conference, 2014: 335-339.

[23] YUAN Y, HAN Y F, XUE J Y, et al. A dynamic dispatching method in the unmanned airport baggage transportation system[C]//2022 23RD IEEE International Conference on Mobile Data Management, 2022: 50-55.

机场混编摆渡车队高峰小时服务调度优化

Optimization of Peak-Hour Service Scheduling for Airport Mixed Ferry Fleets

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