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Value-at-risk optimal policies for revenue management problems

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  • Koenig, Matthias
  • Meissner, Joern

Abstract

Consider a single-leg dynamic revenue management problem with fare classes controlled by capacity in a risk-averse setting. The revenue management strategy aims at limiting the down-side risk, and in particular, value-at-risk. A value-at-risk optimised policy offers an advantage when considering applications which do not allow for a large number of reiterations. They allow for specifying a confidence level regarding undesired scenarios.

Suggested Citation

  • Koenig, Matthias & Meissner, Joern, 2015. "Value-at-risk optimal policies for revenue management problems," International Journal of Production Economics, Elsevier, vol. 166(C), pages 11-19.
    • Handle: RePEc:eee:proeco:v:166:y:2015:i:c:p:11-19
    DOI: 10.1016/j.ijpe.2015.03.027
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    References listed on IDEAS

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    1. Matthias Koenig & Joern Meissner, 2016. "Risk minimising strategies for revenue management problems with target values," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(3), pages 402-411, March.
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    5. Ferrer, Juan-Carlos & Oyarzún, Diego & Vera, Jorge, 2012. "Risk averse retail pricing with robust demand forecasting," International Journal of Production Economics, Elsevier, vol. 136(1), pages 151-160.
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    8. Koenig, Matthias & Meissner, Joern, 2010. "List pricing versus dynamic pricing: Impact on the revenue risk," European Journal of Operational Research, Elsevier, vol. 204(3), pages 505-512, August.
    9. Yuri Levin & Jeff McGill & Mikhail Nediak, 2008. "Risk in Revenue Management and Dynamic Pricing," Operations Research, INFORMS, vol. 56(2), pages 326-343, April.
    10. Kang Boda & Jerzy Filar, 2006. "Time Consistent Dynamic Risk Measures," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 63(1), pages 169-186, February.
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    12. 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. Schlosser, Rainer & Gönsch, Jochen, 2023. "Risk-averse dynamic pricing using mean-semivariance optimization," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1151-1163.
    2. Terciyanlı, Erman & Avṣar, Zeynep Müge, 2019. "Alternative risk-averse approaches for airline network revenue management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 27-46.
    3. Peter Buchholz & Iryna Dohndorf, 2021. "A multi-objective approach for PH-graphs with applications to stochastic shortest paths," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 93(1), pages 153-178, February.
    4. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    5. Sebastian Koch & Jochen Gönsch & Michael Hassler & Robert Klein, 2016. "Practical decision rules for risk-averse revenue management using simulation-based optimization," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 15(6), pages 468-487, December.
    6. Jochen Gönsch & Michael Hassler & Rouven Schur, 2018. "Optimizing conditional value-at-risk in dynamic pricing," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(3), pages 711-750, July.
    7. Catherine Cleophas & Daniel Kadatz & Sebastian Vock, 2017. "Resilient revenue management: a literature survey of recent theoretical advances," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(5), pages 483-498, October.
    8. Schur, Rouven & Gönsch, Jochen & Hassler, Michael, 2019. "Time-consistent, risk-averse dynamic pricing," European Journal of Operational Research, Elsevier, vol. 277(2), pages 587-603.
    9. Gönsch, Jochen, 2017. "A survey on risk-averse and robust revenue management," European Journal of Operational Research, Elsevier, vol. 263(2), pages 337-348.
    10. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.

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    More about this item

    Keywords

    Capacity control; Revenue management; Risk; Value-at-risk;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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