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Revenue Management

  • Darius Walczak
  • E. Andrew Boyd
  • Roxy Cramer
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 169)

Abstract

Revenue management is arguably the most celebrated application of mathematical modeling used in the travel industry. Originating in the airline industry around the time of deregulation in the late 1970s, revenue management has consistently been credited with increasing airline revenues by over 6%.

Keywords

Exponentially Weighted Move Average Protection Level Discrete Choice Model Revenue Management Cancellation Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adelman D (2007) Dynamic bid prices in revenue management. Oper Res 55:647–661CrossRefGoogle Scholar
  2. Barz C, Waldmann K-H (2007) Risk-sensitive capacity control in revenue management. Math Methods Oper Res 65:565–579CrossRefGoogle Scholar
  3. Barz C, Uhrig-Homburg M, Waldmann K.-H (2006) Yield management: beyond expected revenue. Dreier T, Studer R, Weinhardt C (eds), Information management and market engineering, Universitätsverlag, Karlsruhe (TH), 71–84Google Scholar
  4. Belobaba PP (1987) Air travel demand and airline seat inventory management. Ph.D. thesis, Flight Transportation Laboratory, MIT, CambridgeGoogle Scholar
  5. Belobaba PP (1989) Application of a probabilistic decision model to airline seat inventory control. Oper Res 37(2):183–197CrossRefGoogle Scholar
  6. Belobaba PP (2004) Research highlights: pricing, distribution, and revenue management. Presentation to industry advisory board. See web.mit.edu/airlines/www/board-meetings/meeting-nov-2004/Pricing&RM-Belobaba.pdf. Accessed 1 May, 2007Google Scholar
  7. Belobaba PP, Weatherford LR (1996) Comparing decision rules that incorporate customer diversion in perishable assett revenue management situations. Decis Sci 27(2):343–363Google Scholar
  8. Bertsimas D, de Boer S (2005) Simulation-based booking limits for airline revenue management. Oper Res 53(1):90–106CrossRefGoogle Scholar
  9. Bertsimas D, Popescu I (2003) Revenue management in a dynamic network environment. Transp Sci 37(3):257–277CrossRefGoogle Scholar
  10. Boyd EA (2007) The future of pricing: how airline ticket pricing has inspired a revolution. Palgrave Macmillan, New YorkGoogle Scholar
  11. Boyd A, Bilegan I (2003) Revenue management and e-commerce. Manag Sci 49(10):1363–1386CrossRefGoogle Scholar
  12. Boyd EA, Kallesen R (2004) The science of revenue management when passengers purchase the lowest available fare. J Rev Pric Manag 3(2):171–177Google Scholar
  13. Boyd EA, Kambour E, Koushik D, Tama J (2001) The impact of buy down on sell up, unconstraining, and spiral down. Presented at the first annual INFORMS revenue management and pricing section conference. Columbia University, New YorkGoogle Scholar
  14. Brumelle SL, McGill JI (1993) Airline seat allocation with multiple nested fare classes. Oper Res 41(1):127–137CrossRefGoogle Scholar
  15. Brumelle SL McGill JI (1988) Airline seat allocation with multiple nested fare classes. Presented at the INFORMS annual conference. Also presented at a University of British Columbia seminar, Denver, 1987Google Scholar
  16. Brumelle S, Walczak D (1997) Dynamic allocation of airline seat inventory with batch arrivals. Air transport research group of the WCTR society proceedings. 3. Faculty of Commerce and Business Administration, University of British Columbia, VancouverGoogle Scholar
  17. Chatwin R (2002) On the equivalence of pricing and discount allocation problems in revenue management. Presented at INFORMS, San JoseGoogle Scholar
  18. Chen L, Homem-de-Mello T (2006) Re-solving stochastic programming models for airline revenue management. Working paper, Department of Industrial, Welding and Systems Engineering, The Ohio State University, ColumbusGoogle Scholar
