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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Vydáno v:	Mathematical programming Ročník 167; číslo 2; s. 235 - 292
Hlavní autoři:	Bertsimas, Dimitris, Gupta, Vishal, Kallus, Nathan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2018 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
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