Faculty of Business Administration
Visiting Scholar Seminar
Assortment and Pricing Optimization on (Constant) Focal Luce Model
Dr. Lianmin ZHANG
Shenzhen Research Institute of Big Data (SRIBD)
Date: 26 March 2021 (Friday)|
This paper investigates the assortment and pricing problems under the Focal Luce Model (FLM), which is a new choice model describing the phenomenon that consumers may focus on some alternatives more than others. Both the MNL model and the threshold Luce model can be seen as special case of FLM. With discussing the basic features of FLM, we show that the assortment problem can be solved in polynomial time in general, and a markup-order assortment is optimal under a mild condition. For the pricing optimization problem, the quasi-same-markup/same-attractiveness pricing policy is optimal, while there only exists one unique Nash equilibrium in most cases. Our numerical experiment mainly compares FLM with the MNL model and the threshold Luce model. The results indicate that FLM can outperform other two model except the ground-truth model.
Lianmin Zhang is a Research Scientist at Shenzhen Research Institute of Big Data (SRIBD). Before joining SRIBD, he was an associate professor of the School of Management and Engineering at Nanjing University. He received his PhD in System Engineering and Engineering Management Science from The Chinese University of Hong Kong. His current research interests include supply chain management and operations research. He has published articles in Production and Operations Management, Decision Science, European Journal of Operations Research, and other journals. His current research interests include data driven optimization, robust optimization, assortment optimization, target-based risk management.