【Time】14:00-15:00, May 25th, 2017, Thursday

【Venue】Room N510, Shunde Building

【Speaker】Assistant Prof. He Wang, Georgia Tech

【Host】Dr. Simin Huang & Hai Jiang

【Title】 Data-driven Dynamic Pricing Algorithms in Revenue Management

【Abstract】We consider a revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As common in practice, we assume the demand function contains unknown parameters, which must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a tradeoff commonly referred to as the exploration-exploitation tradeoff. We propose a class of dynamic pricing algorithms that builds upon the simple yet powerful machine learning technique known as Thompson sampling to address the challenge of balancing the exploration-exploitation tradeoff inventory constraints. Moreover, we show how our algorithms can be extended for use in general multi-armed bandit problems with resource constraints, with applications in other revenue management settings and beyond.

【Bio Note】 He Wang is an assistant professor in the H. Milton Stewart School of Industrial & Systems Engineering at Georgia Tech. His research interests include revenue management, supply chain and logistics, and statistical learning. His recent research focuses on the interface between machine learning and operations management, where he develops data-driven methods for applications including inventory management and dynamic pricing. He received his Ph.D. in operations research and M.S. in transportation, both from MIT. He received a B.S. in industrial engineering and a B.S. in math from Tsinghua University.

All interested are welcome!