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Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty

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  • Shone, Rob
  • Glazebrook, Kevin
  • Zografos, Konstantinos G.

Abstract

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management.

Suggested Citation

  • Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2021. "Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 292(1), pages 1-26.
    • Handle: RePEc:eee:ejores:v:292:y:2021:i:1:p:1-26
    DOI: 10.1016/j.ejor.2020.10.039
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    Cited by:

    1. Li, Mingjie & Hao, Jin-Kao & Wu, Qinghua, 2022. "Learning-driven feasible and infeasible tabu search for airport gate assignment," European Journal of Operational Research, Elsevier, vol. 302(1), pages 172-186.
    2. Liu, Wenjing & Zhao, Qiuhong & Delahaye, Daniel, 2022. "Research on slot allocation for airport network in the presence of uncertainty," Journal of Air Transport Management, Elsevier, vol. 104(C).
    3. Bolić, Tatjana & Castelli, Lorenzo & Corolli, Luca & Scaini, Giovanni, 2021. "Flexibility in strategic flight planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).

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