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HotelSimu: Simulation-Based Optimization for Hotel Dynamic Pricing

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Learning and Intelligent Optimization (LION 2020)

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Abstract

Exact and approximated mathematical optimization methods have already been used to solve hotel revenue management (RM) problems. However, to obtain solutions which can be solved in acceptable CPU times, these methods require simplified models. Approximated solutions can be obtained by using simulation-based optimization, but existing approaches create empirical demand curves which cannot be easily modified if the current market situation deviates from the past one. We introduce HotelSimu, a flexible simulation-based optimization approach for hotel RM, whose parametric demand models can be used to inject new information into the simulator and adapt pricing policies to mutated market conditions. Also, cancellations and reservations are interleaved, and seasonal averages can be set on a daily basis. Monte Carlo simulations are employed with black-box optimization to maximize revenue, and the applicability of our models is evaluated in a case study on a set of hotels in Trento, Italy.

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Notes

  1. 1.

    Code available at http://beniz.github.io/libcmaes.

  2. 2.

    http://dati.istat.it/?lang=en; section: Communication,culture,trips/Trips/Trips and their characteristics.

  3. 3.

    http://dati.trentino.it/dataset/esercizi-alberghieri.

  4. 4.

    http://www.statistica.provincia.tn.it, section “Annuari del Turismo".

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Acknowledgements

A. Mariello would like to thank H. T. Nguyen for the useful advice on the RIM quantifiers.

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Correspondence to Manuel Dalcastagné .

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Mariello, A., Dalcastagné, M., Brunato, M. (2020). HotelSimu: Simulation-Based Optimization for Hotel Dynamic Pricing. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_31

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