Practical Decision Rules for Risk-Averse Revenue Management Using Simulation-Based Optimization
Journal of Revenue and Pricing Management, Volume 15, pp. 468-487, doi:10.1057/s41272-016-0065-x
28 Pages Posted: 8 Mar 2016 Last revised: 8 Feb 2017
Date Written: February 5, 2016
In practice, human-decision makers often feel uncomfortable with the risk-neutral revenue management systems’ output. Reasons include a low number of repetitions of similar events, a critical impact of the achieved revenue for economic survival, or simply business constraints imposed by management. However, solving capacity control problems is a challenging task for many risk measures and the approaches are often not compatible with existing software systems.
In this paper, we propose a flexible framework for risk-averse capacity control under customer choice behavior. Existing risk-neutral decision rules are augmented by the integration of adjustable parameters. Our key idea is the application of simulation-based optimization (SBO) to calibrate these parameters. This allows to easily tailor the resulting capacity control mechanism to almost every risk measure and customer choice behavior.
In an extensive simulation study, we analyze the impact of our approach on expected utility, conditional value-at-risk (CVaR), and expected value. The results show a superior performance in comparison to risk-neutral approaches from literature.
Keywords: Revenue Management, Capacity Control, Risk-Aversion, Conditional Value-at-Risk
JEL Classification: L93, L91, M10
Suggested Citation: Suggested Citation