Movatterモバイル変換

[0]ホーム
URL:

Skip to main content

Advertisement

Springer Nature Link
Log in

A receding horizon control-based holistic ant colony system approach for multi-runway aircraft arrival sequencing and scheduling

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

Aircraft arrival sequencing and scheduling (ASS) is a significant research problem that aims to relieve aircraft congestion in airports. Due to the increasing demand for air transportation and the limitation of runway capacity, effective and efficient scheduling approaches for handling the ASS problem are in great need for most modern airports. Ant colony system (ACS) in evolutionary computation is now commonly used to tackle the ASS problem due to its promising performance. However, most existing ACS-based algorithms are designed to tackle single-runway ASS problems or to tackle multi-runway ASS problems in a separative fashion (e.g., first considering the landing sequence and then considering the runway assignment, which will easily result in local optima). This paper the first time proposes a novel holistic ACS (HACS)-based scheduling approach for effectively solving the multi-runway ASS problem by scheduling the sequencing and the runways simultaneously. The proposed approach follows the local memetic feature of ASS that very late arrived aircraft are not likely to be scheduled to land very early, so as to divide the ASS problem into a set of subproblems using a receding horizon control technique and then to optimize each subproblem through the HACS algorithm. The advantage of HACS is that it can figure out the runway assignment of the aircraft in each receding horizon window as well as their landing sequence simultaneously in one stage, which is a global view to obtain the global optimal solution rather than the separative ACS algorithm that is easily trapped to local optima. Instances with different scales and different congestion modes are adopted to comprehensively evaluate the performance of the HACS approach. The experimental results show the superiority of HACS, especially in the large-scale, congested mode, and in scheduling environments with more runways.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Guo XY, Zeng Z, Li MX, Fu S (2022) Simulation of aircraft cabin evacuation strategy based on exit flow equilibrium. Int J Simul Modell 21(2):261–272

    Article MATH  Google Scholar 

  2. Tan X, Sun Y, Zeng W, Quan Z (2022) Congestion recognition of the air traffic control sector based on deep active learning. Aerospace 9(6):302.https://doi.org/10.3390/aerospace9060302

    Article MATH  Google Scholar 

  3. Messaoud MB, Ghedira K, Kefi M (2018) Detailed mathematical programming formulations for the aircraft landing problem on a single and multiple runway configurations. Proced Comput Sci 126:345–354

    Article MATH  Google Scholar 

  4. Rodríguez-Sanz Á, Comendador FG, Valdés RA, Pérez-Castán J, Montes RB, Serrano SC (2019) Assessment of airport arrival congestion and delay: prediction and reliability. Transp Res Part C: Emerg Technol 98:255–283

    Article  Google Scholar 

  5. Hong Y, Choi B, Kim Y (2019) Two-stage stochastic programming based on particle swarm optimization for aircraft sequencing and scheduling. IEEE Trans Intell Transp Syst 20(4):1365–1377

    Article MATH  Google Scholar 

  6. Ji XP, Cao XB, Tang K (2016) Sequence searching and evaluation: a unified approach for aircraft arrival sequencing and scheduling problems. Memet Comput 8:109–123

    Article MATH  Google Scholar 

  7. Jung S, Hong S, Lee K (2019) A data-driven air traffic sequencing model based on pairwise preference learning. IEEE Trans Intell Transp Syst 20(3):803–816

    Article MATH  Google Scholar 

  8. Bennell JA, Mesgarpour M, Potts CN (2017) Dynamic scheduling of aircraft landings. Eur J Oper Res 258(1):315–327

    Article MathSciNet MATH  Google Scholar 

  9. Zhou Z, Chen J, Liu Y (2021) Optimized landing of drones in the context of congested air traffic and limited vertiports. IEEE Trans Intell Transp Syst 22(9):6007–6017

    Article MATH  Google Scholar 

  10. Yang X, Wei P (2021) Autonomous free flight operations in urban air mobility with computational guidance and collision avoidance. IEEE Trans Intell Transp Syst 22(9):5962–5975

    Article MATH  Google Scholar 

  11. Cecen RK (2022) A stochastic programming model for the aircraft sequencing and scheduling problem considering flight duration uncertainties. Aeronaut J 126(1304):1736–1751

    Article MATH  Google Scholar 

  12. Bo X, Ma W, Ke H, Yang W, Zhang H (2022) An efficient ant colony algorithm based on rank 2 matrix approximation method for aircraft arrival/departure scheduling problem. Processes 10(9):1825.https://doi.org/10.3390/pr10091825

    Article MATH  Google Scholar 

  13. Cecen RK (2022) Fuel-optimal aircraft arrival operations in extended terminal maneuvering areas. Transp Res Rec 2676(6):330–339

