Hotel Intelligence: Maximizing Revenue with Nonlinear Dynamic Pricing and Predictive Demand Analysis
摘要
Dynamic pricing is fundamental for revenue maximization in the hotel industry, particularly given demand volatility. Machine Learning (ML) models offer a promising avenue for learning optimal pricing policies without necessitating direct market experimentation. However, evaluating the effectiveness of these models presents challenges, especially due to the scarcity of robust theoretical benchmarks. This study introduces a mathematical simulator employing constrained nonlinear programming to identify optimal daily rates across various pricing scenarios. The simulator is designed to serve as a reference for assessing the performance of ML-based pricing strategies. Simulations conducted demonstrate the model’s stability and its applicability for generating benchmark solutions in diverse operational contexts.