A Practical Guide to Robust Optimization

TL;DR

This paper provides a practical introduction to robust optimization, highlighting its usefulness, computational tractability, and potential for real-world applications, along with guidelines for practitioners.

Contribution

It offers a concise, practitioner-oriented overview of robust optimization, including do's and don'ts, with illustrative examples to facilitate real-world application.

Findings

Robust optimization is computationally tractable and tailored to available information.

Real-life applications of robust optimization are still underdeveloped.

Guidelines and best practices are provided for practitioners.

Abstract

Robust optimization is a young and active research field that has been mainly developed in the last 15 years. Robust optimization is very useful for practice, since it is tailored to the information at hand, and it leads to computationally tractable formulations. It is therefore remarkable that real-life applications of robust optimization are still lagging behind; there is much more potential for real-life applications than has been exploited hitherto. The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice. We provide a brief introduction to robust optimization, and also describe important do's and don'ts for using it in practice. We use many small examples to illustrate our discussions.