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Approximate nucleolus-based revenue sharing in airline alliances

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  • Kimms, Alf
  • Çetiner, Demet

Abstract

Alliances allow the airlines to extend their networks and increase the number of destinations they can access. Different from the traditional single airline approach, in an alliance, partner airlines may sell tickets for the same itinerary. In addition, one itinerary may consist of several flight legs, each of which may be operated by a different airline. A major issue that needs to be addressed is how to share the revenue generated from selling a ticket for a product among the individual airlines in a fair way. The fair allocation of the revenue has a critical importance for the long-term stability of the alliance. We model the problem as a cooperative game and show that the core of the game is non-empty. We propose to use a revenue proration scheme based on the concept of the nucleolus. The numerical studies reveal that the revenue shares can effectively be computed even for large alliance networks.

Suggested Citation

  • Kimms, Alf & Çetiner, Demet, 2012. "Approximate nucleolus-based revenue sharing in airline alliances," European Journal of Operational Research, Elsevier, vol. 220(2), pages 510-521.
    • Handle: RePEc:eee:ejores:v:220:y:2012:i:2:p:510-521
    DOI: 10.1016/j.ejor.2012.01.057
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    2. Karaca, Orcun & Delikaraoglou, Stefanos & Hug, Gabriela & Kamgarpour, Maryam, 2022. "Enabling inter-area reserve exchange through stable benefit allocation mechanisms," Omega, Elsevier, vol. 113(C).
    3. Guajardo, Mario & Jörnsten, Kurt, 2015. "Common mistakes in computing the nucleolus," European Journal of Operational Research, Elsevier, vol. 241(3), pages 931-935.
    4. Minhyuk Sur & Deok-Joo Lee & Kyung-Taek Kim, 2019. "Optimal revenue sharing in platform markets: a Stackelberg model," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 18(4), pages 317-331, August.
    5. Yuntong Wang, 2016. "Revenue Sharing in Airline Alliance Networks," Working Papers 1605, University of Windsor, Department of Economics.
    6. Li, Deng-Feng, 2012. "A fast approach to compute fuzzy values of matrix games with payoffs of triangular fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 223(2), pages 421-429.
    7. Çetiner, D. & Kimms, A., 2013. "Assessing fairness of selfish revenue sharing mechanisms for airline alliances," Omega, Elsevier, vol. 41(4), pages 641-652.
    8. Mehmet Onur Olgun, 2022. "Collaborative airline revenue sharing game with grey demand data," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(3), pages 861-882, September.
    9. 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.
    10. Grauberger, Waldemar & Kimms, Alf, 2016. "Revenue management under horizontal and vertical competition within airline alliances," Omega, Elsevier, vol. 59(PB), pages 228-237.
    11. Clempner, Julio B., 2020. "Penalizing passenger’s transfer time in computing airlines revenue," Omega, Elsevier, vol. 97(C).
    12. 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).
    13. Lütkemeyer, Daniel & Heese, H. Sebastian & Wuttke, David A., 2021. "Overcoming inefficiencies in the development of personalized medicine," European Journal of Operational Research, Elsevier, vol. 290(1), pages 278-296.
    14. 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.
    15. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    16. Bin Zhang & Qingyao Xin & Min Tang & Niu Niu & Heran Du & Xiqiang Chang & Zhaohua Wang, 2022. "Revenue allocation for interfirm collaboration on carbon emission reduction: complete information in a big data context," Annals of Operations Research, Springer, vol. 316(1), pages 93-116, September.

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