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Risk minimising strategies for revenue management problems with target values

Author

Listed:
  • Matthias Koenig

    (Bielefeld University of Applied Sciences, Minden, Germany)

  • Joern Meissner

    (Kuehne Logistics University, Hamburg, Germany)

Abstract

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 maximising the expected revenue, the decision maker has the main objective of minimising 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 that generates the policy minimising 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, 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.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:3:p:402-411
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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. Gönsch, Jochen, 2017. "A survey on risk-averse and robust revenue management," European Journal of Operational Research, Elsevier, vol. 263(2), pages 337-348.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

    More about this item

    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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