Mathematical optimization with discrete and continuous decisions in (non-) linear contexts
Development of methods that can solve large instances
Protecting best possible decisions against uncertainties already in the modeling process by method development in robust optimization and integrating robustness and stochasticity. E.g. distributional robustness
novel approaches for integrating analytical models with knowledge learnt from data
applications in energy, logistics, material science, and the natural sciences
third-party projects funded by the DFG, the EU, the BMBF, the BMWK, the Fee State of Bavaria, etc.
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