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Selfish Revenue Sharing Mechanisms for Airline Alliances

In: Fair Revenue Sharing Mechanisms for Strategic Passenger Airline Alliances

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  • Demet Çetiner

Abstract

So far we have assumed that revenue management decisions of an alliance are made centrally such that the airlines maximize the revenue of the whole alliance. However, in an airline alliance, each airline makes its seat allocation decisions selfishly with the aim of maximizing its own revenue. In this chapter, we define several revenue sharing mechanisms, which are applied in a selfish setting and propose a method to evaluate the fairness of these mechanisms. The method includes a simulation model for the booking process of the alliance and uses the nucleolus-based revenue allocations as a benchmark. Through a numerical study, the fairness of the allocation mechanisms is compared with each other and against a random revenue sharing approach. In a further analysis, the fairness evaluation is performed by benchmarking the allocations to the Owen set-based allocations. Consistent results have been drawn from the two analyses.

Suggested Citation

  • Demet Çetiner, 2013. "Selfish Revenue Sharing Mechanisms for Airline Alliances," Lecture Notes in Economics and Mathematical Systems, in: Fair Revenue Sharing Mechanisms for Strategic Passenger Airline Alliances, edition 127, chapter 0, pages 111-132, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-35822-7_7
    DOI: 10.1007/978-3-642-35822-7_7
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    Citations

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    Cited by:

    1. M. G. Fiestras-Janeiro & I. García-Jurado & A. Meca & M. A. Mosquera, 2020. "On benefits of cooperation under strategic power," Annals of Operations Research, Springer, vol. 288(1), pages 285-306, May.
    2. Algaba, Encarnación & Fragnelli, Vito & Llorca, Natividad & Sánchez-Soriano, Joaquin, 2019. "Horizontal cooperation in a multimodal public transport system: The profit allocation problem," European Journal of Operational Research, Elsevier, vol. 275(2), pages 659-665.
    3. Grauberger, W. & Kimms, A., 2014. "Computing approximate Nash equilibria in general network revenue management games," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1008-1020.
    4. Grauberger, Waldemar & Kimms, Alf, 2016. "Revenue management under horizontal and vertical competition within airline alliances," Omega, Elsevier, vol. 59(PB), pages 228-237.
    5. Clempner, Julio B., 2020. "Penalizing passenger’s transfer time in computing airlines revenue," Omega, Elsevier, vol. 97(C).
    6. Luo, Chunlin & Zhou, Xiaoyang & Lev, Benjamin, 2022. "Core, shapley value, nucleolus and nash bargaining solution: A Survey of recent developments and applications in operations management," Omega, Elsevier, vol. 110(C).
    7. W. Grauberger & A. Kimms, 2018. "Computing pure Nash equilibria in network revenue management games," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(2), pages 481-516, March.

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