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Dick den Hertog

Dick den Hertog

Professor of Operations Research at Tilburg University & Scientific Director of the Data Science Center Tilburg

Dick den Hertog is professor of Operations Research at Tilburg University and scientific director of the Data Science Center Tilburg. His research interests cover various fields in prescriptive analytics, in particular linear and nonlinear optimization. In recent years his main focus has been on robust optimization. He is also active in applying the theory in real-life applications. In particular, he is interested in applications that contribute to a better society. For many years he has been involved in research for optimal flood protection, which was awarded by the INFORMS Franz Edelman Award in 2013. Currently, he is doing research to develop better optimization models and techniques for cancer treatment, and he is involved in research to optimize the food supply chain for the World Food Programme. He is chairman of the Dutch Network on the Mathematics Operations Research, and associate editor of three journals (Management Science, Operations Research, and INFORMS Journal on Optimization).

Track: Emerging Analytics

Monday, April 15, 9:10–10:00am

A Tutorial on Robust Optimization

In this presentation we explain the core ideas in robust optimization and show how to successfully apply them in practice.

Real-life optimization problems often contain parameters that are uncertain, due to, e.g., estimation or implementation errors. The idea of robust optimization is to find a solution that is immune against these uncertainties. The last two decades efficient methods have been developed to find such robust solutions. The underlying idea is to formulate an uncertainty region for the uncertain parameters for which one would like to safeguard the solution. In the robust paradigm it is then required that the constraints should hold for all parameter values in this uncertainty region. It can be shown that, e.g., for linear programming, for the most important choices of the uncertainty region, the final problem can be reformulated as linear optimization or conic quadratic optimization problems, for which very efficient solvers are available nowadays. Robust Optimization is valuable for practice, since it can solve large-scale uncertain problems and it only requires crude information on the uncertain parameters. Some state-of-the-art modeling packages have already incorporated the robust optimization technology.

In this tutorial we restrict ourselves to linear optimization. We will treat the basics of robust linear optimization, and also show the huge value of robust optimization in (dynamic) multistage problems. Robust optimization has already shown its high practical value in many fields: logistics, engineering, finance, medicine, etc. In this tutorial we will discuss some of these applications. We will also highlight some of the most important (recent) papers on Robust Optimization.