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【Talk&Lecture】School of Data Science and Management Engineering Academic Lecture No.86: A Representative Consumer Model in Data-Driven Pricing and Promotion Design Problem

Published:2019-11-22

Date: 25th November, 2019

Time: 10:00 – 11:30 a.m.

Venue: Room1004, Administration Building, Zijingang Campus, Zhejiang University.

 

Speaker Introduction】:Yan Zhenzhen is an assistant professor at the School of Physical and Mathematical Sciences, Nanyang Technological University. She received her Ph.D. in Management Science from the National University of Singapore, and her BSc and MSc in Management Science from the National University of Defense and Technology in China. Her research interests mainly focus on the interplay between optimization and data analytics. She is keen to solve various operations management problems and engineering problems from the distrbibutionally robust perspective, including e-commerce operations, supply chain design, and healthcare operations. She is also particularly interested in data-driven pricing problems, sequential decision-making problems, and online matching problems.

 

Lecture Abstract】:We develop a data-driven approach to recover the right choice model for a multi-product pricing problem, using the theory of a representative consumer in discrete choice. This approach uses a regularization function to capture diversification in choice behaviour and establishes a set of closed-form relationships between the prices and choice probabilities with a separable function. By penalizing against deviation from these relationships in the data set, we propose a new loss function that is used to derive efficient algorithms for the inverse optimization problem, in both online and offline settings. This allows us to build tractable models for both estimation and price optimization problem. Extensive tests using both synthetic and industry data demonstrate the benefits of this approach in a multi-product pricing problem. By generalizing the representative consumer model to a sequential decision-making process, we further develop a data-driven framework to solve a bundle-pricing problem and a threshold-type of promotion optimization problem, which is widely used in e-commerce.


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