Revenue optimization for a hotel property with different market segments : demand prediction, price selection and capacity allocation

Abstract

We present our work with a hotel company as an example of how machine learning techniques can be used to improve the demand predictions of a hotel property, as well as its pricing and capacity allocation decisions. First, we build a price-sensitive random forest model to predict the number of daily bookings for each customer market segment. We feed these predictions into a mixed integer linear program (MILP) to optimize prices and capacity allocations at the same time. We prove that the MILP can be equivalently solved as a linear program, and then show that it produces upper and lower bounds for the expected revenue maximization Dynamic Program (DP), and that the gap between the bounds depends on the probabilistic distribution of the demand. Thus, for high prediction accuracies, the optimal value of the DP can be closely approximated by the MILP solution. Finally, numerical results show that the optimized decisions are able to generate an increase in revenue compared to the historical policies, and that the fast running time achieved permits real time policy updates.