A Robust Price-Setting Newsvendor Problem

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

9 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)276-292
Journal / PublicationProduction and Operations Management
Issue number1
Online published17 Sep 2020
Publication statusPublished - Jan 2021


The price-setting newsvendor problem is well studied in the literature. However, it is commonly assumed that retailers have complete demand information modeled as a function of price and random noise. In reality, a retailer may have very limited information on a demand model because a retailer who has exercised only a few prices does not have sufficient information to accurately estimate a demand model. This creates a gap between academic research and practical applications. In this study, we consider the price-setting newsvendor problem in which the retailer knows the expected demand on a few exercised price points and the distribution of the random noise. Both additive and multiplicative demand models are studied. The retailer makes price and inventory decisions to minimize the maximum regret, which is defined as the difference between the expected profit based on limited demand information and that based on complete demand information. We show that this robust optimization problem can be reduced to a one-dimensional optimization problem, and we derive the optimal price and inventory decisions. We also provide a demand learning policy that can reduce the minmax regret to any δ within O(1/δ) steps. We also study the problem in which the retailer maximizes the worst-case profit, and we obtain similar results. Extensive numerical studies show that our method has a great performance that dominates that of the regression method.

Research Area(s)

  • incomplete demand information, newsvendor problem, pricing decision

Citation Format(s)

A Robust Price-Setting Newsvendor Problem. / He, Rongchuan; Lu, Ye.

In: Production and Operations Management, Vol. 30, No. 1, 01.2021, p. 276-292.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review