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The history of unconstraining models in revenue management

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  • Larry Weatherford

    (College of Business)

Abstract

Unconstraining has been used in revenue management (RM) for nearly the last 40 years. This brief, historical article surveys over 40 articles from the recent period and traces the evolution of RM unconstraining models. The two main areas that are covered are general articles comparing the performance of different unconstraining methods under a variety of circumstances and more recently, customer choice models related to unconstraining censored data.

Suggested Citation

  • Larry Weatherford, 2016. "The history of unconstraining models in revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(3), pages 222-228, July.
    • Handle: RePEc:pal:jorapm:v:15:y:2016:i:3:d:10.1057_rpm.2016.10
    DOI: 10.1057/rpm.2016.10
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    References listed on IDEAS

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    1. Steven Nahmias, 1994. "Demand estimation in lost sales inventory systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 41(6), pages 739-757, October.
    2. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    3. S. L. Brumelle & J. I. McGill & T. H. Oum & K. Sawaki & M. W. Tretheway, 1990. "Allocation of Airline Seats between Stochastically Dependent Demands," Transportation Science, INFORMS, vol. 24(3), pages 183-192, August.
    4. Gustavo Vulcano & Garrett van Ryzin & Richard Ratliff, 2012. "Estimating Primary Demand for Substitutable Products from Sales Transaction Data," Operations Research, INFORMS, vol. 60(2), pages 313-334, April.
    5. Barry C. Smith & John F. Leimkuhler & Ross M. Darrow, 1992. "Yield Management at American Airlines," Interfaces, INFORMS, vol. 22(1), pages 8-31, February.
    6. Wen-Chyuan Chiang & Jason C.H. Chen & Xiaojing Xu, 2007. "An overview of research on revenue management: current issues and future research," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 1(1), pages 97-128.
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    Cited by:

    1. Timothy Webb, 2022. "Forecasting at capacity: the bias of unconstrained forecasts in model evaluation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(6), pages 645-656, December.
    2. Ian Yeoman, 2022. "Ukraine, price and inflation," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(3), pages 253-254, June.
    3. Evan Fields & Carolina Osorio & Tianli Zhou, 2021. "A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand," Transportation Science, INFORMS, vol. 55(3), pages 616-636, May.
    4. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
    5. Kavitha Balaiyan & R. K. Amit & Atul Kumar Malik & Xiaodong Luo & Amit Agarwal, 2019. "Joint forecasting for airline pricing and revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(6), pages 465-482, December.
    6. Philipp Bartke & Natalia Kliewer & Catherine Cleophas, 2018. "Benchmarking filter-based demand estimates for airline revenue management," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(1), pages 57-88, March.
    7. Wang, Jianbiao & Miwa, Tomio & Morikawa, Takayuki, 2023. "Recursive decomposition probability model for demand estimation of street-hailing taxis utilizing GPS trajectory data," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 171-195.

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