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Ground Delay Program Planning Under Uncertainty Based on the Ration-by-Distance Principle

Author

Listed:
  • Michael O. Ball

    (Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742)

  • Robert Hoffman

    (Metron Aviation, Inc., Dulles, Virginia 20166)

  • Avijit Mukherjee

    (University Affiliated Research Center, University of California (Santa Cruz), Moffett Field, California 94035)

Abstract

This paper presents ration-by-distance (RBD), a new allocation method to be used in planning ground delay programs (GDPs) for traffic flow management. It is shown that RBD minimizes total expected delay, under certain assumptions related to the manner in which GDPs are dynamically controlled. On the other hand, RBD taken to the extreme has poor characteristics with respect to the equity of the allocation it produces. To address this issue, we propose a constrained version of RBD as a practical alternative to allocation procedures used in operations today. It is shown that this algorithm has superior overall performance in terms of efficiency and equity relative to existing procedures.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:1:p:1-14
    DOI: 10.1287/trsc.1090.0289
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. Sun, D. & Clinet, A. & Bayen, A.M., 2011. "A dual decomposition method for sector capacity constrained traffic flow optimization," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 880-902, July.
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    5. Alexander Estes & David J. Lovell & Michael O. Ball, 2019. "Unsupervised prototype reduction for data exploration and an application to air traffic management initiatives," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 467-510, December.
    6. 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.
    7. Alexander S. Estes & Michael O. Ball, 2021. "Monge Properties, Optimal Greedy Policies, and Policy Improvement for the Dynamic Stochastic Transportation Problem," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 785-807, May.
    8. Karsu, Özlem & Morton, Alec, 2015. "Inequity averse optimization in operational research," European Journal of Operational Research, Elsevier, vol. 245(2), pages 343-359.
    9. Yi Liu & Mark Hansen, 2016. "Incorporating Predictability Into Cost Optimization for Ground Delay Programs," Transportation Science, INFORMS, vol. 50(1), pages 132-149, February.
    10. Kim, Amy & Hansen, Mark, 2015. "Some insights into a sequential resource allocation mechanism for en route air traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 79(C), pages 1-15.
    11. Schummer, James & Abizada, Azar, 2017. "Incentives in landing slot problems," Journal of Economic Theory, Elsevier, vol. 170(C), pages 29-55.
    12. Caccavale, Maria Virginia & Iovanella, Antonio & Lancia, Carlo & Lulli, Guglielmo & Scoppola, Benedetto, 2014. "A model of inbound air traffic: The application to Heathrow airport," Journal of Air Transport Management, Elsevier, vol. 34(C), pages 116-122.
    13. 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.
    14. Dixit, Aasheesh & Jakhar, Suresh Kumar, 2021. "Airport capacity management: A review and bibliometric analysis," Journal of Air Transport Management, Elsevier, vol. 91(C).
    15. Xiangning Dong & Xuhao Zhu & Minghua Hu & Jie Bao, 2023. "A Methodology for Predicting Ground Delay Program Incidence through Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    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. Samà, Marcella & D’Ariano, Andrea & D’Ariano, Paolo & Pacciarelli, Dario, 2017. "Scheduling models for optimal aircraft traffic control at busy airports: Tardiness, priorities, equity and violations considerations," Omega, Elsevier, vol. 67(C), pages 81-98.
    18. Guo, Yechenfeng & Hu, Minghua & Zou, Bo & Hansen, Mark & Zhang, Ying & Xie, Hua, 2022. "Air Traffic Flow Management Integrating Separation Management and Ground Holding: An Efficiency-Equity Bi-objective Perspective," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 394-423.

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