Skip to main content

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Algers, S. and Besser, M. (2001). Modelling choice of flight and booking class — a study using stated preference and revealed preference data. Intl. J. of Services Technology and Management, 2:28–45.

    Google Scholar 

  • Andersson, S. E. (1989). Operational planning in airline business — Can science improve efficiency? Experiences from SAS. European Journal of Operations Research, 43:3–12.

    Article  Google Scholar 

  • Beckman, M. J. and Bobkowski, F. (1958). Airline demand: An analysis of some frequency distributions. Naval Research Quarterly, 43(5):43–51.

    Google Scholar 

  • Beckmann, M. J. (1958). Decision and team problems in airline reservations. Econometrica, 26:134–145.

    MATH  Google Scholar 

  • Belobaba, P. P. (1987a). Air Travel Demand and Airline Seat Inventory Management. PhD thesis, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusettes.

    Google Scholar 

  • Belobaba, P. P. (1987b). Airline yield management: An overview of seat inventory control. Transportation Science, 21:63–73.

    Google Scholar 

  • Belobaba, P. P. (1989). Application of a probabilistic decision model to airline seat inventory control. Operations Research, 37:183–197.

    Google Scholar 

  • Belobaba, P. P. (1992). Optimal vs. heuristic methods for nested seat allocation. Presentation at ORS A/TIMS Joint National Meeting.

    Google Scholar 

  • Belobaba, P. P. (1998). PODS results update: Impacts of forecasting on O-D control methods. In 1998 AGIFORS Reservations and Yield Management Study Group Symposium, Melborne.

    Google Scholar 

  • Belobaba, P. P. (2001). Revenue and competitive impacts of O-D control: Summary of PODS results. In First Annual INFORMS Revenue Management Section Meeting, New York.

    Google Scholar 

  • Belobaba, P. P. and Lee, S. (2000). PODS update: Large network O-D control results. In 2000AGIFORS Reservations and Yield Management Study Group Symposium, New York.

    Google Scholar 

  • Belobaba, P. P. and Weatherford, L. R. (1996). Comparing decision rules that incorporate customer diversion in perishable asset revenue management situations. Decision Sciences, 27:343–363.

    Google Scholar 

  • Bertsimas, D. J. and Popescu, I. (2001). Revenue management in a dynamic network environment. Working Paper, Massachusettes Institute of Technology.

    Google Scholar 

  • Bhatia, A. V. and Parekh, S. C. (1973). Optimal allocation of seats by fare. Presentation to AGIFORS Reservations Study Group, Trans World Airlines.

    Google Scholar 

  • Bierman Jr., H. and Thomas, J. (1975). Airline overbooking strategies and bumping procedures. Public Policy, 21:601–606.

    Google Scholar 

  • Bratu, S. (1999). Network value concept in airline revenue management. Master’s thesis, Massachusettes Institute of Technology, Cambridge, Massachusettes. Department of Aeronautics and Astronatics.

    Google Scholar 

  • Brumelle, S. and Walczak, D. (1997). Dynamic allocation of airline seat inventory with batch arrivals. In 1997 Air Transport Research Group of the WCTR Society Proceedings 3, Vancouver, Faculty of Commerce and Business Administration, University of British Columbia.

    Google Scholar 

  • Brumelle, S. L. and McGill, J. I. (1993). Airline seat allocation with multiple nested fare classes. Operations Research, 41(1):127–137.

    Google Scholar 

  • Brumelle, S. L., McGill, J. I., Oum, T. H., Sawaki, K., and Tretheway, M. W. (1990). Allocation of airline seat between stochastically dependent demands. Transportation Science, 24:183–192.

    Google Scholar 

  • Chatwin, R. E. (1993). Optimal Airline Overbooking. PhD thesis, Stanford University, Palo Alto.

    Google Scholar 

  • Chatwin, R. E. (1997). Continuous-time airline overbooking with time dependent fares and refunds. Working paper, Applied Decision Analysis, Inc., Menlo Park, California.

    Google Scholar 

  • Chatwin, R. E. (1999). Mutiperiod airline overbooking with a single fare class. Operations Research, 46:805–819.

    Google Scholar 

  • Civil Aeronautics Board (1967). Civil Aeronautics Board Economic Regulations Docket 16563. Washington, D.C.

    Google Scholar 

  • Cooper, W. L. (2000). Asymptotic behavior of some revenue management policies. Working Paper, Univ. of Minnesota.

