Hotel revenue management: Benefits of simultaneous overbooking and allocation problem formulation in price optimization

Abstract

We develop a hotel revenue management optimization method in an environment where market segment prices are optimized via demand curves ahead of a planning horizon. This new method simultaneously optimizes overbooking levels and allocation (of capacity to market segments) levels, as opposed to the traditional sequential approach. We test our method against the reference in a simulation of a hotel reservation system that has all the functionality of a real-world revenue management system: the estimation of true demand from censored demand; different market segments with different demand patterns; price elasticities; varying propensities to stay certain lengths of time; short- and long-term forecasting with periodic reoptimization of all forecaster parameters; explicit optimization of market segment prices based on estimated demand curves; and optimization routines for overbooking and allocation. A walkthrough of this simulation was performed by the revenue management staff at a major hotel. This simulation has been scaled down to permit extensive experimentation. Our new method outperforms the reference method by an average of 20.2% with respect to nightly net revenue. The improvement is much larger in situations where demand is more saturated. Our new method takes less than two minutes of computing time from a cold start on a realistically sized problem, which is sufficiently fast for hotel managers who want the capability of rerunning the algorithm many times during the course of a day.