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

详细信息    查看全文

• 作者：Gokhan Metan ; Aurélie Thiele
• 刊名：Computational Management Science
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：13
• 期：3
• 页码：459-482
• 全文大小：1,370 KB
• 刊物主题：Operations Research/Decision Theory; Optimization;
• 出版者：Springer Berlin Heidelberg
• ISSN：1619-6988
• 卷排序：13

文摘

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.KeywordsData-driven optimizationNewsvendor problemRare eventsCorrection factor