Final Thesis

Scheduling under uncertainty: Efficient Optimization by multidimensional wavelet-analysis

Key Info

Basic Information

Process Systems Engineering
Focus/Key Topic:


Scheduling problem are commonly characterized by a large amount of degrees of freedom. In order to handle this issue, the SVT has recently proposed an algorithm to reduce the number of degrees of freedom in a reasonable manner by using the wavelet transform. This algorithm shall now be extended to multidimensional problems, as they arise in optimization under uncertainty. In your thesis, you will first generate performance data using a rigorous model of a chemical process. An artificial neural net will then represent this data and thereby form the basis of the optimization problem, to which the extended algorithm will be applied. If you are interested in this thesis, do not hesitate to contact me to arrange a personal discussion. Please always attach a short CV as well as your recent overview of your grades.