O&D revenue management in cargo airlines—a mathematical programming approach
Regular Article
First Online:
- 392 Downloads
- 15 Citations
Abstract
In this paper we present a mathematical programming based approach for revenue management in cargo airlines. The approach is based on a modified version of a multicommodity network flow model which has been developed in a decision support approach for schedule planning in cargo airlines. We think that using the same concept for planning and revenue management is essential for consistency of planning and operation. To meet the real-time requirements of revenue management special computational strategies for solving the large models are necessary.
Keywords
Revenue management Mathematical programming Multi-commodity flow Column generation Simulation studyNotes
Acknowledgements
We want to thank two anonymous referees for their valuable comments on an earlier version of this paper.
References
- Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice Hall, Englewood CliffsGoogle Scholar
- Antes J, Campen L, Derigs U, Titze C, Wolle G-D (1998) SYNOPSE: a model-based decision support system for the evaluation of flight schedules for cargo airlines. Decis Support Syst 22:307–323CrossRefGoogle Scholar
- Bartodziej P, Derigs U (2004) On an experimental algorithm for revenue management for cargo airlines. In: Lecture notes in computer science 3059. Springer, Berlin Heidelberg New York, pp 57–71Google Scholar
- Berge ME, Hopperstad CA (1993) Demand driven dispatch: a method for dynamic aircraft capacity assignment, models and algorithms. Oper Res 41(1):153–168Google Scholar
- Billings JS, Diener AG, Yuen BB (2003) Cargo revenue optimization. J Revenue Pricing Manage 2:69–79Google Scholar
- Cross RG (1997) Revenue management: hard core tactics for market domination. Broadway Books, New YorkGoogle Scholar
- Derigs U, Zils M (2001) Strategisches Controlling: Strategic Alliance Portfolio Analysis (SAP) - ein modellbasierter Ansatz zur Strategie- und Partnerselektion bei Strategischen Allianzen. In: ZfB-Ergänzungsheft 2/2001, pp 137–159Google Scholar
- Etschmaier MM, Mathaisel DF (1985) Arline scheduling: an overview. Transp Sci 19(2):127–138Google Scholar
- Elmasri R, Navathe SB (2004) Fundamentals of database systems, 4 edn. Pearson Education, BostonGoogle Scholar
- Gershkoff I (1998) An approach for just-in-time airline scheduling, chapter 6. In: Yu G (ed) Operations research in the airline industry, Kluwer, Dordrecht, pp 158–188Google Scholar
- Kasilingam RG (1996) Air cargo revenue management: characteristics and complexities. Eur J Oper Res 96:36–44CrossRefGoogle Scholar
- Kleywegt AJ, Papastavrou JD (1998) The dynamic and stochastic knapsack problem. Oper Res 46:17–35Google Scholar
- Kleywegt AJ, Papastavrou JD (2001) The dynamic and stochastic knapsack problem with random sized items. Oper Res 49:26–41CrossRefGoogle Scholar
- McGill JI, van Ryzin GJ (1999) Revenue management: research overview and prospects. Transp Sci 33(2):233–256CrossRefGoogle Scholar
- Pak K, Dekker R (2004) Cargo revenue management: Bid-prices for a 0-1 multi knapsack problem, ERIM Report Series Research in Management 55, Erasmus University RotterdamGoogle Scholar
- Pompeo L, Sapountzis T (2002) Freight expectations. McKinsey Q 2:90–99Google Scholar
- Slager B, Kapteijns L (2004) Implementation of cargo revenue management at KLM. J Revenue Pricing Manage 3:80–90Google Scholar
- Talluri KT, van Ryzin GJ (2004) The theory and practice of revenue management. Springer, Berlin Heidelberg New YorkGoogle Scholar
- Zils M (1998) C.A.R.M.A.—Cargo Airline Relative Market Share Analyst: An O&D-based market model for flight network design in the Air Cargo industry, Working Paper, WINFORS, University of Cologne, GermanyGoogle Scholar
- Zils M (1999) AirCargo Scheduling Problem Benchmark Instanzen, Working Paper, WINFORS, University of Cologne, GermanyGoogle Scholar
Copyright information
© Springer 2006