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Revenue Management Without Forecasting or Optimization: An Adaptive Algorithm for Determining Airline Seat Protection Levels

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Author Info

  • Garrett van Ryzin

    ()
    (Graduate School of Business, Columbia University, New York, New York 10027)

  • Jeff McGill

    ()
    (School of Business, Queen's University, Kingston, Ontario, Canada)

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    Abstract

    We investigate a simple adaptive approach to optimizing seat protection levels in airline revenue management systems. The approach uses only historical observations of the relative frequencies of certain seat-filling events to guide direct adjustments of the seat protection levels in accordance with the optimality conditions of Brumelle and McGill (1993). Stochastic approximation theory is used to prove the convergence of this adaptive algorithm to the optimal protection levels. In a simulation study, we compare the revenue performance of this adaptive approach to a more traditional method that combines a censored forecasting method with a common seat allocation heuristic (EMSR-b).

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    File URL: http://dx.doi.org/10.1287/mnsc.46.6.760.11936
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 46 (2000)
    Issue (Month): 6 (June)
    Pages: 760-775

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    Handle: RePEc:inm:ormnsc:v:46:y:2000:i:6:p:760-775

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    Related research

    Keywords: yield management; revenue management; airlines; forecasting; optimization; fare class allocation; distribution free; adaptive algorithms; stochastic approximation;

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    Cited by:
    1. Pak, K. & Piersma, N., 2002. "Airline revenue management: an overview of OR techniques 1982-2001," Econometric Institute Research Papers EI 2002-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Chiang, David Ming-Huang & Wu, Andy Wei-Di, 2011. "Discrete-order admission ATP model with joint effect of margin and order size in a MTO environment," International Journal of Production Economics, Elsevier, vol. 133(2), pages 761-775, October.
    3. Pak, K. & Piersma, N., 2002. "airline revenue management," ERIM Report Series Research in Management ERS-2002-12-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
    4. Becher, Michael, 2009. "Simultaneous capacity and price control based on fuzzy controllers," International Journal of Production Economics, Elsevier, vol. 121(2), pages 365-382, October.
    5. Hung, Yi-Feng & Chen, Chien-Hao, 2013. "An effective dynamic decision policy for the revenue management of an airline flight," International Journal of Production Economics, Elsevier, vol. 144(2), pages 440-450.

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