  19. Clements, Michael P, David F. Hendry (1998) Forecasting economic time series. Cambridge University PressGoogle Scholar
  20. Cook TS (1998) Sabre soars. OR/MS Today 25(3):26–31Google Scholar
  21. Cooper WL (2002) Asymptotic behavior of an allocation policy for revenue management. Oper Res 50:968–987CrossRefGoogle Scholar
  22. Cooper WL, Homem-de-Mello T, Kleywegt AJ (2006a) Models of the spiral-down effect in revenue management. Oper Res 54(5):968–987CrossRefGoogle Scholar
  23. Cooper WL, Homem-de-Mello T, Kleywegt AJ (2006b) Pricing dynamics of competitors whose models ignore competition. Presented at the 6th annual INFORMS revenue management and pricing section conference. Columbia University, New YorkGoogle Scholar
  24. Cooper WL, Homem-de-Mello T. Some decomposition methods for revenue management. Transp Sci (to appear)Google Scholar
  25. Cross RG (1997) Revenue management: hard-core tactics for market domination. Broadway Books, New YorkGoogle Scholar
  26. Curry RE (1988) Optimum seat allocation with fare classes nested on segments and legs. Technical note 88-1. Aeronomics, AtlantaGoogle Scholar
  27. Curry RE (1990) Optimal airline seat allocation with fare classes nested by origin and destinations. Transp Sci 24:193–204CrossRefGoogle Scholar
  28. Davis TA (2006) Direct methods for sparse linear systems. Society for Industrial and Applied Mathematics, PhiladelphiaCrossRefGoogle Scholar
  29. de Boer SV, Freling R, Piersma N (2002) Mathematical programming for network revenue management revisited. Eur J Oper Res 137:72–92CrossRefGoogle Scholar
  30. de Farias DP, Van Roy B (2003) The linear programming approach to approximate dynamic programming. Oper Res 51(6):850–865CrossRefGoogle Scholar
  31. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B39:1–38Google Scholar
  32. Esterl M (2011) Dogfight erupts in plane ticket sales. Wall Str J, Bus. January 6, 2011Google Scholar
  33. Farias V, Van Roy B (2006) An approximate dynamic programming approach to network revenue management. Working paper, Electrical Engineering, Stanford University, CaliforniaGoogle Scholar
  34. Fellner Karolin, Kallesen R, Ruggiero A, Yuen B (2006) Improving revenue through fare rationalization and a new business process between revenue management and sales. J Rev Pric Manag 5(2):118–127Google Scholar
  35. Feng Y, Xiao B (2008) A risk-sensitive model for managing perishable products. Oper Res 56(5):1305–1311CrossRefGoogle Scholar
  36. Fiig T, Isler K, Hopperstad C, Cleaz-Savoyen R (2005) DAVN-MR, a unified theory of O&D optimization in a mixed network with restricted and unrestricted fare products. Presented at AGIFORS reservations and yield management Google Scholar
  37. Fiig T, Isler K, Hopperstad C, Olsen S (2010) Forecasting and optimization of fare families in network RM. Presented at AGIFORS revenue management and cargo Google Scholar
  38. Fuller WA (1996) Introduction to statistical time series, 2nd edn. Wiley, New YorkGoogle Scholar
  39. Gallego G, van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Manag Sci 40(8):999–1020CrossRefGoogle Scholar
  40. Gallego G, van Ryzin G (1997) A multiproduct dynamic pricing problem and its applications to network yield management. Oper Res 45:24–41CrossRefGoogle Scholar
  41. Gallego G, Lin L, Ratliff R (2007) Revenue management with customer choice models. Presented at AGIFORS, South KoreaGoogle Scholar
  42. Garrow L (2010) 50 years of our OR contributions. Presented at AGIFORS revenue management and cargo Google Scholar
  43. Gelman A, Carlin JB, Stern HS, Rubin DB (1995) Bayesian data analysis. Chapman and Hall, LondonGoogle Scholar
  44. Gijbels I, Pope A, Wand MP (1999) Understanding exponential smoothing via kernel regression. J R Stat Soc B 61(1):39–50CrossRefGoogle Scholar
