Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	陆烨教授，中国科学技术大学
Inviter:	袁亚湘院士
Title:	Robust Price-setting Newsvendor Problem
Time & Venue:	2019.4.3 15:00-16:00 Z301
Abstract:	The price-setting newsvendor problem is well studied in the literature. However, it is commonlyassumed that retailers have complete demand information modeled as a function of price and randomnoise. In reality, a retailer may have very limited information on a demand model because a retailer whohas exercised only a few prices does not have sufficient information to accurately estimate a demandmodel. This creates a gap between academic research and practical applications. In this talk, weconsider the price-setting newsvendor problem in which the retailer knows the expected demand on afew exercised price points and the distribution of the random noise. Both additive and multiplicativedemand models are studied. The retailer makes price and inventory decisions to minimize the maximumregret, defined as the difference between theexpected profit based on limited demand information andthe expected profit based on complete demand information. We show that this robust optimizationproblem can be reduced to a one-dimensional optimization problem, and derive the optimal price andinventory decisions. We also provide a demand learning policy that can reduce the minmax regret toany δ within O(1/δ) steps. Extensive numerical studies show that our method has a great performancethat dominates those of regression methods.