Intelligent choice-based network revenue management

Choice-based network revenue management concentrates on importing choice models within the traditional revenue management system. Multinomial logit is a popular and well-known model which is the basic choice model in revenue management. Empirical results indicate inadequacy of this model for predicting itinerary shares; therefore, more realistic models, such as nested logit, can be proposed for substituting it. Incorporating complex choice models in the optimization module based on statistical tests without considering the complexity of the obtained mathematical model would lead to increase in the complexity of a system without obtaining signicant improvement. Considering the in uence of discrete choice model on the structure of optimization model, it is necessary to analyze the interaction between specic discrete choice and optimization models. In this paper, a knowledge acquisition subsystem is introduced for providing intelligence and considering the most suitable choice models. We develop the feedforward multilayer perceptron articial neural network for forecasting revenue improvement percent obtained by using more realistic choice models. The obtained results demonstrate that the new system will decrease the complexity of the system, simultaneously, while preserving revenue of the rm. According to the computational results, by increasing the resource restriction, the process of incorporating more realistic choice model will be more important.