    Article MATH  Google Scholar 

  14. Messaoud MB (2021) A thorough review of aircraft landing operation from practical and theoretical standpoints at an airport which may include a single or multiple runways. Appl Soft Comput 98:1–46

    MATH  Google Scholar 

  15. Hu XB, Chen WH (2005) Receding horizon control for aircraft arrival sequencing and scheduling. IEEE Trans Intell Transp Syst 6(2):189–197

    Article MATH  Google Scholar 

  16. Zhang J, Pengli Zhao Y, Zhang XD, Sui D (2020) Criteria selection and multi-objective optimization of aircraft landing problem. J Air Trans Manag 82:101734.https://doi.org/10.1016/j.jairtraman.2019.101734

    Article MATH  Google Scholar 

  17. Ikli S, Mancel C, Mongeau M, Olive X, and Rachelson E (2019) An optimistic planning approach for the aircraft landing problem, In Proceedings of 6th ENRI international workshop on ATM/CNS, 2019, pp. 1–7.

  18. Yu SP, Cao XB, Zhang J (2011) A real-time schedule method for aircraft landing scheduling problem based on cellular automation. Appl Soft Comput 11(4):3485–3493

    Article MATH  Google Scholar 

  19. Prakash R, Piplani R, Desai J (2018) An optimal data-splitting algorithm for aircraft scheduling on a single runway to maximize throughput. Transp Res Part C-Emerg Technol 95:570–581

    Article MATH  Google Scholar 

  20. Lieder A, Briskorn D, Stolletz R (2015) A dynamic programming approach for the aircraft landing problem with aircraft classes. Eur J Oper Res 243(1):61–69

    Article MathSciNet MATH  Google Scholar 

  21. Donmez K, Cetek C, Kaya O (2022) Aircraft sequencing and scheduling in parallel-point merge systems for multiple parallel runways. Transp Res Rec 2676(3):108–124

    Article  Google Scholar 

  22. Diallo C, Ndiaye BM, Seck D (2012) Scheduling aircraft landing at LSS airport. Am J Op Res 2(2):235–241

    Google Scholar 

  23. Bali KK, Gupta A, Ong Y-S, Tan PS (2021) Cognizant multitasking in multiobjective multifactorial evolution: MO-MFEA-II. IEEE Trans Cybern 51(4):1784–1796

    Article MATH  Google Scholar 

  24. Liu C-H, Ting C-K (2017) Computational intelligence in music composition: a survey. IEEE Trans Emerg Top Comput Intell 1(1):2–15

    Article MATH  Google Scholar 

  25. Aslan S (2020) A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization. Memet Comput 12:129–150

    Article MATH  Google Scholar 

  26. Li X, Xiao S, Wang C, Yi J (2019) Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem. Memet Comput 11:371–389

    Article MATH  Google Scholar 

  27. Niu M, Liu R, and Wang H 2021 A max-min ant system based on decomposition for the multi-depot cumulative capacitated vehicle routing problem. In: Proceedings of IEEE congress on evolutionary computation, pp. 620–627.

  28. Nguyen BH, Xue B, Zhang M (2020) A survey on swarm intelligence approaches to feature selection in data mining. Swarm Evol Comput 54:100663

    Article MATH  Google Scholar 

  29. Chen K, Xue B, Zhang M, Zhou F (2022) Evolutionary multitasking for feature selection in high-dimensional classification via particle swarm optimization. IEEE Trans Evol Comput 26(3):446–460

    Article MATH  Google Scholar 

  30. Sun Y, Xue B, Zhang M, Yen GG (2019) A particle swarm optimization-based flexible convolutional autoencoder for image classification. IEEE Trans Neural Netw Learn Syst 30(8):2295–2309

    Article MATH  Google Scholar 

  31. Du KJ, Li JY, Wang H, Zhang J (2023) A knowledge learning and random pruning-based memetic algorithm for user route planning in bike-sharing system. Memet Comput 15(2):259–279

    Article MATH  Google Scholar 

  32. Zhan ZH, Shi L, Tan KC, Zhang J (2022) A survey on evolutionary computation for complex continuous optimization. Artif Intell Rev 55(1):59–110

    Article MATH  Google Scholar 

  33. Hong J, Zhan ZH, He L, Xu Z, Zhang J (2024) Protein structure prediction using a new optimization-based evolutionary and explainable artificial intelligence approach. IEEE Trans Evol Comput.https://doi.org/10.1109/TEVC.2024.3365814.Feb

