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Decomposition Algorithms for Analyzing Transient Phenomena in Multiclass Queueing Networks in Air Transportation

Author

Listed:
  • Michael D. Peterson

    (McKinsey & Company, Inc., Bedminster, New Jersey)

  • Dimitris J. Bertsimas

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

  • Amedeo R. Odoni

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

A previous paper (1992) by the same authors studied the phenomenon of transient congestion in landings at an airport and developed a recursive approach for computing moments of queue lengths and waiting times. This paper extends our approach to a network, developing two approximations based on the prior method. Both approaches work by using delay information estimated at one location to update arrival schedules at other points in the network. We present computational results for a simple 2-node network, comparing the performance of the approximations with an alternative simulation approach. The methods give similar results in light to moderate traffic but show a growing disparity under heavier traffic, where the algorithms underestimate the true magnitude of delay propagation relative to simulation. Finally, to illustrate the usefulness of the modeling, we show how the results may be used to explore the issue of interaction between airports. Although this particular application motivated development of the model, the method is, in principle, applicable to other multiclass queueing networks where service capacity at a station may be modeled as a Markov or semi-Markov process. The model represents a new approach for analyzing transient congestion phenomena in such networks.

Suggested Citation

  • Michael D. Peterson & Dimitris J. Bertsimas & Amedeo R. Odoni, 1995. "Decomposition Algorithms for Analyzing Transient Phenomena in Multiclass Queueing Networks in Air Transportation," Operations Research, INFORMS, vol. 43(6), pages 995-1011, December.
  • Handle: RePEc:inm:oropre:v:43:y:1995:i:6:p:995-1011
    DOI: 10.1287/opre.43.6.995
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    Cited by:

    1. Broyles, James R. & Cochran, Jeffery K. & Montgomery, Douglas C., 2010. "A statistical Markov chain approximation of transient hospital inpatient inventory," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1645-1657, December.
    2. Xiao Chen & Carolina Osorio & Bruno Filipe Santos, 2019. "Simulation-Based Travel Time Reliable Signal Control," Transportation Science, INFORMS, vol. 53(2), pages 523-544, March.
    3. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    4. Ren, Pan & Li, Lishuai, 2018. "Characterizing air traffic networks via large-scale aircraft tracking data: A comparison between China and the US networks," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 181-196.
    5. Linsen Chong & Carolina Osorio, 2018. "A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 52(3), pages 637-656, June.
    6. Wang, Yanjun & Li, Max Z. & Gopalakrishnan, Karthik & Liu, Tongdan, 2022. "Timescales of delay propagation in airport networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).

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