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Alternative risk-averse approaches for airline network revenue management

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  • Terciyanlı, Erman
  • Avṣar, Zeynep Müge

Abstract

In this study, seat inventory control is considered for airline networks. Alternative optimization models are proposed for risk-averse decision makers by incorporating the following measure: lower-semideviation of revenue from a given threshold level or expected revenue. Performance of the proposed models is tested in a simulation model for a sample network under different scenarios by using a nesting heuristic and simulating arrival pattern of the airline demand.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transe:v:125:y:2019:i:c:p:27-46
    DOI: 10.1016/j.tre.2019.02.002
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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.
    2. de Boer, Sanne V. & Freling, Richard & Piersma, Nanda, 2002. "Mathematical programming for network revenue management revisited," European Journal of Operational Research, Elsevier, vol. 137(1), pages 72-92, February.
    3. 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.
    4. Kalyan Talluri & Garrett van Ryzin, 1999. "A Randomized Linear Programming Method for Computing Network Bid Prices," Transportation Science, INFORMS, vol. 33(2), pages 207-216, May.
    5. Youyi Feng & Baichun Xiao, 2008. "Technical Note---A Risk-Sensitive Model for Managing Perishable Products," Operations Research, INFORMS, vol. 56(5), pages 1305-1311, October.
    6. 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.
    7. Popovic, Jovan B., 1987. "Decision making on stock levels in cases of uncertain demand rate," European Journal of Operational Research, Elsevier, vol. 32(2), pages 276-290, November.
    8. H Xiong & J Xie & X Deng, 2011. "Risk-averse decision making in overbooking problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1655-1665, September.
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    Cited by:

    1. Liang, Jinpeng & Li, Liming & Zheng, Jianfeng & Tan, Zhijia, 2023. "Service-oriented container slot allocation policy under stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).

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