Abstract
The computation of bid-prices for resources is the most popular instrument for capacity control in network revenue management. The basic task of this control includes supporting accept/reject decisions on dynamically arriving requests for products that differ in their revenues and resource demands, respectively. Within actual control, bid-prices can be used to approximate the opportunity cost of reserving resources to satisfy a request. Using this valuation, the request is accepted if the associated revenue equals or exceeds the opportunity cost. Most commonly, bid prices are computed by linear programming based on the forecasted demand with a few updates during the booking period. Due to accepted requests and variations between forecasted and real demand, the approximation of the opportunity cost becomes less accurate with time passing by, leading to inferior accept/reject decisions. Therefore, we propose the concept of self-adjusting bid-prices. The basic idea includes defining bid-prices as functions of the amount of capacity already used and of the expected demand-to-come. Coefficients for calibrating the bid-price functions are obtained by a simulation-based optimization using the metaheuristic scatter search.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bartodziej P, Derigs U (2004) On an experimental algorithm for revenue management for cargo airlines. In: Ribeiro CC, Martins SL (eds) Experimental and efficient algorithms. Springer, Berlin Heidelberg New York, pp 57–71
Bertsimas D, de Boer S (2005) Simulation-based booking limits for airline revenue management. Oper Res 53:90–106
Bertsimas D, Popescu I (2003) Revenue management in a dynamic network environment. Transp Sci 37:257–277
De Boer S, Freling R, Piersma N (2002) Mathematical programming for network revenue management revisited. Eur J Oper Res 137:72–92
Fu MC (2002) Optimization for simulation: Theory vs. practice. INFORMS J Comput 14:192–215
Gallego G, van Ryzin GJ (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Manage Sci 40:999–1020
Glover F (1998) Scatter search and path relinking. In: Corne D, Dorigo M, Glover F (eds) New methods in optimization. McGraw-Hill, London, pp 297–316
Goldman PR, Freling R, Pak K, Piersma N (2002) Models and techniques for hotel revenue management using a rolling horizon. Journal of Revenue and Pricing Management 1:207–219
Gosavi A (2003) Simulation-based optimization: Optimization techniques and reinforcement learning. Kluwer, Boston
Gosavi A, Ozkaya E, Kahraman AF (2006) Simulation optimization for revenue management of airlines with cancellations and overbooking. OR Spectrum 29 (in press). DOI 10.1007/s00291-005-0018-z
Kalyan V (2002) Dynamic customer value management: Asset values under demand uncertainty using airline yield management and related techniques. Information System Frontiers 4:101–119
Kimms A, Klein R (2005) Revenue Management im Branchenvergleich. Zeitschrift für Betriebswirtschaft (special issue I). Revenue Management 1–30
Kimms A, Müller-Bungart M (2006) Simulation of stochastic demand data streams for network revenue management problems. OR Spectrum 28 (in press). DOI 10.1007/s00291-005-0020-5
Klein R (2005) Revenue management—Grundlagen und Methoden der Kapazitätssteuerung. Habilitation Thesis, Darmstadt University of Technology
Laguna M, Martí R (2003) Scatter search—Methodology and implementations in C. Kluwer, Boston
Law AM, Kelton WD (2000) Simulation modeling and analysis, 3rd edn. McGraw-Hill, Boston
Pak K, Dekker R (2004) Cargo revenue management: Bid-prices for a 0–1 multi knapsack problem. Erim report series research in management 55/2004, Erasmus University, Rotterdam
Philips RL (2005) Pricing and revenue management. Stanford University Press, Stanford
Rinnooy Kan A, Stougie L, Vercellis C (1993) A class of generalized greedy algorithms for the multi-knapsack problem. Discrete Appl Math 42:279–290
Simpson RW (1989) Using network flow techniques to find shadow prices for market and seat inventory control. MIT flight transportation laboratory memorandum M89-1, Cambridge, Massachusetts
Smith BC, Penn CW (1988) Analysis of alternative origin–destination control strategies. Proceedings of the twenty eighth annual AGIFORS symposium, New Seabury, Massachusetts
Spall JC (2003) Introduction to stochastic search and optimization: Estimation, simulation, and control. Wiley, New York
Spengler T, Rehkopf S, Volling T (2006) Revenue management in make-to-order manufacturing—An application to the iron and steel industry. OR Spectrum 29 (in press). DOI 10.1007/s00291-005-0024-1
Talluri KT, van Ryzin GJ (1998) An analysis of bid-price controls for network revenue management. Manage Sci 44:1577–1593
Talluri KT, van Ryzin GJ (1999) A randomized linear programming method for computing network bid prices. Transp Sci 33:207–216
Talluri KT, van Ryzin GJ (2004) The theory and practice of revenue management. Kluwer, Boston
van Ryzin GJ, Vulcano G (2004) Simulation-based optimization of virtual nesting controls for network revenue management. Working paper, Graduate School of Business, Columbia University, New York
Williamson EL (1992) Airline network seat control. Ph.D. thesis, MIT, Cambridge, Massachusetts
Wollmer RD (1986) A hub-and-spoke seat management model. Technical report, McDonnell Douglas Corporation, Long Beach
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Klein, R. Network capacity control using self-adjusting bid-prices. OR Spectrum 29, 39–60 (2007). https://doi.org/10.1007/s00291-006-0043-6
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00291-006-0043-6