Learning-Based Robust Optimization: Procedures and Statistical Guarantees

L. Jeff Hong (), Zhiyuan Huang () and Henry Lam ()
Additional contact information
L. Jeff Hong: School of Management and School of Data Science, Fudan University, Shanghai 200433, China
Zhiyuan Huang: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
Henry Lam: Department of Industrial Engineering and Operations Research, Columbia University, New York, New York 10027

Management Science, 2021, vol. 67, issue 6, 3447-3467

Abstract: Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions for optimization problems with uncertain constraints. In this paper, we study a statistical framework to integrate data into RO based on learning a prediction set using (combinations of) geometric shapes that are compatible with established RO tools and on a simple data-splitting validation step that achieves finite-sample nonparametric statistical guarantees on feasibility. We demonstrate how our required sample size to achieve feasibility at a given confidence level is independent of the dimensions of both the decision space and the probability space governing the stochasticity, and we discuss some approaches to improve the objective performances while maintaining these dimension-free statistical feasibility guarantees.

Keywords: robust optimization; chance constraint; prediction set learning; quantile estimation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2020.3640 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:6:p:3447-3467

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().