  45. Higle JL (2007) Bid-price control with origin-destination demand: a stochastic programming approach. J Rev Pric Manag 5(4):291–304Google Scholar
  46. Holt CC (1957) Forecasting trends and seasonals by exponenetially weighted moving averages, O.N.R. Memorandum 52, Carnegie Institute of Technology, Pittsburgh, PAGoogle Scholar
  47. Isler K (2004) On the equivalence of airline revenue management and pricing problems. Operations research technical report, Swiss International Air Lines Ltd Google Scholar
  48. Isler K (2005) From seamless availability to seamless quote. Presented at PROS RM conference, HoustonGoogle Scholar
  49. Isler K, Imhof H (2007) Revenue management with fenceless fares and competition. J Rev Pric Manag (to appear)Google Scholar
  50. Iyengar G, Phillips R, Gallego G, Dubey A (2004) Managing flexible products on a network. CORC technical report TR-2004-01, Department of Industrial Engineering and Operations Research. Columbia University, New YorkGoogle Scholar
  51. Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 92:35–45CrossRefGoogle Scholar
  52. Kalman RE, Bucy RS (1961) New results in linear filtering and prediction theory. J Basic Eng 83:95–108CrossRefGoogle Scholar
  53. Karaesmen I, van Ryzin G (2004) Overbooking with substitutable inventory classes. Oper Res 52(1):83–104CrossRefGoogle Scholar
  54. Kincaid WM, Darling DA (1963) An inventory pricing problem. J Math Anal Appl 7:183–208CrossRefGoogle Scholar
  55. Klophaus R, Poelt S (2007) Airline overbooking with dynamic spoilage costs. J Rev Pric Manag 6(1):9–18Google Scholar
  56. L’Heureux E (1986) A new twist in forecasting short-term passenger pickup. AGIFORS Symposium Proc. 26. Bowness-On-Windermere, EnglandGoogle Scholar
  57. Lautenbacher CJ, Stidham SJ (1999) The underlying markov decision process in the single-leg airline yield management problem. Transp Sci 33:136–146CrossRefGoogle Scholar
  58. Lawrence R, Se June Hong, Cherrier J (2003) Passenger based predictive modeling of airline no-show rates. SIGKDD. Aug 24–27Google Scholar
  59. Lee TC, Hersh M (1993) A model for dynamic airline seat inventory control with multiple seat bookings. Transp Sci 27:252–265CrossRefGoogle Scholar
  60. Levin Y, McGill J, Nediak M (2008) Risk in revenue management and dynamic pricing. Oper Res 56(2):326–343CrossRefGoogle Scholar
  61. Lieberman W (2011) From yield management to price optimization: lessons learned. J Rev Pric Manag 10(1):40–43, 10th Anniversary EditionGoogle Scholar
  62. Littlewood K (1972) Forecasting, control of passenger bookings. reprinted in 2005. J Rev Pric Manag 4:111–123Google Scholar
  63. McGill JI, van Ryzin GJ (1999) Revenue management: research overview and prospects. Transp Sci 33:233–256CrossRefGoogle Scholar
  64. Meissner J, Strauss AK (2008) Network revenue management with inventory-sensitive bid prices and customer choice (Working paper). Department of Management Science, Lancaster University Management School, LancasterGoogle Scholar
  65. Oosten M, Walczak D (2005) Transforming pricing problems. Presented at the 5th annual INFORMS revenue management and pricing section conference. MIT, CambridgeGoogle Scholar
  66. Petersen JP, Fiig T (2005) SAS Experiences with a network optimization in a mixed environment of restricted and unrestricted fare structures (OPTIMIX). Presented at PROS RM conference Houston Google Scholar
  67. Poelt S (2011) The rise and fall of RM. J Rev Pric Manag 10(1):23–25, 10th Anniversary EditionGoogle Scholar
  68. PROS (2011) Lufthansa extends PROS portfolio with implementation of O&D network optimizer. Press release, PROS corporate communications, Houston, TXGoogle Scholar
  69. Queenan CC, Ferguson M, Higbie J, Kapoor R (2007) A comparison of unconstraining methods to improve revenue management systems. Prod Oper Manag 16(6):729–746CrossRefGoogle Scholar
