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Abstract
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.
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1 National Academy of Sciences of Belarus, United Institute of Informatics Problems, Minsk, Belarus (GRID:grid.410300.6) (ISNI:0000 0001 2271 2138)
2 University of Siegen, Institute of Information Systems, Faculty III, Siegen, Germany (GRID:grid.5836.8) (ISNI:0000 0001 2242 8751); HHL Leipzig, Center for Advanced Studies in Management, Leipzig, Germany (GRID:grid.461621.6) (ISNI:0000 0001 0728 9327)
3 University College Cork, Cork University Business School, Cork, Ireland (GRID:grid.7872.a) (ISNI:0000000123318773)