This tutorial will provide you a basic understanding of practical robust optimization. Optimization problems in practice often contain parameters that are uncertain, due to e.g. estimation or rounding. 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, and then require 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 robust optimization problem can be reformulated as linear programming or conic quadratic programming problems, for which very efficient solvers are available nowadays. In this tutorial we restrict ourselves to linear programming; extensions to nonlinear programming are briefly sketched. We will treat the basics of robust linear optimization, and also show the huge value of robust optimization in (dynamic) multistage problems. Different applications of (adjustable) robust optimization will be given in the tutorial. Robust optimization has already shown its high practical value in many fields: logistics, engineering, finance, medicine, etc. Some state-of-the-art modeling packages have already implemented robust optimization technology.
Bio: Dr. den Hertog is Professor in the School of Economics and Management at Tilburg University. His research interests include various fields in linear and nonlinear optimization. He is also active in applying the theory in real-life applications. In particular, he has been involved in research to optimize water safety, cancer treatments, and (more recently) the food supply chain for World Food Programme. He serves on editorial board of Operations Research and Management Science.
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