  70. Rao CR (2001) A note on the Kalman filter. Proc Natl Acad Sci U S A 98(19):10557–10559CrossRefGoogle Scholar
  71. Ratliff R (2006) Multi-flight demand untruncation with recapture Presented at the 2006 AGIFORS revenue management and distribution study group conference, Cancun, MexicoGoogle Scholar
  72. Robinson LW (1995) Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Oper Res 43:252–263CrossRefGoogle Scholar
  73. Rothstein M (1985) OR and the airline overbooking problem. Oper Res 33:237–248CrossRefGoogle Scholar
  74. Rothstein M, Stone AW (1967) Passenger booking levels. Proceedings of the seventh AGIFORS symposium, 392–435Google Scholar
  75. Samu K (2010) WSJ opinion. Auctions for overbooking (a better idea than airline bumping). Wall St J, Rev Outlook, June 8, 2010Google Scholar
  76. Shaykevich A (1994) Airline yield management: dynamic programming approach. Master’s thesis, Department of Operations Research, University of North Carolina, Chapel HillGoogle Scholar
  77. Simon J (1968) An almost practical solution to the airline overbooking. J Trans Econ Policy 2:201–202Google Scholar
  78. Simon J (1972) Airline overbooking: the state of the art––the reply. J Trans Econ Policy 6:254–256Google Scholar
  79. Simpson RW (1989) Using network flow techniques to find shadow prices for market and seat inventory control technical report, MIT Flight Transportation Laboratory Memorandum M89-1 MIT, CambridgeGoogle Scholar
  80. Smith BC, Penn CW (1988) Analysis of alternative origin-destination control strategies. AGIFORS Annual symposium proceedingsGoogle Scholar
  81. Smith BC, Leimkuhler JF, Darrow RM (1992) Yield management at American airlines. Interfaces 22:8–31CrossRefGoogle Scholar
  82. Stone R, Diamond M (1992) Optimal inventory control for a single flight leg. Working paper, Northwest Airlines, Operations Research Division, MinneapolisGoogle Scholar
  83. Subramanian J, Lautenbacher CJ, Stidham SJ (1999) Yield management with overbooking, cancellations and no shows. Transp Sci 33:147–167CrossRefGoogle Scholar
  84. Talluri K (2003) on equilibria in duopolies with finite strategy spaces. Working paper. Universitat Pompeu Fabra, BarcelonaGoogle Scholar
  85. Talluri K, van Ryzin G (2004) Theory and practice of revenue management. Kluwer, DordrechtGoogle Scholar
  86. Tanner MA (1993) Tools for statistical inference: methods for the exploration of posterior distributions and likelihood functions. Springer, New YorkGoogle Scholar
  87. Ternoey C (1997) Multi-period state contingent yield management. Presented at INFORMS Dallas 1997, Decision Focus/AeronomicsGoogle Scholar
  88. van Ryzin G, Liu Q (2004) Decision, risk, and operations. Working papers series DRO-2004-04, Columbia University, New YorkGoogle Scholar
  89. van Ryzin G, McGill J (2000) Revenue management without forecasting or optimization: an adaptive algorithm for choosing airline seat protection levels. Manage Sci 46(6):760–775CrossRefGoogle Scholar
  90. Vinod B (1989) A set partitioning algorithm for virtual nesting indexing using dynamic programming internal technical report, SABRE decision technologies Google Scholar
  91. Vinod B (2006) Advances in inventory control. J Rev Pric Manag 4(4):367–381Google Scholar
  92. Vinod B (1995) Origin-and-destination yield management. In: Jenkins D (ed). The handbook of airline economics. McGraw-Hill, New York, pp 459–468Google Scholar
  93. Vulcano G, van Ryzin G, Ratliff R (2008) Estimating primary demand for substitutable products from sales transaction data. Working paper. Presented at the 8th INFORMS RM and pricing conference, MontrealGoogle Scholar
  94. Walczak D (2003) Dynamic programs for revenue management and dynamic pricing. Presented at annual conference of Canadian OR Society (CORS), VancouverGoogle Scholar