    Google Scholar 

  • Cross, R. G. (1997). Revenue Management: Hardcore Tactics for Market Domination. Broadway Books (Bantam, Doubleday, Dell Publishing Group), New York.

    Google Scholar 

  • Curry, R. E. (1990). Optimal airline seat allocation with fare classes nested by origins and destinations. Transportation Science, 24:193–204.

    Google Scholar 

  • Curry, R. E. (1992). Real-time revenue management: Bid price strategies for origins/destinations and legs. Scorecard, Aeronomics Inc., Atlanta, 2Q:n.a.

    Google Scholar 

  • Dror, M., Trudeau, P., and Ladany, S. P. (1988). Network models for seat allocation on flights. Transportation Research, 22B:239–250.

    Google Scholar 

  • D’Sylva, E. (1982). O-and-D seat assignment to maximize expected revenue. Technical report, Boeing Commercial Airplane Company, Seattle, Washington. Unpublished internal technical report.

    Google Scholar 

  • Glover, F., Glover, R., Lorenzo, J., and McMillan, C. (1982). The passenger mix problem in the scheduled airlines. Interfaces, 12:73–79.

    Google Scholar 

  • Kaplan, A. (1969). Stock rationing. Management Science, 15:260–267.

    Google Scholar 

  • Kleywegt, A. J. and Papastavrou, J. D. (1998). The dynamic and stochastic knapsack problem. Operations Research, 46:17–35.

    MathSciNet  Google Scholar 

  • Lautenbacher, C. J. and Stidham, S. J. (1999). The underlying markov decision process in the single-leg airline yield management problem. Transportation Science, 34:136–146.

    Google Scholar 

  • Lee, T. C. and Hersh, M. (1993). A model for dynamic airline seat inventory control with multiple seat bookings. Transportation Science, 27:252–265.

    Google Scholar 

  • Liang, Y. (1999). Solution to the continuous time dynamic yield management model. Transportation Science, 33:117–123.

    MATH  Google Scholar 

  • Littlewood, K. (1972). Forecasting and control of passenger bookings. In Proceedings of the Twelfth AnnualAGIFORS Symposium, Nathanya, Israel.

    Google Scholar 

  • Martinez, R. and Sanchez, M. (1970). Automatic booking level control. In Proceedings of the Tenth AGIFORS Symposium.

    Google Scholar 

  • McGill, J. I. and van Ryzin, G. J. (1999). Revenue management: Research overview and prospects. Transportation Science, 33(2):233–256.

    Google Scholar 

  • Phillips, R. L. (1994). A marginal value approach to airline origin and destination revenue management. In Henry, J. and Yvon, P., editors, Proceedings of the 16th Conference on System Modeling and Optimization, New York. Springer-Verlag.

    Google Scholar 

  • Robinson, L. W. (1995). Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Operations Research, 43:252–263.

    MATH  Google Scholar 

  • Rothstein, M. (1971). Airline overbooking: The state of the art. J. Trans. Econ. & Policy, 5:96–99.

    MathSciNet  Google Scholar 

  • Rothstein, M. (1975). Airline overbooking: Fresh approaches are needed. Transportation Science, 2:169–173.

    Google Scholar 

  • Rothstein, M. (1985). O.R. and the airline overbooking problem. Operations Research, 33:237–248.

    Google Scholar 

  • Rothstein, M. and Stone, A. W. (1967). Passenger booking levels. In Proceedings of the Seventh AGIFORS Symposium.

    Google Scholar 

  • Shlifer, E. and Vardi, Y. (1975). An airline overbooking policy. Transportation Science, 9:101–114.

    Google Scholar 

  • Simon, J. L. (1968). An almost practical solution to airline overbooking. J. Trans. Econ. & Policy, 2:201–202.

    Google Scholar 

  • Simon, J. L. (1972). Airline overbooking: The state of the art — a reply. J. Trans. Econ. & Policy, 6:255–256.

    Google Scholar 

  • Simon, J. L. (1993). The airline oversales auction plan: How it was adopted and how it has fared. In Fifth IATA Revenue Management Conference, Montreal.

    Google Scholar 

  • Simon, J. L. (1994). The airline oversales auction plan: The results. J. Trans. Econ. & Policy, 28:319–323.

