Data-driven robust optimization

The last decade witnessed an explosion in the availability of data for operations research applications. Motivated by this growing availability, we propose a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests. The approach is flexibl...

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Veröffentlicht in:	Mathematical programming Jg. 167; H. 2; S. 235 - 292
Hauptverfasser:	Bertsimas, Dimitris, Gupta, Vishal, Kallus, Nathan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018 Springer Nature B.V
Schlagworte:	Calculus of Variations and Optimal Control; Optimization Combinatorics Design optimization Full Length Paper Mathematical and Computational Physics Mathematical Methods in Physics Mathematics Mathematics and Statistics Mathematics of Computing Nonlinear programming Numerical Analysis Operations research Optimization techniques Portfolio management Queues Statistical analysis Theoretical Uncertainty Hypothesis testing Robust optimization Chance-constraints 62H15 (Multivariate Analysis: Hypothesis Testing) 80M50 (Optimization: Operations research, mathematical programming) Data-driven optimization
ISSN:	0025-5610, 1436-4646
Online-Zugang:	Volltext
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