Abstract: This tutorial will provide you a basic understanding of practical robust optimization, which is indispensable for each Operations Research practitioner.
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 decade 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 final problem can be reformulated as linear programming or conic quadratic programming problems, for which very efficient solvers are available nowadays. In this talk we restrict ourselves to linear programming. 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. Some state-of-the-art modelling packages have already implemented robust optimization technology.