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Managing severe airspace flow programs: The Airlines’ side of the problem

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  • Abdelghany, Khaled
  • Abdelghany, Ahmed
  • Niznik, Tim

Abstract

This paper presents a heuristic-based approach for minimizing airlines’ schedule disruptions and operation costs associated with severe airspace flow programs. It considers primary decisions made by flight dispatchers such as flight slot substitution and rerouting outside the boundaries of the flow-constrained area. A two-stage heuristic is developed. In the first, a linear approximation of the problem is used to screen inefficient routing and slot substitution alternatives. The second stage examines possible solution improvements through trading flight assignments for every pair of conflicting routes. A genetic algorithm is developed and used to benchmark the performance of the two-stage heuristic. In the algorithm, flight route and slot allocation schemes are modeled as chromosomes. The fitness of these chromosomes measures the magnitude of schedule disruption and overall operating cost. A set of experiments that compare the performance of the two heuristics considering airspace flow programs with different levels of severity is presented.

Suggested Citation

  • Abdelghany, Khaled & Abdelghany, Ahmed & Niznik, Tim, 2007. "Managing severe airspace flow programs: The Airlines’ side of the problem," Journal of Air Transport Management, Elsevier, vol. 13(6), pages 329-337.
    • Handle: RePEc:eee:jaitra:v:13:y:2007:i:6:p:329-337
    DOI: 10.1016/j.jairtraman.2007.05.004
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    References listed on IDEAS

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    1. Abdelghany, Khaled F. & S. Shah, Sharmila & Raina, Sidhartha & Abdelghany, Ahmed F., 2004. "A model for projecting flight delays during irregular operation conditions," Journal of Air Transport Management, Elsevier, vol. 10(6), pages 385-394.
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    Cited by:

    1. 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).
    2. Abdelghany, Ahmed & Guzhva, Vitaly S. & Abdelghany, Khaled, 2023. "The limitation of machine-learning based models in predicting airline flight block time," Journal of Air Transport Management, Elsevier, vol. 107(C).
    3. Vaaben, Bo & Larsen, Jesper, 2015. "Mitigation of airspace congestion impact on airline networks," Journal of Air Transport Management, Elsevier, vol. 47(C), pages 54-65.

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