Operations research can be regarded as a collection of useful tools and techniques for optimizing complex systems and decision-making. As most of the members of our team have an operations research background, we have a broad spectrum of general-purpose techniques in our toolbox. In addition, we have developed deeper skills in some of those techniques by means of our range of theoretical and practical experience.
Usually, the complex optimization problems we want to solve are subject to various types of uncertainty (e.g. uncertainty of the data, of the outcome) and we want to ensure that we take this into account in our decision process, i.e. that the solution we provide to a specific problem remains feasible and reasonably good under various perturbations. Robust optimization is a framework that allows one to include uncertainty as part of the formulation. Starting with some reasonable hypotheses regarding the uncertainty sets, one can solve those problems efficiently. We have experienced robust optimization formulations of corporate portfolio optimization problems and risk management problems in the pharma sector.