Dr. Andreas Bärmann


Lehrstuhl für Wirtschaftsmathematik


(Reduced Order Modelling, Simulation and Optimization of Coupled systems):
ROMSOC: Reduced Order Modelling, Simulation and Optimization of Coupled systems
Dr. Andreas Bärmann; Prof. Dr. Alexander Martin
(01.09.2017 - 31.08.2021)

Publikationen (Download BibTeX)

Bärmann, A., Büsing, C., & Liers, F. (2019). Globalized Robust Optimization with Gamma-Uncertainties.
Bärmann, A., & Liers, F. (2018). Aggregation Methods for Railway Network Design Based on Lifted Benders Cuts. In Borndörfer R, Klug T, Lamorgese L, Mannino C, Reuther M, Schlechte T (Eds.), Handbook of Optimization in the Railway Industry. (pp. 47--72). Cham: Springer International Publishing.
Bärmann, A. (2018). An Optimal Expansion Strategy for the German Railway Network Until 2030. In Fink A, Fügenschuh A, Geiger MJ (Eds.), Operations Research Proceedings 2016 (pp. 3--8). Cham: Springer International Publishing.
Bärmann, A., Gellermann, T., Merkert, M., & Schneider, O. (2018). Staircase Compatibility and its Applications in Scheduling and Piecewise Linearization. Discrete Optimization, 29, 111-132. https://dx.doi.org/10.1016/j.disopt.2018.04.001
Bärmann, A., Martin, A., & Schneider, O. (2017). A comparison of performance metrics for balancing the power consumption of trains in a railway network by slight timetable adaptation. Public Transport, 9(1), 95-113. https://dx.doi.org/10.1007/s12469-017-0160-4
Bärmann, A., Martin, A., & Schuelldorf, H. (2017). A Decomposition Method for Multiperiod Railway Network Expansion - With a Case Study for Germany. Transportation Science. https://dx.doi.org/10.1287/trsc.2017.0747
Bärmann, A., Pokutta, S., & Schneider, O. (2017). Emulating the Expert: Inverse Optimization through Online Learning. In Precup D, Teh YW (Eds.), Proceedings of the 34th International Conference on Machine Learning (ICML) (pp. 400--410). International Convention Centre, Sydney, Australia: PMLR.
Bärmann, A., Heidt, A., Martin, A., Pokutta, S., & Thurner, C. (2015). Polyhedral approximation of ellipsoidal uncertainty sets via extended formulations: a computational case study. Computational Management Science, 13(2), 151-193. https://dx.doi.org/10.1007/s10287-015-0243-0
Bärmann, A., Liers, F., Martin, A., Merkert, M., Thurner, C., & Weninger, D. (2015). Solving Network Design Problems via Iterative Aggregation. Mathematical Programming Computation, 7(2), 189-217. https://dx.doi.org/10.1007/s12532-015-0079-1

Zuletzt aktualisiert 2016-03-10 um 02:00