Computational Management Science

, Volume 12, Issue 1, pp 99–109 | Cite as

The impact of customer behavior models on revenue management systems

  • Shadi Sharif Azadeh
  • M. Hosseinalifam
  • G. Savard
Original Paper


Revenue management (RM) can be considered an application of operations research in the transportation industry. For these service companies, it is a difficult task to adjust supply and demand. In order to maximize revenue, RM systems display demand behavior by using historical data. Usually, parametric methods are applied to estimate the probability of choosing a product at a given time. However, parameter estimation becomes challenging when we need to deal with constrained data. In this research, we evaluate the performance of a revenue management system when a non-parametric method for choice probability estimation is chosen. The outcomes of this method have been compared to the total expected revenue using synthetic data.


Revenue management Parametric and non-parametric demand models Customer choice behaviour 


  1. Bront J, Méndez-Díaz I, Vulcano G (2009) A column generation algorithm for choice-based network revenue management. Oper Res 57(3):769–784CrossRefGoogle Scholar
  2. Chaneton JM, Vulcano G (2011) Computing bid prices for revenue management under customer choice behavior. Manuf Serv Oper Manag 13(4):452–470Google Scholar
  3. Cooper W, Homem-de Mello T, Kleywegt A (2006) Models of the spiral-down effect in revenue management. Oper Res 54(5):968–987CrossRefGoogle Scholar
  4. Gallego G, Iyengar G, Phillips R, Dubey A (2004) Managing flexible products on a network. Working paper, Columbia University, New YorkGoogle Scholar
  5. Kunnumkal S, Topaloglu H (2008) A refined deterministic linear program for the network revenue management problem with customer choice behavior. Nav Res Logist (NRL) 55(6):563–580CrossRefGoogle Scholar
  6. Liu Q, van Ryzin G (2008) On the choice-based linear programming model for network revenue management. Manuf Serv Oper Manag 10(2):288–310Google Scholar
  7. McFadden D (2001) Disaggregate behavioral travel demand’s RUM side. In: Travel behaviour research. Pergamon Press, OxfordGoogle Scholar
  8. Simpson RW (1989) Using network flow techniques to find shadow prices for market and seat inventory control. In: Proceedings of technical report memorandum, M89–1, Flight Transportation Laboratory, MIT, CambridgeGoogle Scholar
  9. Talluri K, van Ryzin G (2004) Revenue management under a general discrete choice model of consumer behavior. Manag Sci 50(1):15–33CrossRefGoogle Scholar
  10. Train K (2009) Discrete choice methods with simulation, 2nd edn. Cambridge university press, CambridgeCrossRefGoogle Scholar
  11. van Ryzin G (2005) Future of revenue management: models of demand. J Revenue Pricing Manag 4(2):204–210CrossRefGoogle Scholar
  12. Weatherford L (2000) Unconstraining methods. In: AGIFORS Reservations and Yield Management Study GroupGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Shadi Sharif Azadeh
    • 1
  • M. Hosseinalifam
    • 1
  • G. Savard
    • 1
  1. 1.Polytechnique MontrealMontréalCanada

Personalised recommendations