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Ground delay program planning under uncertainty in airport capacity

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  • Avijit Mukherjee
  • Mark Hansen
  • Shon Grabbe

Abstract

This paper presents an algorithm for assigning flight departure delays under probabilistic airport capacity. The algorithm dynamically adapts to weather forecasts by revising, if necessary, departure delays. The proposed algorithm leverages state-of-the-art optimization techniques that have appeared in recent literature. As a case study, the algorithm is applied to assigning departure delays to flights scheduled to arrive at San Francisco International Airport in the presence of uncertainty in the fog clearance time. The cumulative distribution function of fog clearance time was estimated from historical data. Using daily weather forecasts to update the probabilities of fog clearance times resulted in improvement of the algorithm's performance. Experimental results also indicate that if the proposed algorithm is applied to assign ground delays to flights inbound at San Francisco International airport, overall delays could be reduced up to 25% compared to current level.

Suggested Citation

  • Avijit Mukherjee & Mark Hansen & Shon Grabbe, 2012. "Ground delay program planning under uncertainty in airport capacity," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(6), pages 611-628, June.
    • Handle: RePEc:taf:transp:v:35:y:2012:i:6:p:611-628
    DOI: 10.1080/03081060.2012.710031
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    References listed on IDEAS

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    1. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    2. Octavio Richetta & Amedeo R. Odoni, 1993. "Solving Optimally the Static Ground-Holding Policy Problem in Air Traffic Control," Transportation Science, INFORMS, vol. 27(3), pages 228-238, August.
    3. Avijit Mukherjee & Mark Hansen, 2007. "A Dynamic Stochastic Model for the Single Airport Ground Holding Problem," Transportation Science, INFORMS, vol. 41(4), pages 444-456, November.
    4. Michael O. Ball & Robert Hoffman & Amedeo R. Odoni & Ryan Rifkin, 2003. "A Stochastic Integer Program with Dual Network Structure and Its Application to the Ground-Holding Problem," Operations Research, INFORMS, vol. 51(1), pages 167-171, February.
    5. Michael O. Ball & Robert Hoffman & Avijit Mukherjee, 2010. "Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle," Transportation Science, INFORMS, vol. 44(1), pages 1-14, February.
    6. Balázs Kotnyek & Octavio Richetta, 2006. "Equitable Models for the Stochastic Ground-Holding Problem Under Collaborative Decision Making," Transportation Science, INFORMS, vol. 40(2), pages 133-146, May.
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    Cited by:

    1. Alexander S. Estes & Michael O. Ball, 2020. "Equity and Strength in Stochastic Integer Programming Models for the Dynamic Single Airport Ground-Holding Problem," Transportation Science, INFORMS, vol. 54(4), pages 944-955, July.
    2. Yi Liu & Mark Hansen, 2016. "Incorporating Predictability Into Cost Optimization for Ground Delay Programs," Transportation Science, INFORMS, vol. 50(1), pages 132-149, February.
    3. 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.
    4. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.

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