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Optimal Selection of Airport Runway Configurations

Author

Listed:
  • Dimitris Bertsimas

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Michael Frankovich

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Amedeo Odoni

    (Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

Abstract

We present a mixed integer programming (MIP) model to solve the problems of (i) selecting an airport's optimal sequence of runway configurations and (ii) determining the optimal balance of arrivals and departures to be served at any moment. These problems, the runway configuration management (RCM) problem and the arrival/departure runway balancing (ADRB) problem, respectively, are of critical importance in minimizing the delay of both in-flight and on-the-ground aircraft along with their associated costs. We show that under mild assumptions on the time required to change between configurations, large realistic problem instances can be solved within several seconds. Furthermore, as assumptions are relaxed, optimal solutions are still found within several minutes. Comparison with a sophisticated baseline heuristic reveals that in many cases the potential reduction in cost from using the method is significant and could be expected to be of the order of at least 10%. Finally, we present an extension of the MIP model to solve these two problems for a group of airports in a metropolitan area such as New York ( metroplex ), where operations at each airport within the metroplex might have an impact on operations at some of the other airports due to limitations in shared airspace.

Suggested Citation

  • Dimitris Bertsimas & Michael Frankovich & Amedeo Odoni, 2011. "Optimal Selection of Airport Runway Configurations," Operations Research, INFORMS, vol. 59(6), pages 1407-1419, December.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:6:p:1407-1419
    DOI: 10.1287/opre.1110.0956
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    Citations

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    Cited by:

    1. Chia-Nan Wang & Kristofer Neal Castro Imperial & Ching-Chien Huang & Thanh-Tuan Dang, 2022. "Output Targeting and Runway Utilization of Major International Airports: A Comparative Analysis Using DEA," Mathematics, MDPI, vol. 10(4), pages 1-23, February.
    2. Cavusoglu, Sabriye Sera & Macário, Rosário, 2021. "Minimum delay or maximum efficiency? Rising productivity of available capacity at airports: Review of current practice and future needs," Journal of Air Transport Management, Elsevier, vol. 90(C).
    3. Gillen, David & Jacquillat, Alexandre & Odoni, Amedeo R., 2016. "Airport demand management: The operations research and economics perspectives and potential synergies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 495-513.
    4. Alexandre Jacquillat & Amedeo R. Odoni, 2015. "An Integrated Scheduling and Operations Approach to Airport Congestion Mitigation," Operations Research, INFORMS, vol. 63(6), pages 1390-1410, December.
    5. Shone, Rob & Glazebrook, Kevin & Zografos, Konstantinos G., 2019. "Resource allocation in congested queueing systems with time-varying demand: An application to airport operations," European Journal of Operational Research, Elsevier, vol. 276(2), pages 566-581.
    6. 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.
    7. Jacquillat, Alexandre & Odoni, Amedeo R., 2018. "A roadmap toward airport demand and capacity management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 114(PA), pages 168-185.
    8. Dimitris Bertsimas & Michael Frankovich, 2016. "Unified Optimization of Traffic Flows Through Airports," Transportation Science, INFORMS, vol. 50(1), pages 77-93, February.
    9. Alexandre Jacquillat & Amedeo R. Odoni & Mort D. Webster, 2017. "Dynamic Control of Runway Configurations and of Arrival and Departure Service Rates at JFK Airport Under Stochastic Queue Conditions," Transportation Science, INFORMS, vol. 51(1), pages 155-176, February.

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