The Data-Driven Newsvendor Problem: New Bounds and Insights
Author(s)
Levi, Retsef; Perakis, Georgia; Uichanco, Joline![Thumbnail](/bitstream/handle/1721.1/111091/The%20Data-driven%20with%20SI.pdf.jpg?sequence=4&isAllowed=y)
DownloadThe Data-driven with SI.pdf (613.3Kb)
OPEN_ACCESS_POLICY
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2015-10Department
Sloan School of ManagementJournal
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
ISSN
0030-364X
1526-5463