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INTRODUCTION
Revenue management (RM) is the science of managing the available amount of supply to maximize revenue by dynamically controlling the price/quantity offered (Bitran and Caldentey, 2003; Ingold et al , 2003; Talluri and Van Ryzin, 2005). RM systems have been widely adopted in the hotel industry. Because of the large number of existing hotels, any possible improvement in the technology will amount to potentially very large overall savings. A key component of hotel room RM system is the forecasting of the daily hotel arrivals and occupancy. Inaccurate forecasts will significantly impact the performance of the RM system, because the forecast is the main driver of the pricing/room allocation decisions (see Lee (1990) and Weatherford and Kimes (2003) for discussions of this issue). In fact, Chiang et al (2007) mentions that for the airline industry it is estimated that a 20 per cent improvement in forecasting error translates into a 1 per cent increase in revenue generated from the RM system (Lee (1990) estimates an even larger impact). For either the airline industry or the hotel industry this will probably impact the net income in a much larger way, because of the small margins existing in these industries.
In this article, we consider the problem of forecasting daily hotel arrivals and hotel occupancy, with the forecast horizon being several months. In the theory of forecasting, there have been two competing philosophies. The first one is based on developing an empirical formula that relates the value to be forecasted with the recent history (for example, ARIMA-type or exponential smoothing models). The other approach focuses on developing a model from first principles that relates the value in question with the available variables/parameters and so on, and simulates that model forward to obtain the forecast. This approach has been prevalent in weather forecasting, where the partial differential equations (PDEs) relating the weather variables are simulated in a spatiotemporal way (see Sivillo et al , 1997). Because the majority of real-world systems are either intractable or very complex to model, most forecasting applications follow the first approach. In contrast, we follow here the second approach. In other words, the proposed model is based on simulating forward in time in a Monte Carlo fashion the actual hotel mechanisms. Rather...