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Optimal Starting Times for End-of-Season Sales and Optimal Stopping Times for Promotional Fares

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Author Info

  • Youyi Feng

    (Department of Industrial Engineering and Operational Research, Columbia University, New York, New York 10027)

  • Guillermo Gallego

    (Department of Industrial Engineering and Operational Research, Columbia University, New York, New York 10027)

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    Abstract

    Many industries face the problem of selling a fixed stock of items over a finite horizon. These industries include airlines selling seats before planes depart, hotels renting rooms before midnight, theaters selling seats before curtain time, and retailers selling seasonal goods such as air-conditioners or winter coats before the end of the season. Given a fixed number of seats, rooms, or coats, the objective for these industries is to maximize revenues in excess of salvage value. When demand is price sensitive and stochastic, pricing is an effective tool to maximize revenues. In this paper we address the problem of deciding the optimal timing of a single price change from a given initial price to either a given lower or higher second price. Under mild conditions, we show that it is optimal to decrease (resp., to increase) the initial price as soon as the time-to-go falls below (resp., above) a time threshold that depends on the number of yet unsold items.

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    File URL: http://dx.doi.org/10.1287/mnsc.41.8.1371
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 41 (1995)
    Issue (Month): 8 (August)
    Pages: 1371-1391

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    Handle: RePEc:inm:ormnsc:v:41:y:1995:i:8:p:1371-1391

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    Related research

    Keywords: dynamic pricing; yield management; stopping times; intensity control; martingales; finite horizon; optimal policies;

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    Cited by:
    1. Jørgensen, Steffen & Kort, Peter M. & Zaccour, Georges, 2009. "Optimal pricing and advertising policies for an entertainment event," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 583-596, March.
    2. Chen, Jing & Bell, Peter C., 2012. "Implementing market segmentation using full-refund and no-refund customer returns policies in a dual-channel supply chain structure," International Journal of Production Economics, Elsevier, vol. 136(1), pages 56-66.
    3. Khouja, Moutaz, 1999. "The single-period (news-vendor) problem: literature review and suggestions for future research," Omega, Elsevier, vol. 27(5), pages 537-553, October.
    4. Chatwin, Richard E., 2000. "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices," European Journal of Operational Research, Elsevier, vol. 125(1), pages 149-174, August.
    5. 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.
    6. Pascal Courty & Li Hao, 1998. "Timing of seasonal sales," Economics Working Papers 331, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Hernández-Mireles, C. & Fok, D. & Franses, Ph.H.B.F., 2008. "The Triggers, Timing and Speed of New Product Price Landings," ERIM Report Series Research in Management ERS-2008-044-MKT, 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.
    8. Anjos, Miguel F. & Cheng, Russell C. H. & Currie, Christine S. M., 2005. "Optimal pricing policies for perishable products," European Journal of Operational Research, Elsevier, vol. 166(1), pages 246-254, October.
    9. X. DHaultfoeuille & P. Fevrier & L. Wilner, 2012. "Demand Estimation in the Presence of Revenue Management," Documents de Travail de la DESE - Working Papers of the DESE g2012-13, Institut National de la Statistique et des Etudes Economiques, DESE.
    10. 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.
    11. Oded Koenigsberg & Eitan Muller & Naufel Vilcassim, 2008. "easyJet® pricing strategy: Should low-fare airlines offer last-minute deals?," Quantitative Marketing and Economics, Springer, vol. 6(3), pages 279-297, September.
    12. Barut, M. & Sridharan, V, 2004. "Design and evaluation of a dynamic capacity apportionment procedure," European Journal of Operational Research, Elsevier, vol. 155(1), pages 112-133, May.
    13. Fabian Dickmann & Nikolaus Schweizer, 2014. "Faster Comparison of Stopping Times by Nested Conditional Monte Carlo," Papers 1402.0243, arXiv.org.
    14. Nair, Anand & Closs, David J., 2006. "An examination of the impact of coordinating supply chain policies and price markdowns on short lifecycle product retail performance," International Journal of Production Economics, Elsevier, vol. 102(2), pages 379-392, August.

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