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Journal of Revenue and Pricing Management

, Volume 18, Issue 2, pp 164–180 | Cite as

Fluid arrivals simulation for choice network revenue management

  • Thibault BarbierEmail author
  • Miguel Anjos
  • Fabien Cirinei
  • Gilles Savard
Research Article
  • 43 Downloads

Abstract

Since the beginning of revenue management, simulation has been used to estimate the expected revenue resulting from an availability policy. It has also been used to verify the quality of forecasts by projecting them onto past availability policies. Recently, it has been used in simulation-based optimization approaches to find the best policy. Simulation thus has a central role in revenue management. We focus on the choice network revenue management (CNRM) problem that incorporates multiple resources and customer behavior. The traditional CNRM simulation is based on discrete customer arrivals; we propose a new approach based on fluid arrivals. Our estimator is biased, but we observe that the bias is often insignificant in practice and appears to be asymptotically null. Our approach consistently outperforms the traditional simulation in terms of estimation time and is thus a better choice for large instances. We also prove that it is equivalent to an approximation for the CNRM availability policy optimization problem. This equivalence limits the value of simulation-based optimization methods but allows us to propose heuristics to rapidly support the optimization.

Keywords

Revenue management Fluid arrivals simulation Choice behavior Availability control Simulation-based optimization 

Notes

Acknowledgements

The authors are grateful to the Natural Sciences and Engineering Research Council of Canada (NSERC), the Fonds de recherche du Quebec en nature et technologies (FRQNT), and ExPretio technologies for funding and supporting this research.

