Vortragende(r): Prof. Dr. Nicole Megow
Combinatorial optimization problems appear in various application areas, e.g., in logistics, production planning, communication and financial markets. When solving real‐world optimization problems, uncertainty in the input data is a prevalent issue: resources may become unavailable, material arrives late, jobs may take more or less time than originally estimated, etc. In this talk, we will discuss models, such as online, stochastic and robust models, and solution methods for optimization under uncertainty. The main focus will lie on provable performance guarantees. What is an algorithm that "performs well", even under uncertain input? Exemplarily, we will consider scheduling problems and show how to design algorithms and derive rigorous worst‐case performance guarantees.
Nicole Megow joined the computer science institute at the University of Bremen as a full professor in August 2016. Her main research interests are in the field of combinational optimization, on the design and analysis of efficient algorithms with provable performance guarantees. She and her group contribute with theoretic results and apply them to complex real‐world environments, e.g, in production management and logistics.
After her studies at TU Berlin and M.I.T. (Cambridge, US), Nicole Megow received her doctorate in mathematics from TU Berlin in 2006. She was postdoc and senior research at the Max Planck Institute for Informatics, Saarbrücken, held a position as interim professor for discrete optimization at TU Darmstadt 2011/12, and headed an Emmy Noether Research Group at TU Berlin. Before joining the University of Bremen, she was an assistant professor for discrete mathematics at TU Munich. Her work has won several awards, including the Dissertation Award by the German Operations Research Society (GOR) and the Heinz Maier‐Leibnitz Prize by the German Research Foundation (DFG).