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Forecasting hotel arrivals and occupancy using Monte Carlo simulation

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Journal of Revenue and Pricing Management Aims and scope

Abstract

Forecasting hotel arrivals and occupancy is an important component in hotel revenue management systems. In this article, we propose a new Monte Carlo simulation approach for the arrivals and occupancy forecasting problem. In this approach, we simulate the hotel reservations process forward in time, and these future Monte Carlo paths will yield forecast densities. A key step for the faithful emulation of the reservations process is the accurate estimation of its parameters. We propose an approach for the estimation of these parameters from the historical data. Then, the reservations process will be simulated forward with all its constituent processes such as reservation arrivals, cancellations, length of stay, no shows, group reservations, seasonality, trend and so on. We considered as a case study the problem of forecasting room demand for Plaza Hotel, Alexandria, Egypt. The proposed model gives superior results compared to existing approaches.

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Acknowledgements

We acknowledge the help of Hossam Shehata of Alexandria University. His help has been invaluable in giving us information and insights about the hotel business, and in interfacing with Plaza Hotel managers. We acknowledge the help of Professor Hanan Kattara of Alexandria University (and the owner of Plaza Hotel), for her generous help and willingness to supply all Plaza Hotel's data. We thank Emad Mourad, the manager of Plaza Hotel, for his assistance. We acknowledge the help of Robert Andrawis of Cairo University, who has developed the maximum-likelihood-based exponential smoothing code. We also acknowledge the useful discussions with Professor Ali Hadi of the American University of Cairo and Cornell University. This work is part of the Data Mining for Improving Tourism Industry Revenue in Egypt research project within the Data Mining and Computer Modeling Center of Excellence in Egypt. This work is supported by the Information Technology Industry Development Agency (ITIDA) in Egypt through the Centers of Excellence Program.

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Authors and Affiliations

  1. Department of Information Technology, Faculty of Computers and Information, Cairo University, Giza, Egypt

    Athanasius Zakhary & Neamat E Gayar

  2. Department of Computer Engineering, Cairo University, Berkeley, Giza, Egypt

    Amir F Atiya

  3. Advanced Technology and Center for Advanced Studies at IBM Technology Development Center, Cairo, Egypt

    Hisham El-Shishiny

Authors
  1. Athanasius Zakhary
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  2. Amir F Atiya
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  3. Hisham El-Shishiny
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  4. Neamat E Gayar
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Corresponding author

Correspondence to Amir F Atiya.

Additional information

1received his BS degree from the Department of Information Technology, the Faculty of Computers and Information, Cairo University, Egypt. Since then, he has been a researcher with the Data Mining and Computer Modeling Center of Excellence, Ministry of Information and Telecommunications (MCIT). In addition, he is affiliated with the Faculty of Computers and Information, Cairo University, where he is pursuing his Masters degree. His research interests are in the theory of forecasting, neural networks and machine learning.

2received his BS degree in from Cairo University, Egypt, and the MS and PhD degrees from Caltech, Pasadena, CA, all in electrical engineering. Dr Atiya is currently a professor at the Department of Computer Engineering, Cairo University. He recently held several visiting appointments, such as in Caltech and in Chonbuk National University, South Korea. His research interests are in the areas of neural networks, machine learning, theory of forecasting, pattern recognition, computational finance and Monte Carlo methods. He obtained several awards, such as the Kuwait Prize in 2005, and was an associate editor for the IEEE Transactions on Neural Networks from 1998 to 2008.

APPENDIX

APPENDIX

Table A1 shows the different seasonal periods for Plaza Hotel, as determined by the managers. Shown is the very low season period and the high season periods. Any other period is considered low season. We made use of these periods to determine the seasonal average.

Table a1 The dates of the different seasonal regimes for Plaza Hotel (the rest of the days are low season days)
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Zakhary, A., Atiya, A., El-Shishiny, H. et al. Forecasting hotel arrivals and occupancy using Monte Carlo simulation. J Revenue Pricing Manag 10, 344–366 (2011). https://doi.org/10.1057/rpm.2009.42

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  • DOI: https://doi.org/10.1057/rpm.2009.42

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