Abstract
Airlines typically divide a pool of identical seats into several booking classes that represent e.g. different discount levels with differentiated sale conditions and restrictions. Assuming perfect market segmentation, mixing discount and higher-fare passengers in the same aircraft compartment offers the airline the potential of gaining revenue from seats that would otherwise fly empty. If too many seats are sold at a discount price, however, the airline company would loose full-fare passengers. If too many seats are protected for higher-fare demand, the flight would depart with vacant seats. Seat inventory control deals with the optimal allocation of capacity to these different classes of demand, forming a substantial part of a revenue management system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aviv Y, Pazgal A (2005) A partially observed Markov decision process for dynamic pricing. Management Science 49(9):1400–1416
Belobaba PP (1987) Airline yield management. Transportation Science 21(2):63–73
Belobaba PP (1989) Application of a probabilistic decision model to airline seat inventory control. Operations Research 37(2):183–197
Brumelle SL, McGill JI (1993) Airline seat allocation with multiple nested fare classes. Operations Research 41(1):127–137
Brumelle SL, McGill JI, Oum TH, Sawaki K, Thretheway MW (1990) Allocation of airline seat between stochastically dependent demands. Transportation Science 24(3):183–192
Brumelle SL, Walczak D (2003) Dynamic airline revenue management with multiple semi-Markov demand. Operations Research 51(1):137–148
Curry RE (1990) Optimal airline seat allocation with fare classes nested by origins and destinations. Transportation Science 24(3):193–204
Helm WE, Waldmann KH (1984) Optimal control of arrivals to multi-server queues in a random environment. Journal of Applied Probability 21(3):602–615
Hinderer K, Waldmann KH (2005) Algorithms for countable state Markov decision models with an absorbing set. SIAM J. Control and Optimization 43(6):2109–2131
Kleywegt AJ, Papastavrou JD (1998) The dynamic and stochastic Knapsack problem. Operations Research 46(1):17–35
Lautenbacher CJ, Stidham S Jr (1999) The underlying Markov decision process in the single-leg airline yield management problem. Transportation Science 33(2):136–146
Lee TC, Hersh M (1993) A model for dynamic airline seat inventory control with multiple seat bookings. Transportation Science 27(3):252–265
Liang Y (1999) Solution to the continuous time dynamic yield management model. Transportation Science 33(1):117–123
Littlewood K (1972) Forecasting and control of passengers. 12th AG-IFORS Symposium Proceedings, pp. 95–117
Robinson LW (1995) Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Operations Research 43(2):252–263
Schäl M (1975) Conditions for optimality in dynamic programming and for the limit of n-stage optimal policies to be optimal. Z. Wahrscheinlichkeitstheorie verw. Gebiete 32:179–196
Shaked M, Shanthikumar JG (1988) Stochastic convexity and its applications. Advances in applied probabilities 20(2):427–446
van Slyke R, Young Y (2000) Finite horizon stochastic Knapsacks with applications to yield management. Operations Research 48(1):155–172
Stidham S Jr (1978) Socially and individually optimal control of arrivals to a GI/GI/1 queue. Management Science 24(15):1598–1610
Subramanian J, Stidham S Jr, Lautenbacher CJ (1999) Airline yield management with overbooking, cancellations, and no-shows. Transportation Science 33(2):147–167
Talluri KT, van Ryzin GJ (2004) The Theory and Practice of Revenue Management. Kluwer Academic Publishers, Dordrecht
Walczak D (2001) Dynamic modelling approaches to airline revenue management. Ph.D. thesis at the Centre for Transportation Studies, University of British Columbia
Wollmer RD (1992) An airline management model for a single leg route when lower fare classes book first. Operations Research 40(1):26–37
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2006 Deutscher Universitäts-Verlag/GWV Fachverlage GmbH, Wiesbaden
About this chapter
Cite this chapter
Barz, C., Waldmann, KH. (2006). An Application of Markov Decision Processes to the Seat Inventory Control Problem. In: Morlock, M., Schwindt, C., Trautmann, N., Zimmermann, J. (eds) Perspectives on Operations Research. DUV. https://doi.org/10.1007/978-3-8350-9064-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-8350-9064-4_7
Publisher Name: DUV
Print ISBN: 978-3-8350-0234-0
Online ISBN: 978-3-8350-9064-4
eBook Packages: Business and EconomicsBusiness and Management (R0)