In this article, the authors demonstrate how to use optimization combined with Monte Carlo simulation to model an investment decision by means of a case study of a hotel considering the converting a portion of its inventory to allergy-friendly rooms (by applying a proprietary method). Using survey data on consumer demand and hotel occupancy data, the model considers the random nature of occupancy to determine the optimal number of rooms to convert and the corresponding price to charge. In setting up the model, the authors demonstrate the limitations of traditional approaches using average occupancy data.
Actually, the interview survey asked respondents if they were willing to pay in excess of a $20 premium. We artificially set this at $25, the next $5 increment.
2.
The revenues are incurred daily and therefore are discounted over 730 days (two years). The maintenance cost are incurred starting at the end of the each quarter and therefore are discounted quarterly for seven quarters. At the end of the eighth quarter, the hotel general manager has to make the decision to renew the treatment for the rooms at a cost of $750 and therefore maintenance cost for the end of the eighth quarter is subsumed into this renewal investment.
3.
100 rooms × 70 percent average occupancy × 0.38 probability demanded (at $10.00/room-night).
4.
An anonymous reviewer pointed out that the extent of the difference is a function of the variability in demand. That is, a property with steady occupancy rates will obtain similar results for both models, whereas a property whose demand is fluctuating (but not necessarily closed) will obtain significantly different results, as demonstrated above. The difference in models arises because of the committed, fixed nature of costs.
5.
Reclassifying occupancy data into twenty categories was an arbitrary choice for expositional purposes. Using too few categories in the model has the potential to distort the optimization, but one can maintain the original distribution (the fineness being a function of the number of rooms). With our data, this reclassification did not change our results.
6.
Bouich, Lauren. 2005. Guests can breathe easier at Conrad Miami. Hotel Business14 (21): 60-60.
