Felix Prause
I am interested in mixed integer programming, heuristics, and bounds for rolling stock rotation planning. Currently, I am working on incorporating condition-based and predictive maintenance into the schedules.
📬 Contact
- office
- Room 3001 at ZIB
- prause (at) zib.de
🎓 Curriculum vitae
- since 2020
- Researcher at ZIB
- May 2020
- M.Sc. in Mathematics at TUB
- Oct 2016
- B.Sc. in Mathematics at TUB
đź“ť Publications and preprints
Preprints
- Prause, F., and Borndörfer, R. (2024). An Iterative Refinement Approach for the Rolling Stock Rotation Problem with Predictive Maintenance.
[arXiv]
[BibTeX]
- Prause, F. (2023). A Multi-Swap Heuristic for Rolling Stock Rotation Planning with Predictive Maintenance (ZIB Report No. 23-29). Zuse Institute Berlin.
[URL]
[BibTeX]
- Prause, F., and Borndörfer, R. (2023). Construction of a Test Library for the Rolling Stock Rotation Problem with Predictive Maintenance (ZIB Report No. 23-20). Zuse Institute Berlin.
[URL]
[BibTeX]
Full articles
- Prause, F., Borndörfer, R., Grimm, B., and Tesch, A. (2024). Approximating Rolling Stock Rotations with Integrated Predictive Maintenance. Journal of Rail Transport Planning & Management, 30.
DOI: 10.1016/j.jrtpm.2024.100434
[BibTeX]
- Prause, F., Hoppmann-Baum, K., Defourny, B., and Koch, T. (2021). The Maximum Diversity Assortment Selection Problem. Mathematical Methods of Operations Research, 93.
DOI: 10.1007/s00186-021-00740-2
[BibTeX]
🔬 Projects
SynLab researches mathematical generalization of application-specific advances achieved in the Gas-, Rail– and MedLab of the research campus MODAL. The focus is on exact methods for solving a broad class of discrete-continuous optimization problems. This requires advanced techniques for structure recognition, consideration of nonlinear restrictions from practice, and the efficient implementation of mathematical algorithms on modern computer architectures. The results are bundled in a professional software package and complemented by a range of high-performance methods for specific applications with a high degree of innovation.
Airplane data quality is uneven due to varied sources and sensor limitations. This project aims to create data processing services for Component Spotting to help airlines optimize business processes and improve customer satisfaction. We will develop methods for modeling and evaluating data quality, creating a semantic and uniform description for processing models. Using AI algorithms, we will handle inaccuracies and uncertainties to form an analysis-friendly information model.
đź’¬ Talks and posters
Conference and workshop talks
- Sep 2024
- A Bayesian Rolling Horizon Approach for Rolling Stock Rotation Planning with Predictive Maintenance
ATMOS Conference, London - Mar 2024
- A Multi-Swap Heuristic for Rolling Stock Rotation Planning with Predictive Maintenance
INOC Conference, Dublin - Aug 2023
- Construction of a Test Library for the Rolling Stock Rotation Problem with Predictive Maintenance
OR Conference, Hamburg - Apr 2023
- Approximating the Rolling Stock Rotation Problem with Predictive Maintenance by a State-Expanded Event-Graph
RailBelgrade Conference, Belgrade
👨‍🏫Teaching
- summer 2024
- Lecture Assistant for Diskrete Mathematik III at FUB
- summer 2024
- Lecture Assistant for Applied Integer Programming at FUB
- winter 2023
- Lecture Assistant for Diskrete Mathematik II at FUB
- summer 2023
- Lecture Assistant for Diskrete Mathematik 1 at FUB
- summer 2017
- Tutor for Mathematik II fĂĽr Wirtschaftswissenschaftler at TUB
- winter 2016
- Tutor for Mathematik I fĂĽr Wirtschaftswissenschaftler at TUB
- summer 2016
- Tutor for Mathematik II fĂĽr Wirtschaftswissenschaftler at TUB
- winter 2015
- Tutor for Mathematik I fĂĽr Wirtschaftswissenschaftler at TUB
- summer 2015
- Tutor for Mathematik II fĂĽr Wirtschaftswissenschaftler at TUB
- winter 2014
- Tutor for Lineare Algebra fĂĽr Ingenieurwissenschaften at TUB