عنوان مقاله [English]
Decision making regarding the hotel optimal capacity is one of the most important strategic decisions for the hotel industry executives and investors. This importance arises from the fact that after determining hotel capacity and execution of construction operations, it is not possible to change the capacity of hotel, or the changes will involve much higher costs.
Considerable capacity of hotels and residential centers that are located in a tourist town is empty of passengers and unused in relatively many periods of the year. However, in some limited time periods, number of travelers and tourists is increased due to holidays or various occasions and hotels are encountered with lack of capacity for the accommodation of travelers. In this article, to determine the optimal capacity of the hotel, using a novel approach, an attempt is made to present a mathematical optimization model based upon the queueing theory. To achieve this goal, first the reception system is simulated using the queueing models. Then, the capacity and optimal room numbers of various types using bounded multi-dimensional knapsack model are determined. The objective function of the proposed knapsack model is cost minimization. This cost function is developed by taking into account the time value of money and the sum of two different costs associated with the hotel construction. Due to the uncertainty of some parameters of the problem, the objective function of the model is presented as an objective function with fuzzy coefficients. To solve this model, a single-objective function is converted into three objective functions using the techniques of Lai and Hwang.
Then using fuzzy technique of Torabi and Hassini these three objective function problem was converted into a single objective deterministic model. This single objective programming problem was coded in MATLAB to determine the optimal capacity of hotel. The results confirm that the proposed model, unlike other approaches, can be easily and efficiently matched with different situations.