Simulations are commonly applied in Revenue Management (RM) to give proof of new concepts. Most publications in this area focus on the methodical part and disregard a detailed description of the simulation in use. The underlying assumptions and simplifications, however, have a strong impact on the results. Although the make-up of a simulation environment always depends on the investigation undertaken, some general rules for simulations in RM can be established. In this paper, we provide a guideline for developing a stochastic simulation model to be used for analyses in RM. Many occurring questions cannot be universally answered. Therefore, we point out difficult decisions and give advices concerning setup, input data and calibration as well as the modelling of demand, forecast and optimisation. Finally, we present a specific simulation study as an example.
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Carrier, E. (2003) ‘Modeling Airline Passenger Choice: Passenger Preference for Schedule in the Passenger Origin-Destination Simulator (PODS)’, Master's thesis, Massachusetts Institute of Technology.
Eguchi, T. and Belobaba, P. P. (2004) ‘Modelling and simulation of impact of revenue management on Japan's domestic market’, Journal of Revenue and Pricing Management, 3, 2, 119–142.
Frank, M., Friedemann, M., Mederer, M. and Schröder, A. (2006) ‘Airline revenue management: a simulation of dynamic capacity management’, Journal of Revenue and Pricing Management, 5, 1, 62–71.
Hamilton, J. D. (1994) Time Series Analysis, Princeton University Press, Princeton, NJ.
Hane, C. A., Barnhart, C., Johnson, E. L., Marsten, R. E., Nemhauser, G. L. and Sigismondi, G. (1995) ‘The fleet assignment problem: solving a large-scale integer program’, Mathematical Programming, 70, 211–232.
Law, A. M. and Kelton, W. D. (2000) Simulation Modeling and Analysis, 3rd edn, McGraw-Hill, New York.
Littman, M. L., Dean, T. L. and Kaelbling, L. P. (1995) ‘On the Complexity of Solving Markov Decision Problems, Technical Report, Brown University, Department of Computer Science.
Solver-Benchmark. (2007): http://plato.asu.edu/bench.html.
Talluri, K. T. and van Ryzin, G. J. (2004) The Theory and Practice of Revenue Management, Kluwer Academic Publishers, Dordrecht, MA.
Zeni, R. H. (2001) ‘Improved Forecast Accuracy in Revenue Management by Unconstraining Demand Estimates from Censored Data’, PhD thesis, The State University of New Jersey.
1 Michael Frank is senior project manager of revenue management development at Lufthansa German Airline (LH). Dr Frank's research interests include queuing theory, production planning and stochastic simulations.
2 Martin Friedemann develops validation methods for hybrid forecasting as part of his work as project manager in the IT department at LH. He is also in the course of finishing his doctoral thesis, which will include an investigation of dynamic capacity allocation in revenue management.
3 Anika Schröder is currently concentrating on the conclusion of her dissertation. She is sponsored by the research cooperation of LH and Clausthal University of Technology. Her research pertains to dynamic pricing and customer choice, including stochastic simulations for comparing different demand models.
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Frank, M., Friedemann, M. & Schröder, A. Principles for simulations in revenue management. J Revenue Pricing Manag 7, 7–16 (2008). https://doi.org/10.1057/palgrave.rpm.5160107
- revenue management
- stochastic simulation
- simulation setup
- demand modelling