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Optimization and mediated bartering models for ground delay programs

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  • Thomas Vossen
  • Michael Ball

Abstract

The Federal Aviation Administration (FAA) and the airline community within the United States have adopted a new paradigm for air traffic flow management, called Collaborative Decision Making (CDM). A principal goal of CDM is shared decision‐making responsibility between the FAA and airlines, so as to increase airline control over decisions that involve economic tradeoffs. So far, CDM has primarily led to enhancements in the implementation of Ground Delay Programs, by changing procedures for allocating slots to airlines and exchanging slots between airlines. In this paper, we discuss how these procedures may be formalized through appropriately defined optimization models. In addition, we describe how inter‐airline slot exchanges may be viewed as a bartering process, in which each “round” of bartering requires the solution of an optimization problem. We compare the resulting optimization problem with the current procedure for exchanging slots and discuss possibilities for increased decision‐making capabilities by the airlines. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2006

Suggested Citation

  • Thomas Vossen & Michael Ball, 2006. "Optimization and mediated bartering models for ground delay programs," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(1), pages 75-90, February.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:1:p:75-90
    DOI: 10.1002/nav.20123
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. James Schummer & Rakesh V. Vohra, 2013. "Assignment of Arrival Slots," American Economic Journal: Microeconomics, American Economic Association, vol. 5(2), pages 164-185, May.
    2. Katsigiannis, Fotios A. & Zografos, Konstantinos G., 2021. "Optimising airport slot allocation considering flight-scheduling flexibility and total airport capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 146(C), pages 50-87.
    3. 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.
    4. Xu, Yan & Dalmau, Ramon & Melgosa, Marc & Montlaur, Adeline & Prats, Xavier, 2020. "A framework for collaborative air traffic flow management minimizing costs for airspace users: Enabling trajectory options and flexible pre-tactical delay management," Transportation Research Part B: Methodological, Elsevier, vol. 134(C), pages 229-255.
    5. Murça, Mayara Condé Rocha, 2018. "Collaborative air traffic flow management: Incorporating airline preferences in rerouting decisions," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 97-107.
    6. 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).
    7. Guglielmo Lulli & Amedeo Odoni, 2007. "The European Air Traffic Flow Management Problem," Transportation Science, INFORMS, vol. 41(4), pages 431-443, November.
    8. Dalmau, Ramon & Gawinowski, Gilles & Anoraud, Camille, 2022. "Comparison of various temporal air traffic flow management models in critical scenarios," Journal of Air Transport Management, Elsevier, vol. 105(C).
    9. Schummer, James & Abizada, Azar, 2017. "Incentives in landing slot problems," Journal of Economic Theory, Elsevier, vol. 170(C), pages 29-55.

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