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

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 lear...

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Published in:International journal of hospitality management Vol. 133; p. 104472
Main Authors: C-Sánchez, Eleazar, Gomez, Juan F.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.02.2026
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ISSN:0278-4319
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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. •Hotel profit maximization under dynamic market conditions.•Dynamic pricing approach combining heuristics and machine learning: a sim-learnheuristic model.•Hotel pricing system capable of handling dynamic market conditions in near real-time.•Adaptive hotel demand simulator based on real hotel data.
AbstractList 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. •Hotel profit maximization under dynamic market conditions.•Dynamic pricing approach combining heuristics and machine learning: a sim-learnheuristic model.•Hotel pricing system capable of handling dynamic market conditions in near real-time.•Adaptive hotel demand simulator based on real hotel data.
ArticleNumber 104472
Author Gomez, Juan F.
C-Sánchez, Eleazar
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  surname: Gomez
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  email: jfgomgon@upv.edu.es
  organization: Research Center on Production Management and Engineering, Universitat, Politècnica de València, Alcoy 03801, Spain
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Keywords Dynamic pricing
Hotel pricing system
Stochastic combinatorial optimization problems
Simlearnheuristics
Machine learning
Language English
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Snippet 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...
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SubjectTerms Dynamic pricing
Hotel pricing system
Machine learning
Simlearnheuristics
Stochastic combinatorial optimization problems
Title Enhancing hotel profitability: Dynamic pricing with a Sim-Learnheuristic approach
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