The Data-Driven Newsvendor Problem: New Bounds and Insights
Name
The Data-driven with SI.pdf
Size
613.38 KB
Format
Adobe PDF
Checksum (MD5)
b490227691b0f96cf70a09c4d71c3478
Author(s)
Levi, Retsef
Perakis, Georgia
Uichanco, Joline
Date Issued
October 2015
Journal
Operations Research
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Levi, Retsef, et al. “The Data-Driven Newsvendor Problem: New Bounds and Insights.” Operations Research 63, 6 (December 2015): 1294–1306 © 2015 Institute for Operations Research and the Management Sciences (INFORMS)
Version
Author's final manuscript
Abstract
Consider the newsvendor model, but under the assumption that the underlying demand distribution is not known as part of the input. Instead, the only information available is a random, independent sample drawn from the demand distribution. This paper analyzes the sample average approximation (SAA) approach for the data-driven newsvendor problem. We obtain a new analytical bound on the probability that the relative regret of the SAA solution exceeds a threshold. This bound is significantly tighter than existing bounds, and it matches the empirical accuracy of the SAA solution observed in extensive computational experiments. This bound reveals that the demand distribution’s weighted mean spread affects the accuracy of the SAA heuristic.
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1287/opre.2015.1422
