The Mathematical Optimization department develops efficient modeling, simulation, and optimization methods for difficult problems in transport and logistics, telecommunications, energy supply, and healthcare. We combine theoretical insight and practical experience to provide operational optimization cores that can be integrated as "solvers" into the software systems of our cooperation partners.

Advances in basic research on branch-and-cut-and-price algorithms, algorithmic (hyper)graph theory, combinatorics, algorithmic game theory, and convex optimization allow to study large-scale models. The integrated treatment of multiple levels, multiple objectives, behavioral aspects, and data uncertainty is demanded by our application partners and requires novel decomposition techniques, adaptive and dynamic methods, and parallelization.