Dynamic pricing with demand disaggregation for hotel revenue management

• Published in: Journal of heuristics Vol. 27; no. 5; pp. 869 - 885
• Main Authors: Bandalouski, Andrei M, Egorova, Natalja G, Kovalyov, Mikhail Y, Pesch, Erwin, Tarim, S. Armagan
• Format: Journal Article
• Language: English
• Published: New York, NY Springer US 01.10.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Calculus of Variations and Optimal Control; Optimization; Concave programming; COVID-19; Demand elasticity; Dynamic pricing; Economic forecasting; Hotel revenue management; Hotels; Hotels & motels; Management Science; Mathematical programming; Mathematics; Mathematics and Statistics; Operations Research; Operations Research/Decision Theory; Optimization; Pricing; Revenue; COVID-19; Dynamic pricing; Concave programming; Hotel revenue management; Demand elasticity
• ISSN: 1572-9397; 1381-1231; 1572-9397
• DOI: 10.1007/s10732-021-09480-2

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.