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The Data-Driven Newsvendor Problem: New Bounds and Insights

Retsef Levi¹; Georgia Perakis¹; Joline Uichanco²

¹ Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 · ² Ross School of Business, University of Michigan , Ann Arbor, Michigan 48109,

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.

DOI

10.1287/opre.2015.1422

Volume

63 (6)

Pages

1294-1306

Language

en

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Sources

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