TITLE:  Dynamic Learning and Optimization for Online Revenue Management

ABSTRACT:

In a dynamic pricing problem where the demand function is unknown a priori, price experimentation can be used for demand learning. In practice, however, sellers are faced with business constraints when learning demand, such as the inability to conduct extensive experimentation, short sales window and limited inventory. In this talk I will discuss models and algorithms that combine price optimization with demand learning, and report implementation results at a large e-commerce marketplace for daily deals.

Biography:

He Wang is a PhD candidate in the Operations Research Center at MIT, advised by David Simchi-Levi.  He received master's degree in Transportation from MIT in 2013, and dual bachelor's degree in Industrial Engineering and Mathematics from Tsinghua University in 2011. He is a recipient of Edward Linde (1962) MIT Presidential Graduate Fellowship, a Finalist in IBM Service Science Best Student Paper Award, and second place in CSAMSE Best Student Paper Award.  His current research focuses on data-driven methods in revenue management and supply chains.