Enhancing hotel profitability: dynamic pricing with a Sim-Learnheuristic approach

Abstract

In recent decades, online travel agencies have emerged as the leading channel for hotel bookings, promoting dynamic pricing and real-time data analysis to stay competitive. Accordingly, we propose a dynamic pricing system based on a sim-learnheuristic model, which combines heuristic and machine learning to provide the most appropriate daily room prices. Additionally, deterministic and sim-heuristic models were implemented to benchmark our proposal. The models were tested using simulations based on real data. The results indicate that the sim-learnheuristic learns from past data and react to future outcomes, thus supporting effective hotel price management under dynamic conditions.