IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v22y2022i5d10.1007_s12351-021-00686-7.html
   My bibliography  Save this article

A n − D ant colony optimization with fuzzy logic for air traffic flow management

Author

Listed:
  • Charis Ntakolia

    (Hellenic Air Force Academy
    National Technical University of Athens)

  • Dimitrios V. Lyridis

    (National Technical University of Athens)

Abstract

Recent studies show that the number of flights is expected to be increased significantly by 2030, leading to air traffic capacity and congestion issues in the air sectors. This challenging management of the anticipated volume of flights has emerged new derivatives and procedures from the European Union and EUROCONTROL. Aligned with the new vision of future Air Traffic Flow Management (ATFM), such as Trajectory Based Operations, this study proposes a mixed integer nonlinear formulation of ATFM based on 4D trajectories and free flight aspects. The model targets to minimize the total costs derived from airborne and ground holding delays, speed deviations, route alterations and cancellation policies. To solve the proposed nonlinear formulation, a novel n − D ant colony optimization algorithm integrated with fuzzy logic (n − DACOF) is presented. Each flight level is represented as graph and the n − D stands for the n number of permitted flight levels. n − DACOF can solve the ATFM problem by constructing a route moving among n graphs. Due to the multi-objective formulation, fuzzy logic permits the qualitative evaluation of the generated routes by the algorithm. The results showed that n − DACOF outperformed the baseline algorithm ACO, as well as, the CPLEX solver within computing time limits.

Suggested Citation

  • Charis Ntakolia & Dimitrios V. Lyridis, 2022. "A n − D ant colony optimization with fuzzy logic for air traffic flow management," Operational Research, Springer, vol. 22(5), pages 5035-5053, November.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-021-00686-7
    DOI: 10.1007/s12351-021-00686-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-021-00686-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-021-00686-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hancerliogullari, Gulsah & Rabadi, Ghaith & Al-Salem, Ameer H. & Kharbeche, Mohamed, 2013. "Greedy algorithms and metaheuristics for a multiple runway combined arrival-departure aircraft sequencing problem," Journal of Air Transport Management, Elsevier, vol. 32(C), pages 39-48.
    2. Alonso-Ayuso, Antonio & Escudero, Laureano F. & Martín-Campo, F. Javier, 2016. "Multiobjective optimization for aircraft conflict resolution. A metaheuristic approach," European Journal of Operational Research, Elsevier, vol. 248(2), pages 691-702.
    3. Diao, Xudong & Chen, Chun-Hsien, 2018. "A sequence model for air traffic flow management rerouting problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 15-30.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Yunxiang & Zhao, Yifei & Wu, Yexin, 2024. "Recent progress in air traffic flow management: A review," Journal of Air Transport Management, Elsevier, vol. 116(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alireza Rangrazjeddi & Andrés D. González & Kash Barker, 2023. "Applied Game Theory to Enhance Air Traffic Control in 3D Airspace," Journal of Optimization Theory and Applications, Springer, vol. 196(3), pages 1125-1154, March.
    2. Miriam F. Bongo & Charlle L. Sy, 2024. "Can diverse and conflicting interests of multiple stakeholders be balanced?," Annals of Operations Research, Springer, vol. 339(3), pages 1813-1837, August.
    3. Dönmez, Kadir & Çetek, Cem & Kaya, Onur, 2022. "Air traffic management in parallel-point merge systems under wind uncertainties," Journal of Air Transport Management, Elsevier, vol. 104(C).
    4. Zhang, Qiuhan & Le, Meilong & Xu, Yan, 2021. "Collaborative delay management towards demand-capacity balancing within User Driven Prioritisation Process," Journal of Air Transport Management, Elsevier, vol. 91(C).
    5. Yi Yang & Shangwen Yang & Ming Tong & Ying Xu, 2023. "RETRACTED ARTICLE: A novel dynamic en-route and slot allocation method based on receding horizon control," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-20, March.
    6. Ng, K.K.H. & Lee, C.K.M. & Chan, Felix T.S. & Qin, Yichen, 2017. "Robust aircraft sequencing and scheduling problem with arrival/departure delay using the min-max regret approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 115-136.
    7. María Sierra-Paradinas & Antonio Alonso-Ayuso & Francisco Javier Martín-Campo & Francisco Rodríguez-Calo & Enrique Lasso, 2020. "Facilities Delocation in the Retail Sector: A Mixed 0-1 Nonlinear Optimization Model and Its Linear Reformulation," Mathematics, MDPI, vol. 8(11), pages 1-19, November.
    8. Lieder, Alexander & Stolletz, Raik, 2016. "Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 167-188.
    9. Khan, Waqar Ahmed & Chung, Sai-Ho & Ma, Hoi-Lam & Liu, Shi Qiang & Chan, Ching Yuen, 2019. "A novel self-organizing constructive neural network for estimating aircraft trip fuel consumption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 132(C), pages 72-96.
    10. Chen, Yunxiang & Zhao, Yifei & Wu, Yexin, 2024. "Recent progress in air traffic flow management: A review," Journal of Air Transport Management, Elsevier, vol. 116(C).
    11. Thibault Lehouillier & Moncef Ilies Nasri & François Soumis & Guy Desaulniers & Jérémy Omer, 2017. "Solving the Air Conflict Resolution Problem Under Uncertainty Using an Iterative Biobjective Mixed Integer Programming Approach," Transportation Science, INFORMS, vol. 51(4), pages 1242-1258, November.
    12. Chandra, Aitichya & Choubey, Nipun & Verma, Ashish & Sooraj, K.P., 2024. "Quasi-stochastic optimization model for time-based arrival scheduling considering Standard Terminal Arrival (STAR) track time and a new delay-conflict relationship," Journal of Air Transport Management, Elsevier, vol. 115(C).
    13. Xudong Diao & Ai Gao & Xin Jin & Hui Chen, 2022. "A Layer-Based Relaxation Approach for Service Network Design," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    14. Muren, & Wu, Jianjun & Zhou, Li & Du, Zhiping & Lv, Ying, 2019. "Mixed steepest descent algorithm for the traveling salesman problem and application in air logistics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 87-102.
    15. Sadeque Hamdan & Oualid Jouini & Ali Cheaitou & Zied Jemai & Tobias Andersson Granberg, 2023. "On the binary formulation of air traffic flow management problems," Annals of Operations Research, Springer, vol. 321(1), pages 267-279, February.
    16. Liu, Yulin & Liu, Yi & Hansen, Mark & Pozdnukhov, Alexey & Zhang, Danqing, 2019. "Using machine learning to analyze air traffic management actions: Ground delay program case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 80-95.
    17. Zhang, Junfeng & Zhao, Pengli & Zhang, Yu & Dai, Ximei & Sui, Dong, 2020. "Criteria selection and multi-objective optimization of aircraft landing problem," Journal of Air Transport Management, Elsevier, vol. 82(C).
    18. Woo, Young-Bin & Moon, Ilkyeong, 2021. "Scenario-based stochastic programming for an airline-driven flight rescheduling problem under ground delay programs," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:operea:v:22:y:2022:i:5:d:10.1007_s12351-021-00686-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.