MSIS Seminar
Resource Constrained Revenue Management with Demand Learning and Large Action Spaces
In this talk, I will present my recent works on resource-constrained revenue management with demand learning and large action spaces. We study a class of well-known RM problems such as dynamic pricing and assortment optimization subject to non-replenishable inventory constraints, where demand or choice model information is unknown a priori and needs to be estimated, and the action spaces (price vectors, assortments) are large. We present a general primal-dual optimization algorithm with upper confidence bounds to achieve optimal asymptotic regret. We also extend this result to nonparametric demand modeling in network revenue management problems via a robust ellipsoid method.
If you have any further questions, please contact Jalaj Upadhyay at jalaj.upadhyay@rutgers.edu