Dynamic Models for Robust Optimization

Abstract

This article discusses recent developments in robust optimization, with a focus on dynamic models. Traditional techniques such as stochastic programming and dynamic programming face dimensionality issues as the size of the problem increases; furthermore, probability distributions are often difficult to accurately estimate. After robust optimization emerged as a tractable modeling framework in static optimization, researchers investigated its applicability to dynamic decision making under uncertainty. We provide an overview and discussion of their findings.