Protecting the data-driven newsvendor against rare events: a correction-term approach |
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Authors: | Gokhan Metan Aurélie Thiele |
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Institution: | 1.Humana,Irving,USA;2.Lehigh University,Bethlehem,USA |
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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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