    Article MATH  Google Scholar 

  34. Jiang Y, Zhan ZH, Tan KC, Kwong S, Zhang J (2024) Knowledge structure preserving-based evolutionary many-task optimization. IEEE Trans Evol Comput.https://doi.org/10.1109/TEVC.2024.3355781.Jan

    Article  Google Scholar 

  35. Yang QT, Li JY, Zhan ZH, Jiang Y, Jin Y, Zhang J (2024) A hierarchical and ensemble surrogate-assisted evolutionary algorithm with model reduction for expensive many-objective optimization. IEEE Trans Evol Comput.https://doi.org/10.1109/TEVC.2024.3440354.Aug

    Article MATH  Google Scholar 

  36. Ng KKH, Lee CKM, Chan FTS, Qin YC (2017) Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach. Transp Res Part E: Logist Transp Rev 106:115–136

    Article MATH  Google Scholar 

  37. Liu W, Delahaye D, Zhao Q, Notry P (2023) Benefit of wind networking for aircraft arrival scheduling in terminal manoeuvring area. Comput Ind Eng 182:109418

    Article MATH  Google Scholar 

  38. Girish BS (2016) An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem. Appl Soft Comput 44:200–221

    Article MATH  Google Scholar 

  39. Bencheikh G, Boukachour J, Alaoui AEH (2016) A memetic algorithm to solve the dynamic multiple runway aircraft landing problem. J King Saud Univ: Comput Inf Sci 28(1):98–109

    Article MATH  Google Scholar 

  40. Vadlamani S, Hosseini S (2014) A novel heuristic approach for solving aircraft landing problem with single runway. J Air Transp Manag 40:144–148

    Article MATH  Google Scholar 

  41. Li Y, Nie DM, Wen XX, and Gao YY (2018) Arrival aircraft optimal sequencing based on teaching-learning-based optimization algorithm with immunity, In: Proc. IOP conference series: earth and environmental science vol. 189, pp. 1–6.

  42. Sabar NR, Kendall G (2015) An iterated local search with multiple perturbation operators and time varying perturbation strength for the aircraft landing problem. Omega 56:88–98

    Article MATH  Google Scholar 

  43. Salehipour A, Modarres M, Naeni LM (2013) An efficient hybrid meta-heuristic for aircraft landing problem. Comput Oper Res 40(1):207–213

    Article MathSciNet MATH  Google Scholar 

  44. Salehipour A (2020) An algorithm for single- and multiple-runway aircraft landing problem. Math Comput Simul 175:179–191

    Article MathSciNet MATH  Google Scholar 

  45. Hu XB, Chen WH (2005) Genetic algorithm based on receding horizon control for arrival sequencing and scheduling. Eng Appl Artif Intell 18(5):633–642

    Article MATH  Google Scholar 

  46. Zhan ZH et al (2010) An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem. IEEE Trans Intell Transp Syst 11(2):399–412

    Article MATH  Google Scholar 

  47. Wu LJ, Zhan ZH, Hu X, Guo P, Zhang Y, and Zhang J (2019) Multi-runway aircraft arrival scheduling: a receding horizon control based ant colony system approach. In: Proc. ieee congress on evolutionary computation, pp. 538–545.

  48. Bianco L, Dell’Olmo P, Giordani S (1997) Scheduling models and algorithms for TMA traffic management. In: Bianco L, Dell’Olmo P, Odoni AR (eds) Modelling and simulation in air traffic management. Springer Berlin Heidelberg, Berlin, pp 139–167.https://doi.org/10.1007/978-3-642-60836-0_7

    Chapter MATH  Google Scholar 

  49. Li ZP and Wang YY (2018) A review for aircraft landing problem, In Proc. MATEC Web of Conferences, vol. 179(1), pp. 1-6

  50. Salama KM, Freitas AA (2014) ABC-Miner+: constructing Markov blanket classifiers with ant colony algorithms. Memet Comput 6(3):183–206

    Article  Google Scholar 

  51. Wang R et al (2022) An adaptive ant colony system based on variable range receding horizon control for berth allocation problem. IEEE Trans Intell Transp Syst 23(11):21675–21686

    Article  Google Scholar 

  52. Ezzat A, Abdelbar AM, Wunsch DC (2014) A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem. Memet Computng 6(1):19–29

    Article MATH  Google Scholar 

  53. Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66

    Article MATH  Google Scholar 

  54. Chen ZG, Zhan ZH, Kwong S, Zhang J (2022) Evolutionary computation for intelligent transportation in smart cities: a survey. IEEE Comput Intell Mag 17(2):83–102

    Article MATH  Google Scholar 

  55. Wu LJ, Shi L, Zhan ZH, Lai KK, Zhang J (2022) A buffer-based ant colony system approach for dynamic cold chain logistics scheduling. IEEE Trans Emerg Top Comput Intell 6(6):1438–1452