References

  1. Belobaba, P., and C. Hopperstad. 1999. Boeing/mit simulation study: PODS results update. In 1999 AGIFORS reservations and yield management study. MIT simulation Study: PODS results update, 1999 AGIFORS reservations and yield management study group symposium, April.Google Scholar
  2. Bertsimas, D., and S. de Boer. 2005. Simulation-based booking limits for airline revenue management. Operations Research 53 (1): 90–106.CrossRefGoogle Scholar
  3. Bijvank, M., P. L’Ecuyer, and P. Marcotte. 2011. RMSim: A java library for simulating revenue management systems. In Proceedings of the winter simulation conference. Winter simulation conference, 2703–2714.Google Scholar
  4. Bront, J.J.M., I. Méndez-Díaz, and G. Vulcano. 2009. A column generation algorithm for choice-based network revenue management. Operations Research 57 (3): 769–784.CrossRefGoogle Scholar
  5. Carrier, E., 2003. Modeling airline passenger choice: Passenger preference for schedule in the passenger origin-destination simulator (PODS). Ph.D. thesis, Massachusetts Institute of Technology.Google Scholar
  6. Carrier, E., and L. Weatherford. 2015. Implementation of mnl choice models in the passenger origin-destination simulator (PODS). Journal of Revenue and Pricing Management 14 (6): 400–407.CrossRefGoogle Scholar
  7. Chaneton, J.M., and G. Vulcano. 2011. Computing bid prices for revenue management under customer choice behavior. Manufacturing and Service Operations Management 13 (4): 452–470.CrossRefGoogle Scholar
  8. Cleophas, C., M. Frank, and N. Kliewer. 2009. Simulation-based key performance indicators for evaluating the quality of airline demand forecasting. Journal of Revenue and Pricing Management 8 (4): 330–342.CrossRefGoogle Scholar
  9. Darot, J.F.J. 2001. Revenue management for airline alliances: Passenger origin-destination simulation analysis. Ph.D. thesis, Massachusetts Institute of Technology.Google Scholar
  10. Doreswamy, G.R., A.S. Kothari, and S. Tirumalachetty. 2015. Simulating the flavors of revenue management for airlines. Journal of Revenue and Pricing Management 14 (6): 421–432.CrossRefGoogle Scholar
  11. Eguchi, T., and P.P. Belobaba. 2004. Modelling and simulation of impact of revenue management on Japan’s domestic market. Journal of Revenue and Pricing Management 3 (2): 119–142.CrossRefGoogle Scholar
  12. Figueiredo, D.R., B. Liu, Y. Guo, J. Kurose, and D. Towsley. 2006. On the efficiency of fluid simulation of networks. Computer Networks 50 (12): 1974–1994.CrossRefGoogle Scholar
  13. Fiig, T., R. Härdling, S. Pölt, and C. Hopperstad. 2014. Demand forecasting and measuring forecast accuracy in general fare structures. Journal of Revenue and Pricing Management 13 (6): 413–439.CrossRefGoogle Scholar
  14. Frank, M., M. Friedemann, M. Mederer, and A. Schroeder. 2006. Airline revenue management: A simulation of dynamic capacity management. Journal of Revenue and Pricing Management 5 (1): 62–71.CrossRefGoogle Scholar
  15. Frank, M., M. Friedemann, and A. Schrder. 2008. Principles for simulations in revenue management. Journal of Revenue and Pricing Management 7 (1): 7–16.CrossRefGoogle Scholar
  16. Gilks, W.R., S. Richardson, and D. Spiegelhalter. 1995. Markov chain Monte Carlo in practice. Boca Raton: CRC Press.CrossRefGoogle Scholar
  17. Gorin, T., and P.P. Belobaba. 2004. Special issue papers: Revenue management performance in a low-fare airline environment: Insights from the passenger origindestination simulator. Journal of Revenue and Pricing Management 3 (3): 215–236.CrossRefGoogle Scholar
  18. Gosavi, A. 2015. Simulation-based optimization.Google Scholar
  19. Gosavi, A., E. Ozkaya, and A.F. Kahraman. 2005. Simulation optimization for revenue management of airlines with cancellations and overbooking. OR Spectr. 29 (1): 21–38.CrossRefGoogle Scholar
  20. Kesidis, G., A. Singh, D. Cheung, W. Kwok. 1996. Feasibility of fluid event-driven simulation for ATM networks. In IEEE global telecommunications conference, vol. 3, 2013–2017.Google Scholar
  21. Kunnumkal, S., and H. Topaloglu. 2011. A randomized linear program for the network revenue management problem with customer choice behavior. Journal of Revenue and Pricing management 10 (5): 455–470.CrossRefGoogle Scholar
  22. Liu, Q., and G. van Ryzin. 2008. On the choice-based linear programming model for network revenue management. Manufacturing and Service Operations Management 10 (2): 288–310.CrossRefGoogle Scholar
  23. Meissner, J., A. Strauss, and K. Talluri. 2013. An enhanced concave program relaxation for choice network revenue management. Production and Operations Management 22 (1): 71–87.CrossRefGoogle Scholar
  24. Spall, J.C. 1998. An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest 19 (4): 482–492.Google Scholar
  25. Talluri, K. 2010. A randomized concave programming method for choice network revenue management. Working Papers (Departamento de Economía y Empresa, Universitat Pompeu Fabra).Google Scholar
  26. Talluri, K. 2014. New formulations for choice network revenue management. INFORMS Journal on Computing 26 (2): 401–413.CrossRefGoogle Scholar
  27. Talluri, K., and G. van Ryzin. 2004. The theory and practice of revenue management, vol. 68. New York: Springer.CrossRefGoogle Scholar
  28. van Ryzin, G., and G. Vulcano. 2008a. Computing virtual nesting controls for network revenue management under customer choice behavior. Manufacturing and Service Operations Management 10 (3): 448–467.CrossRefGoogle Scholar
  29. van Ryzin, G., and G. Vulcano. 2008b. Simulation-based optimization of virtual nesting controls for network revenue management. Operations Research 56 (4): 865–880.CrossRefGoogle Scholar
  30. van Ryzin, G., and G. Vulcano. 2015. A market discovery algorithm to estimate a general class of nonparametric choice models. Management Science 61 (2): 281–300.CrossRefGoogle Scholar
  31. van Ryzin, G., and G. Vulcano. 2017. Technical note–an expectation-maximization method to estimate a rank-based choice model of demand. Operations Research 65 (2): 396–407.CrossRefGoogle Scholar
  32. Weatherford, L.R. 2013. Improved revenues from various unconstraining methods in a passenger origin-destination simulator (PODS) environment with semi-restricted fares. Journal of Revenue and Pricing Management 12 (1): 60–82.CrossRefGoogle Scholar

Copyright information

© Springer Nature Limited 2018

Authors and Affiliations

  • Thibault Barbier
    • 1
    Email author
  • Miguel Anjos
    • 1
  • Fabien Cirinei
    • 2
  • Gilles Savard
    • 1
  1. 1.Department of Industrial and Mathematical EngineeringEcole Polytechnique MontrealMontrealCanada
  2. 2.ExPretio TechnologiesMontrealCanada

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