  95. Walczak D (2004) An efficient approximate solution to revenue management pricing networks. Presented at the 4th INFORMS RM and pricing conference, MIT, CambridgeGoogle Scholar
  96. Walczak D (2007) A pricing dynamic programming game. Presented at 2007 INFORMS RM and pricing conference. Barcelona, SpainGoogle Scholar
  97. Walczak D (2010) A note on balancing expected profit, resource utilization. J Rev Pric Manag (submitted). Presented at INFORMS RM and pricing conference, Cornell University, IthacaGoogle Scholar
  98. Walczak D, Brumelle S (2007) Semi-Markov information model for revenue management and dynamic pricing. OR Spectr (Springer) 29(1):61–83Google Scholar
  99. Walczak D, Oosten M (2008) Customer choice models and marginal revenue transformation in revenue management and pricing. (submitted)Google Scholar
  100. Walczak D, Mardan S, Kallesen R (2008) Customer choice, fare adjustments and the marginal expected revenue data transformation. Presented at 2008 INFORMS RM and pricing conference, Montreal, Canada. J Rev Pric Manag 9(1/2):94–109Google Scholar
  101. Weatherford LR (1991) Perishable asset revenue management in general business situations Doctoral thesis Darden Graduate School of Business Administration, University of Virginia, CharlottesvilleGoogle Scholar
  102. Wei YJ (1997) Airline O-D control using network displacement concepts. Masters thesis, MIT, CambridgeGoogle Scholar
  103. Welch G, Bishop G (2006) Introduction to the Kalman filter. UNC-Chapel Hill, TR 95-041Google Scholar
  104. West M, Harrison J (1997) Bayesian forecasting and dynamic models, 2nd edn. Springer, New YorkGoogle Scholar
  105. Williamson EL (1992) Airline network seat inventory control: methodologies and revenue impacts. Ph.D. thesis, MIT, CambridgeGoogle Scholar
  106. Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6:324–342Google Scholar
  107. Wollmer RD (1988) An airline seat management model for a single leg route when lower fare classes book first. Fall ORSA/TIMS Conference, DenverGoogle Scholar
  108. Wollmer RD (1992) An airline seat management model for a single leg route when lower fare classes book first. Oper Res 40:26–37CrossRefGoogle Scholar
  109. Young Y, van Slyke R (1994) Stochastic knapsack models of yield management. Technical Report 94–76. Polytechnic University, New YorkGoogle Scholar
  110. Zeni RH (2001) Improved forecast accuracy in airline revenue management by unconstraining demand estimates from censored data. Ph.D. thesis, Rutgers University, NewarkGoogle Scholar
  111. Zhang D, Adelman D (2007) An approximate dynamic programming approach to network revenue management with customer choice. Working paper. http://faculty.chicagogsp.edu/daniel.adelman/research/network.pdf
  112. Zhang D, Cooper WL (2005) Revenue management for parallel flights with customer-choice behavior. Oper Res 53(3):415–431CrossRefGoogle Scholar
  113. Zhao W (1999) Dynamic and static yield management models. Ph.D. thesis, The Wharton School, Operations and Information Management Department, University of Pennsylvania, PhiladelphiaGoogle Scholar
  114. Zhao W, Zheng YS (1998a) General dynamic models for airline seat allocation. Working paper, The Wharton School, University of Pennsylvania, PhiladelphiaGoogle Scholar
  115. Zhao W, Zheng YS (1998b) Optimal policy for multi-class seat allocation with nonstationary demand. Working paper, The Wharton School, University of Pennsylvania, PhiladelphiaGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC  2012

Authors and Affiliations

  1. 1.Senior Scientist and DirectorPROS Revenue ManagementHouston USA
  2. 2.Chief Scientist and Senior Vice PresidentPROS Revenue ManagementHoustonUSA

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