Demand forecasting in revenue management systems

A revenue management system is defined as the art of developing mathematical models that are capable of determining which product should be offered to which customer segment at a given time in order to maximize revenue. Demand forecasting plays a crucial role in revenue management. The lack of precision in demand models results in the loss of revenue. In this thesis, we provide an in-depth and systematic study of different methods that are applied to demand forecasting. We first introduce a new classification scheme for them and propose the characteristics that differentiate the methods from one another. All existing papers are reviewed and many of them have been categorized based on our classifciation scheme. After, we investigated a demand prediction model that uses a modified neural network method and historical data to forecast the number of passengers at the departure time for a major European railway company. Afterwards, in order to capture seasonal effects and taking customer behavior into account, we proposed a new, non-parametric mathematical model. The original problem is a nonconvex nonlinear model with integer variables. The variables in this model are the product utilities, the daily demand flow and binary assignment variables. We successfully linearized and convexified the model by using linearization techniques. Then, we used the characteristics of product availabilities for a given time to extract logical relations between choice probabilities. Moreover, we have classified each day to one of the predefined numbers of clusters based on their related daily demand flow. We represent a branch and bound algorithm, which uses global optimization techniques to find the estimated utilities and daily potential demand. Several node preprocessing techniques are implemented before branching. Both linear and nonlinear solvers are used in the branching process. The computational results are represented by using synthetic data. Also, they are compared to two well-known nonlinear and global optimizers and our proposed model outperforms both solvers. In the final part of this dissertation, we investigate the impact of the suggested demand model on revenue performance. The numerical results are presented using synthetic data produced by a modified Deterministic Choice-Based Linear Programming approach.