Journal of Revenue and Pricing Management

, Volume 18, Issue 3, pp 213–227 | Cite as

Yield optimization for airlines from ticket resell

  • Benoit Lardeux
  • Gabrielle Sabatier
  • Thierry DelahayeEmail author
  • Mourad Boudia
  • Odile Tonnet
  • Pierre Mathieu
Research Article


During the reservation period of some flights, the demand significantly fluctuates due to changes in the business environment. Some demand variations can hardly be anticipated by airline revenue management systems. Therefore, it often happens that the sales policy applied at a given time in a flight turns out to be suboptimal a posteriori. A mechanism of ticket buy-back can then be an interesting tool for the airlines aiming at boosting revenue from these flights. This paper addresses the main stages of a buy-back process triggered by an airline. The process includes the revenue management-based solutions to support the selection of the tickets and the computation of the proposed prices to get them back. The main contribution of this paper is a new mathematical model which optimizes airline-expected revenue from buy-back according to the probability of passenger acceptance. This model can be applied for many different compensation schemes that could be put in place by an airline to spur some of their passengers to sell back air tickets to the airline. Three of them are further analyzed. We simulate buy-back campaigns for four flights with data drawn from real operations and compare additional revenue due to buy-back, according to the selected compensation scheme. Results emphasize our intuition that business benefits can be expected from a well-automated mechanism of ticket buy-back and resell in the airline industry. Depending on the flight and demand characteristics, up to over \(10\%\) additional revenue can be expected to be added on top of the revenue obtained from a standard revenue management system on a single flight.


Airline Buy-back Revenue management Passenger modelling 


  1. Akiva, M.B., and S. Lerman. 1985. Discrete choice analysis: Theory and application to travel demand. Cambridge: MIT Press.Google Scholar
  2. Alstrup, J., S. Boas, O.B.G. Madsen, and R.V. Valqui. 1986. Booking policy for flights with two types of passengers. European Journal of Operational Research 27: 274–288.CrossRefGoogle Scholar
  3. Avisell. 2018. Accessed 2018.
  4. Bell, P.C. 2008. Short selling and replaning as tools to enhance revenues. Journal of the Operational Research Society 59 (3): 313–321.CrossRefGoogle Scholar
  5. Belobaba, P. 1987. Survey paperairline yield management an overview of seat inventory control. Transportation Science 21 (2): 63–73.CrossRefGoogle Scholar
  6. Belobaba, P. 1989. Or practiceapplication of a probabilistic decision model to airline seat inventory control. Operations Research 37 (2): 183–197.CrossRefGoogle Scholar
  7. Berge, M., and C. Hopperstad. 1993. Demand driven dispatch: A method for dynamic aircraft capacity assignment, models and algorithms. Operations Research 41 (1): 153–168.CrossRefGoogle Scholar
  8. Caravelo. 2017. Accessed 2017.
  9. Chatwin, R. 1993. Optimal airline overbooking, PhD Thesis, Stanford University, CA.Google Scholar
  10. Chatwin, R. 1996. Multiperiod airline overbooking with multiple fare classes. Naval Research Logistics (NRL) 43 (5): 603–612.CrossRefGoogle Scholar
  11. Chatwin, R. 1999. Continuous-time airline overbooking with time-dependent fares and refunds. Transportation Science 33: 182–191.CrossRefGoogle Scholar
  12. Ekstein, N. 2017. United wants to sell your seat to someone else for more money. Bloomberg News. 12 July 2007.Google Scholar
  13. Fiig, T., N. Bondoux, R. Hjorth, and J. Larsen. 2016. Joint overbooking and seat allocation for fare families, presented at AGIFORS Revenue Management Study Group, 18–20 May, Frankfurt, Germany.Google Scholar
  14. Fiig, T., K. Isler, C. Hopperstad, and P. Belobaba. 2010. Optimization of mixed fare structures: Theory and applications. Journal of Revenue and Pricing Management 9 (1–2): 152–170.CrossRefGoogle Scholar
  15. Gallego, G., S.G. Kou, and R. Phillips. 2008. Revenue management of callable products. Management Science 54 (3): 550–564.CrossRefGoogle Scholar
  16. Gallego, G., and Lee, H. 2018. Callable products with early exercise and overbooking. In Proceedings of the AGIFORS Revenue Management Study Group.Google Scholar
  17. Klophaus, R., and S. Polt. 2007. Airline overbooking with dynamic spoilage costs. Journal of Revenue and Pricing Management 6 (1): 9–18.CrossRefGoogle Scholar
  18. Phillips, R. 2005. Pricing and revenue optimization.Google Scholar
  19. Plusgrade. 2017. Accessed 2017.
  20. Rothstein, M. 1968. Stochastic models for airline booking policies. PhD Thesis New-York University, New-York.Google Scholar
  21. Rothstein, M. 1985. Or and the airline overbooking model. Operations Research 33 (2): 237–248.CrossRefGoogle Scholar
  22. Rothstein, M. and Stone, A. 1967. Passenger booking levels. In Proceedings of AGIFORS symposium, 1967, New-York.Google Scholar
  23. Subramanian, J., S. Stidham, and C. Lautenbacher. 1999. Airline yield management with overbooking, cancellations, and no-shows. Transportation Science 33 (2): 147–167.CrossRefGoogle Scholar
  24. Swan, W. 1983. Traffic losses at high load factors. In Proceedings of the AGIFORS symposium.Google Scholar
  25. Swan, W. 2001. Spill modelling for airlines. Boeing marketing, technical paper.Google Scholar
  26. Talluri, K., and G.V. Ryzin. 2005. The theory and practice of revenue management.Google Scholar
  27. TransferTravel. 2017. Accessed 2017.
  28. Wang, W., and D. Walczak. 2015. Overbooking under dynamic and static policies. Journal of Revenue and Pricing Management 15 (6): 534–553.CrossRefGoogle Scholar

Copyright information

© Springer Nature Limited 2018

Authors and Affiliations

  • Benoit Lardeux
    • 1
  • Gabrielle Sabatier
    • 1
  • Thierry Delahaye
    • 1
    Email author
  • Mourad Boudia
    • 1
  • Odile Tonnet
    • 1
  • Pierre Mathieu
    • 1
  1. 1.Innovation and Research, AmadeusSophia AntipolisFrance

Personalised recommendations