Robust Price-setting Newsvendor Problem
- Yixuan XIAO (Principal Investigator / Project Coordinator)Department of Management Sciences
- Ye LU (Co-Investigator)
DescriptionThe price-setting newsvendor problem has been well studied in the literature. However,it is commonly assumed that retailers have complete demand information modeled as afunction of price and other random noises. In reality, a retailer may have very limitedinformation on a demand model, because a retailer who has exercised only a few priceswill not have sufficient information to accurately estimate a demand model. This createsa gap between academic research and practical applications. In this project, we considerthe price-setting newsvendor problem with incomplete demand information. A retailermakes price and inventory decisions to minimize the maximum regret, defined as thedifference between the expected profit based on limited demand information and theexpected profit based on complete demand information. We try to derive a closed-formexpression of the optimal price and inventory decisions for this problem, which makesour model easy to implement and hence practically favorable. To show its applicability,we will test our model on a real data set.?
|Effective start/end date||1/09/16 → 7/04/20|
- robust optimization , newsvendor with pricing , incomplete demand information , data-driven optimization ,