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.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.ABSTRACT KEYWORDS: revenue management, stochastic simulation, simulation setup, demand modellingSimulations 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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