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.

在本文中，我们提出了一种解决酒店企业动态定价问题的新方法。它包括将需求分解为几个类别、预测、弹性需求模拟以及具有凹二次目标函数和用于动态价格优化的线性约束的数学规划模型。该方法计算效率高且易于实现。在使用酒店数据集进行的计算机实验中，在假设需求可能偏离的情况下，采用固定价格政策的情况下，酒店收入与过去一段时间的实际收入相比平均增加了约6%建议的弹性模型。该方法和开发的软件可以成为小型酒店从 COVID-19 大流行的经济后果中恢复过来的有用工具。