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
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Code available at http://beniz.github.io/libcmaes.
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http://www.statistica.provincia.tn.it, section “Annuari del Turismo".
References
Arnold, D.V.: Noisy Optimization with Evolution Strategies, vol. 8. Springer, Cham (2012). https://doi.org/10.1007/978-1-4615-1105-2
Arnold, D.V., Beyer, H.G.: A comparison of evolution strategies with other direct search methods in the presence of noise. Comput. Optim. Appl. 24(1), 135–159 (2003)
Aydin, N., Birbil, S.: Decomposition methods for dynamic room allocation in hotel revenue management. Eur. J. Oper. Res. 271(1), 179–192 (2018)
Aziz, H.A., Saleh, M., Rasmy, M.H., Elshishiny, H.: Dynamic room pricing model for hotel revenue management systems. Egypt. Inform. J. 12(3), 177–183 (2011)
Baker, T.K., Collier, D.A.: A comparative revenue analysis of hotel yield management heuristics. Decis. Sci. 30(1), 239–263 (1999)
Bayoumi, A.E.M., Saleh, M., Atiya, A.F., Aziz, H.A.: Dynamic pricing for hotel revenue management using price multipliers. J. Revenue Pricing Manag. 12(3), 271–285 (2013)
Bertsimas, D., De Boer, S.: Simulation-based booking limits for airline revenue management. Oper. Res. 53(1), 90–106 (2005)
Bertsimas, D., Popescu, I.: Revenue management in a dynamic network environment. Transp. Sci. 37(3), 257–277 (2003)
Bitran, G., Caldentey, R.: An overview of pricing models for revenue management. Manuf. Serv. Oper. Manag. 5(3), 203–229 (2003)
Bitran, G.R., Mondschein, S.V.: An application of yield management to the hotel industry considering multiple day stays. Oper. Res. 43(3), 427–443 (1995)
Choi, T.Y., Cho, V.: Towards a knowledge discovery framework for yield management in the Hong Kong hotel industry. Int. J. Hosp. Manag. 19(1), 17–31 (2000)
Denizci Guillet, B., Mohammed, I.: Revenue management research in hospitality and tourism: a critical review of current literature and suggestions for future research. Int. J. Contemp. Hosp. Manag. 27(4), 526–560 (2015)
Figueira, G., Almada-Lobo, B.: Hybrid simulation-optimization methods: a taxonomy and discussion. Simul. Model. Pract. Theory 46, 118–134 (2014)
Grube, P., Núñez, F., Cipriano, A.: An event-driven simulator for multi-line metro systems and its application to Santiago de Chile metropolitan rail network. Simul. Model. Pract. Theory 19(1), 393–405 (2011)
Guadix, J., Cortés, P., Onieva, L., Muñuzuri, J.: Technology revenue management system for customer groups in hotels. J. Bus. Res. 63(5), 519–527 (2010)
Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J.A., Larrañaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol. 192, pp. 75–102. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32494-1_4
Hansen, N.: Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2389–2396. ACM (2009)
Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9(2), 159–195 (2001)
Ivanov, S.: Hotel Revenue Management: From Theory To Practice. Zangador (2014)
Kleywegt, A.J.: An optimal control problem of dynamic pricing. School of Industrial and Systems Engineering, Georgia Institute of Technology 2001 (2001)
Lai, K.K., Ng, W.L.: A stochastic approach to hotel revenue optimization. Comput. Oper. Res. 32(5), 1059–1072 (2005)
Liu, S., Lai, K.K., Wang, S.: Booking models for hotel revenue management considering multiple-day stays. Int. J. Revenue Manag. 2, 78–91 (2008)
McGill, J.I., Van Ryzin, G.J.: Revenue management: research overview and prospects. Transp. Sci. 33(2), 233–256 (1999)
Nissen, V., Propach, J.: On the robustness of population-based versus point-based optimization in the presence of noise. IEEE Trans. Evol. Comput. 2(3), 107–119 (1998)
Rana, S., Whitley, L.D., Cogswell, R.: Searching in the presence of noise. In: Voigt, H.M., Ebeling, W., Rechenberg, I., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature - PPSN IV, vol. 1141, pp. 198–207. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61723-X_984
Stefanovic, D., Stefanovic, N., Radenkovic, B.: Supply network modelling and simulation methodology. Simul. Model. Pract. Theory 17(4), 743–766 (2009)
Subulan, K., Baykasoğlu, A., Eren Akyol, D., Yildiz, G.: Metaheuristic-based simulation optimization approach to network revenue management with an improved self-adjusting bid-price function. Eng. Econ. 62(1), 3–32 (2017)
Vives, A., Jacob, M., Payeras, M.: Revenue management and price optimization techniques in the hotel sector: a critical literature review. Tourism Econ. 24, 720–752 (2018)
Vock, S., Enz, S., Cleophas, C.: Genetic algorithms for calibrating airline revenue management simulations. In: 2014 Proceedings of the Winter Simulation Conference, pp. 264–275 (2014)
Welch, B.L.: The generalization of student’s problem when several different population variances are involved. Biometrika 34(1/2), 28–35 (1947)
Xiang, Z., Magnini, V.P., Fesenmaier, D.R.: Information technology and consumer behavior in travel and tourism: insights from travel planning using the Internet. J. Retail. Consum. Serv. 22, 244–249 (2015)
Yager, R.R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11(1), 49–73 (1996)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Trans. Evol. Comput. 9(3), 303–317 (2005)
Zakhary, A., Atiya, A.F., El-Shishiny, H., Gayar, N.E.: Forecasting hotel arrivals and occupancy using Monte Carlo simulation. J. Revenue Pricing Manag. 10(4), 344–366 (2011)
Zhang, D., Weatherford, L.: Dynamic pricing for network revenue management: a new approach and application in the hotel industry. INFORMS J. Comput. 29(1), 18–35 (2016)
Acknowledgements
A. Mariello would like to thank H. T. Nguyen for the useful advice on the RIM quantifiers.
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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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