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Discrete Optimization of Technical Systems under Uncertainty

The development and operation of technical systems like production systems, logistics networks or large IT systems has to be based on a large number of design and configuration decisions to meet the performance requirements with a limited amount of resources and costs. Necessary decisions are often based on the solution of optimization  problems  with  discrete  or  mixed  discrete-continuous  parameters  describing the available alternatives.

Optimization problems of this kind are hard to solve as the number of available solutions exponentially increases with the number of decisions between discrete alternatives due to the “combinatorial explosion”. Most practical problems are simplified significantly to allow an algorithmic solution. Furthermore, in practice, decisions often have to be made with incomplete knowledge. The resulting uncertainty is usually not considered in existing optimization approaches even if this may result in considerable differences between the computed and real solution of the optimization problem. In some cases computed solutions may not even be feasible in practice.  

Another yet not deeply considered aspect of the optimization of technical systems is the role of people in the decision process. Mathematical methods and algorithms may compute optimal parameter values but the final solution must be accepted by a person and must be translated into concrete plans and instructions. To increase the applicability of optimization methods in practice, people must be regarded as part of the decision process. This implies that the process of optimization and result representation  must take into account the requirements of users.

The topic of the graduate school is optimization under uncertainty with the incorporation of people in the optimization process. Application scenarios that will be considered occur the areas of logistics, chemical production systems and IT systems.

Topics of the graduate school are interdisciplinary since the school combines research on methods from optimization, algorithms, statistics, applications and psychology.  As doctoral  candidates  with  different  backgrounds  are  expected  to  join  the  graduate school they will attend basic courses to obtain a common foundation for their research and they will participate in specific courses to obtain a deep knowledge in the area of their doctoral research projects.




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