MSc (Research) Student

Research area: 'Neural Network applications in Revenue Management: Forecasting the demand'

Airlines face demand patterns that are highly stochastic. What is more, they also experience perishable inventory - unsold seats at the time of flight departure have no longer any value for an airline. That is why airlines implement complex Revenue Management systems in order to maximise their total revenue. Among other factors, demand forecast accuracy tends to be the crucial element of the Revenue Management system and, thus, foreseeing the demand with least possible error is an issue of highest importance for airlines.

Since this is stochastic problem with many probabilistic variables Neural Networks are highly probable to improve the accuracy of predictions. This project aims at researching various Neural Network models for Revenue Management forecasting module in order to improve accuracy of demand prognoses.