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Risk Minimizing Strategies for Revenue Management Problems with Target Values


  • Matthias Koenig

    (Department of Management Science, Lancaster University Management School)

  • Joern Meissner

    (Department of Management Science, Lancaster University Management School)


Consider a risk-averse decision maker in the setting of a single-leg dynamic revenue management problem with revenue controlled by limiting capacity for a fixed set of prices. Instead of focussing on maximizing the expected revenue, the decision maker has the main objective of minimizing the risk of failing to achieve a given target revenue. Interpreting the revenue management problem in the framework of finite Markov decision processes, we augment the state space of the risk-neutral problem definition and change the objective function to the probability of failing a certain specified target revenue. This enables us to obtain a dynamic programming solution which generates the policy minimizing the risk of not attaining this target revenue. We compare this solution with recently proposed risk-sensitive policies in a numerical study and discuss advantages and limitations.

Suggested Citation

  • Matthias Koenig & Joern Meissner, 2009. "Risk Minimizing Strategies for Revenue Management Problems with Target Values," Working Papers MRG/0015, Department of Management Science, Lancaster University, revised Oct 2014.
  • Handle: RePEc:lms:mansci:mrg-0015

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    1. repec:spr:compst:v:65:y:2007:i:3:p:565-579 is not listed on IDEAS
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Bergen, Mark & Ritson, Mark & Dutta, Shantanu & Levy, Daniel & Zbaracki, Mark, 2003. "Shattering the Myth of Costless Price Changes," European Management Journal, Elsevier, vol. 21(6), pages 663-669, December.
    4. Daniel Levy & Mark Bergen & Shantanu Dutta & Robert Venable, 1997. "The Magnitude of Menu Costs: Direct Evidence from Large U. S. Supermarket Chains," The Quarterly Journal of Economics, Oxford University Press, vol. 112(3), pages 791-824.
    5. Daniel Levy & Shantanu Dutta & Mark Bergen & Robert Venable, 1998. "Price adjustment at multiproduct retailers," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 19(2), pages 81-120.
    6. Luciano, Elisa & Peccati, Lorenzo & Cifarelli, Donato M., 2003. "VaR as a risk measure for multiperiod static inventory models," International Journal of Production Economics, Elsevier, vol. 81(1), pages 375-384, January.
    7. Mark J. Zbaracki & Mark Ritson & Daniel Levy & Shantanu Dutta & Mark Bergen, 2004. "Managerial and Customer Costs of Price Adjustment: Direct Evidence from Industrial Markets," The Review of Economics and Statistics, MIT Press, vol. 86(2), pages 514-533, May.
    8. Constantinos Maglaras & Joern Meissner, 2006. "Dynamic Pricing Strategies for Multiproduct Revenue Management Problems," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 136-148, July.
    9. Netessine, Serguei, 2006. "Dynamic pricing of inventory/capacity with infrequent price changes," European Journal of Operational Research, Elsevier, vol. 174(1), pages 553-580, October.
    10. C. Barz & K. Waldmann, 2007. "Risk-sensitive capacity control in revenue management," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 65(3), pages 565-579, June.
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    Cited by:

    1. 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.
    2. repec:eee:ejores:v:263:y:2017:i:2:p:337-348 is not listed on IDEAS

    More about this item


    capacity control; revenue management; multi-period; risk; target level criterion;

    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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