Hotelsimu: simulation-based optimization for hotel dynamic pricing

Abstract

Exact and approximated mathematical optimization methods have already been used to solve hotel revenue management (RM) problems. However, to obtain solutions which can be solved in acceptable CPU times, these methods require simplified models. Approximated solutions can be obtained by using simulation-based optimization, but existing approaches create empirical demand curves which cannot be easily modified if the current market situation deviates from the past one. We introduce HotelSimu, a flexible simulation-based optimization approach for hotel RM, whose parametric demand models can be used to inject new information into the simulator and adapt pricing policies to mutated market conditions. Also, cancellations and reservations are interleaved, and seasonal averages can be set on a daily basis. Monte Carlo simulations are employed with black-box optimization to maximize revenue, and the applicability of our models is evaluated in a case study on a set of hotels in Trento, Italy.