November 07, 2013, 14:15, Room GC B3 424 (click here for the map)
In transportation companies, demand forecasting is crucial. To maximize revenue, these systems use historical data. However, due to booking limits, registered reservations do not represent the real demand. We first present a comprehensive review on different aspects of demand modeling in the context of revenue management systems. Then, we propose a new non-parametric global optimization approach which is able to model demand by using choice probabilities. Our proposed model is able to extract seasonal features of demand and customer utilities for a given product. Finally, in a comparative study, we investigate the impact of different methods of customer preference estimation on revenue.
Shadi Sharif Azadeh completed her Masters and Ph.D. in Mathematics (Operations Research) at Polytechnique Montreal in 2013. She is a member of GERAD (Group for Research in Decision Analysis) and CIRRELT (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation). She is presently a postdoctoral fellow at Polytechnique Montreal.