Theory and Applications of Robust Optimization

TL;DR

This paper surveys the theoretical foundations and practical applications of Robust Optimization, emphasizing its computational efficiency, modeling versatility, and broad applicability across multiple domains.

Contribution

It provides a comprehensive overview of recent theoretical advances and links Robust Optimization to adaptable multi-stage decision-making models.

Findings

RO is computationally attractive for complex problems

RO has broad applicability in finance, statistics, and engineering

Recent links between RO and multi-stage decision models

Abstract

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.