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

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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.

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Authors and Affiliations

  1. Humana, Irving, TX, 75063, USA

    Gokhan Metan

  2. Lehigh University, Bethlehem, PA, USA

    Aurélie Thiele

Authors
  1. Gokhan Metan
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  2. Aurélie Thiele
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Correspondence to Aurélie Thiele.

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Metan, G., Thiele, A. Protecting the data-driven newsvendor against rare events: a correction-term approach. Comput Manag Sci 13, 459–482 (2016). https://doi.org/10.1007/s10287-016-0258-1

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  • DOI: https://doi.org/10.1007/s10287-016-0258-1

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