Speaker
Polychronis Economou
(University of Patras)
Description
In reservation-based services with volatile demand and competitive pricing pressures, dynamically optimizing prices is essential for revenue maximization. This paper introduces a data-driven pricing framework that integrates demand forecasting with stochastic optimization. We model customer arrivals using a non-homogeneous Poisson process, where expected demand is estimated through a Poisson Generalized Linear Model (GLM) trained on historical data. Leveraging this demand model, we formulate a dynamic pricing strategy using stochastic dynamic programming to update prices over time, considering real-time availability and market conditions. The approach aims to maximize total expected revenue while adapting to evolving demand patterns.
| Classification | Mainly methodology |
|---|---|
| Keywords | Poisson GLM, Revenue Optimization, Stochastic Dynamic Programming |
Primary author
Ekaterini Skamnia
(University of Patras)
Co-authors
Polychronis Economou
(University of Patras)
Sotiris Bersimis
(University of Piraeus, Greece)