Dynamic pricing with demand disaggregation for hotel revenue management

In this paper we present a novel approach to the dynamic pricing problem for hotel businesses. It includes disaggregation of the demand into several categories, forecasting, elastic demand simulation, and a mathematical programming model with concave quadratic objective function and linear constraints for dynamic price optimization. The approach is computationally efficient and easy to implement. In computer experiments with a hotel data set, the hotel revenue is increased by about 6% on average in comparison with the actual revenue gained in a past period, where the fixed price policy was employed, subject to an assumption that the demand can deviate from the suggested elastic model. The approach and the developed software can be a useful tool for small hotels recovering from the economic consequences of the COVID-19 pandemic.

Automated summary

This paper introduces a novel approach to the dynamic pricing problem for hotel businesses, which includes disaggregation of demand, forecasting, elastic demand simulation, and a mathematical programming model. Experiment results show a 6% increase in hotel revenue compared to past performance with fixed pricing policy, proving its potential as a useful tool for small hotels impacted by the COVID-19 pandemic.