Shadi Sharif Azadeh

A non-parametric approach to demand forecasting in revenue management

In revenue management, the profitability of the inventory and pricing decisions rests on the accuracy of demand forecasts. However, whenever a product is no longer available, true demand may differ from registered bookings, thus inducing a negative bias in the estimation figures, as well as an artificial increase in demand for substitute products. In order to address these issues, we propose an original Mixed Integer Nonlinear Program (MINLP) to estimate product utilities as well as capturing seasonal effects. This behavioral model solely rests on daily registered bookings and product availabilities. Its outputs are the product utilities and daily potential demands, together with the expected demand of each product within any given time interval. Those are obtained via a tailored algorithm that outperforms two well-known generic software for global optimization. HighlightsA new mixed integer nonlinear programming model for estimating data from censored observations in revenue management systems.Simultaneous capture of customer behavior and seasonal variations.Design and implementation of a customized semi-global optimization algorithm, based on partial enumeration and cuts.Numerical tests.

The impact of customer behavior models on revenue management systems

Revenue management (RM) can be considered an application of operations research in the transportation industry. For these service companies, it is a difficult task to adjust supply and demand. In order to maximize revenue, RM systems display demand behavior by using historical data. Usually, parametric methods are applied to estimate the probability of choosing a product at a given time. However, parameter estimation becomes challenging when we need to deal with constrained data. In this research, we evaluate the performance of a revenue management system when a non-parametric method for choice probability estimation is chosen. The outcomes of this method have been compared to the total expected revenue using synthetic data.

Railway demand forecasting in revenue management using neural networks

This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies significantly. The prediction model used in this research is an improved multi-layer perceptron (MLP) describing the relationship between number of passengers and factors affecting this quantity based on historical data. Relevant pre-processing approaches have been employed to make learning more efficient. The generalisation of the network is tested to evaluate the accuracy prediction of the regression model for future trends of reservations and cancellations using actual railroad data. The results show that it is a promising approach in railway demand forecasting with a low prediction error.