    Article MATH  Google Scholar 

  56. Shi L, Zhan ZH, Liang D, Zhang J (2022) Memory-based ant colony system approach for multi-source data associated dynamic electric vehicle dispatch optimization. IEEE Trans Intell Transp Syst 23(10):17491–17505

    Article MATH  Google Scholar 

  57. Li JY et al (2022) A multipopulation multiobjective ant colony system considering travel and prevention costs for vehicle routing in COVID-19-like epidemics. IEEE Trans Intell Transp Syst 23(12):25062–25076

    Article  Google Scholar 

  58. Wu LJ et al (2022) Real environment-aware multisource data-associated cold chain logistics scheduling: a multiple population-based multiobjective ant colony system approach. IEEE Trans Intell Transp Syst 23(12):23613–23627

    Article  Google Scholar 

  59. Zhang X, Zhan ZH, Fang W, Qian P, Zhang J (2022) Multi-population ant colony system with knowledge-based local searches for multiobjective supply chain configuration. IEEE Trans Evol Comput 26(3):512–526

    Article MATH  Google Scholar 

  60. Jiang Y, Xu X-X, Zheng M-Y, Zhan Z-H (2024) Evolutionary computation for unmanned aerial vehicle path planning: a survey. Artif Intell Rev 57:267.https://doi.org/10.1007/s10462-024-10913-0

    Article MATH  Google Scholar 

  61. Zhan ZH, Li JY, Kwong S, Zhang J (2023) Learning-aided evolution for optimization. IEEE Trans Evol Comput 27(6):1794–1808

    Article MATH  Google Scholar 

  62. Jiang Y, Zhan ZH, Tan KC, Zhang J (2023) Knowledge learning for evolutionary computation. IEEE Trans Evol Comput.https://doi.org/10.1109/TEVC.2023.3278132

    Article MATH  Google Scholar 

  63. Li JY, Du KJ, Zhan ZH, Wang H, Zhang J (2023) Distributed differential evolution with adaptive resource allocation. IEEE Trans Cybern 53(5):2791–2804

    Article MATH  Google Scholar 

  64. Liu XF, Zhan ZH, Gao Y, Zhang J, Kwong S, Zhang J (2019) Coevolutionary particle swarm optimization with bottleneck objective learning strategy for many-objective optimization. IEEE Trans Evol Comput 23(4):587–602

    Article MATH  Google Scholar 

  65. Wu SH, Zhan ZH, Zhang J (2021) SAFE: scale-adaptive fitness evaluation method for expensive optimization problems. IEEE Trans Evol Comput 25(3):478–491

    Article MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2023YFB3308903; in part by the China Scholarship Council (Grant No. 202306330052 for Xin-Xin Xu); and in part by the Fundamental Research Funds for the Central Universities, Nankai University (078-63243159).

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

    Xin-Xin Xu, Hui-Li Gong & Xiang-Qian Ding

  2. School of Computer Science, Liaocheng University, Liaocheng, 252000, China

    Hong-Yan Sang

  3. Hanyang University, ERICA, Ansan, 15588, South Korea

    Yi Jiang

  4. Department of Computing and Decision Science, Lingnan University, Tuen Mun, Hong Kong SAR, China

    Sam Kwong

  5. College of Artificial Intelligence, Nankai University, Tianjin, 300350, China

    Zhi-Hui Zhan

Authors
  1. Xin-Xin Xu

    You can also search for this author inPubMed Google Scholar

  2. Yi Jiang

    You can also search for this author inPubMed Google Scholar

  3. Hong-Yan Sang

    You can also search for this author inPubMed Google Scholar

  4. Hui-Li Gong

    You can also search for this author inPubMed Google Scholar

  5. Xiang-Qian Ding

    You can also search for this author inPubMed Google Scholar

  6. Sam Kwong

    You can also search for this author inPubMed Google Scholar

  7. Zhi-Hui Zhan

    You can also search for this author inPubMed Google Scholar

Contributions

Xin-Xin Xu wrote the main manuscript text and conducted the experiments; Yi Jiang conducted the experiments; Hong-Yan Sang, Hui-Li Gong, and Xiang-Qian Ding revised the manuscript; Xin-Xin Xu, Sam Kwong and Zhi-Hui Zhan discussed the idea and revised the manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence toHui-Li Gong orZhi-Hui Zhan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, XX., Jiang, Y., Sang, HY.et al. A receding horizon control-based holistic ant colony system approach for multi-runway aircraft arrival sequencing and scheduling.Memetic Comp.17, 16 (2025). https://doi.org/10.1007/s12293-025-00447-5

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp