Protecting the data-driven newsvendor against rare events: a correction-term approach

Gokhan Metan and Aurélie Thiele ()
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Gokhan Metan: Humana
Aurélie Thiele: Lehigh University

Computational Management Science, 2016, vol. 13, issue 3, No 6, 459-482

Abstract: Abstract We propose an approach to the data-driven newsvendor problem that incorporates a correction factor to account for rare events, when the decision-maker has few historical data points at his disposal but knows the range of the demand. This mitigates a weakness of pure data-driven methodologies, specifically, the fact that they under-protect the system against tail events, which are in general under-observed in the empirical demand distribution. We test the approach in extensive computational experiments and provide a summary table of the numerical experiments to help the decision maker gain further insights.

Keywords: Data-driven optimization; Newsvendor problem; Rare events; Correction factor (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10287-016-0258-1

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