The Data-Driven Newsvendor Problem: New Bounds and Insights

Retsef Levi (), Georgia Perakis () and Joline Uichanco ()
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Retsef Levi: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Georgia Perakis: Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Joline Uichanco: Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109

Operations Research, 2015, vol. 63, issue 6, 1294-1306

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.

Keywords: data-driven stochastic program; newsvendor problem; sample average approximation; weighted mean spread (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (52)

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