    Google Scholar 

  • Simpson, R. W. (1989). Using network flow techniques to find shadow prices for market and seat inventory control. Technical Report Memorandum, M89-1, MIT Flight Transportation Laboratory, Cambridge, Massachusettes.

    Google Scholar 

  • Smith, B. C. and Penn, C. W. (1988). Analysis of alternative origin-destination control strategies. In Proceedings of the Twenty Eighth Annual AGIFORS Symposium, New Seabury, Massachusettes.

    Google Scholar 

  • Smith, B. C., Leimkuhler, J. F., and Darrow, R. M. (1992). Yield management at american airlines. Interfaces, 22:8–31.

    Google Scholar 

  • Subramanian, J., Stidham Jr., S., and Lautenbacher, C. (1999). Airline yield management with overbooking, cancellations and no-shows. Transportation Science, 33:147–167.

    Google Scholar 

  • Talluri, K. T. and van Ryzin, G. J. (1999a). An analysis of bid-price controls for network revenue management. Management Science, 44:1577–1593.

    Google Scholar 

  • Talluri, K. T. and van Ryzin, G. J. (1999b). A randomized linear programming method for computing network bid prices. Transportation Science, 33:207–216.

    Google Scholar 

  • Talluri, K. T. and van Ryzin, G. J. (2001). Revenue management under a general discrete choice model of consumer behavior. Working paper, Graduate School of Business, Columbia University.

    Google Scholar 

  • Talluri, K. T. and van Ryzin, G. J. (2002). The Theory and Practice of Revenue Management. Kluwer Academic Publishers, Dordrecht, The Netherlands. To be published.

    Google Scholar 

  • Taylor, C. J. (1962). The determination of passenger booking levels. In Proceedings of the Second AGIFORS Symposium.

    Google Scholar 

  • Thompson, H.R. (1961). Statistical problems in airline reservation control. Oper. Res. Quart., 12:167–185.

    Google Scholar 

  • Topkis, D. M. (1968). Optimal ordering and rationing policies in a nonstationary dynamic inventory model with n demand classes. Management Science, 15:160–176.

    Google Scholar 

  • Van Ryzin, G. J. and McGill, J. I. (2000). Revenue management without forecasting or optimization: An adaptive algorithm for determining seat protection levels. Management Science, 46:760–775.

    Article  Google Scholar 

  • Van Slyke, R. and Young, Y. (2000). Finite horizon stochastic knapsacks with applications to yield management. Operations Research, 48:155–172.

    MathSciNet  Google Scholar 

  • Vinod, B. (1989). A set partitioning algorithm for virtual nesting indexing using dynamic programming. Technical report, Internal Technical Report, SABRE Decision Technologies.

    Google Scholar 

  • Vinod, B. (1995). Origin-and-destination yield management. In Jenkins, D., editor, The Handbook of Airline Economics, pages 459–468. The Aviation Weekly Group of the McGraw-Hill Companies, New York.

    Google Scholar 

  • Williamson, E. L. (1988). Comparison of optimization techniques for origin-destination seat inventory control. Master’s thesis, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusettes.

    Google Scholar 

  • Williamson, E. L. (1992). Airline Network Seat Inventory Control: Methodologies and Revenue Impacts. PhD thesis, Flight Transportation Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusettes.

    Google Scholar 

  • Wollmer, R. D. (1986). A hub-spoke seat management model. Unpublished company report, Douglas Aircraft Company, McDonnell Douglas Corporation.

    Google Scholar 

  • Wollmer, R. D. (1992). An airline seat management model for a single leg route when lower fare classes book first. Operations Research, 40:26–37.

    MATH  Google Scholar 

  • Wong, J. T. (1990). AirlineNetworkSeatAllocation. PhD thesis, Northwestern University, Evanston, Ill.

    Google Scholar 

  • Wong, J. T., Koppelman, F. S., and Daskin, M. S. (1993). Flexible assignment approach to itinerary seat allocation. Transportation Research, 27B:33–48.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Kluwer Academic Publishers

About this chapter

Cite this chapter

van Ryzin, G.J., Talluri, K.T. (2003). Revenue Management. In: Hall, R.W. (eds) Handbook of Transportation Science. International Series in Operations Research & Management Science, vol 56. Springer, Boston, MA. https://doi.org/10.1007/0-306-48058-1_16

Download citation

  • DOI: https://doi.org/10.1007/0-306-48058-1_16

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-7246-8

  • Online ISBN: 978-0-306-48058-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics