Frauke Liers at AAAI-24!
Prof. Dr. Frauke Liers
Department of Data Science (DDS)
Professorship of Optimization under Uncertainty & Data Analysis (Prof. Dr. Liers)
Cauerstraße 11
91058 Erlangen
- Phone number: +49 9131 85-67151
- Fax number: +49 9131 85-67162
- Email: frauke.liers@fau.de
- Website: https://www.datascience.nat.fau.eu/research/groups/ouda/members/frauke-liers/
Publications
2024
- Aigner, K.-M., Goerigk, M., Hartisch, M., Liers, F., & Miehlich, A. (2024). A Framework for Data-driven Explainability in Mathematical Optimization. In Proceedings of the The 38th Annual AAAI Conference on Artificial Intelligence. Vancouver, CA.
- Bernhard, D., Liers, F., & Stingl, M. (2024). Single and Joint Chance-Constrained Optimization with Continuous Distributions. (Unpublished, Submitted).
- Dienstbier, J., Liers, F., & Rolfes, J. (2024). A Positive Semidefinite Safe Approximation of Multivariate Distributionally Robust Constraints Determined by Simple Functions. (Unpublished, Submitted).
- Dienstbier, J., Liers, F., & Rolfes, J. (2024). A Safe Approximation Based on Mixed-Integer Optimization for Non-Convex Distributional Robustness Governed by Univariate Indicator Functions. (Unpublished, Submitted).
- Glomb, L., Liers-Bergmann, F., & Rösel, F. (2024). A novel Pareto-optimal Cut Selection Strategy for Benders Decomposition. (Unpublished, In review).
- Glomb, L., Liers-Bergmann, F., & Rösel, F. (2024). Fleet & tail assignment under uncertainty. Discrete Optimization, 52. https://dx.doi.org/10.1016/j.disopt.2024.100836
- Kreuz, S., Apeleo-Zubiri, B., Englisch, S., Kang, S.-G., Ramachandramoorthy, R., Spiecker, E.,... Rolfes, J. (2024). Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization. (Unpublished, Submitted).
- Kuchlbauer, M., Dienstbier, J., Muneer, A., Hedges, H., Stingl, M., Liers, F., & Pflug, L. (2024). Quality Control in Particle Precipitation via Robust Optimization. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2024.108619
- Lüer, L., Peters, I.M., Smith, A.-S., Dorschky, E., Eskofier, B., Liers-Bergmann, F.,... Brabec, C. (2024). A digital twin to overcome long-time challenges in photovoltaics. Joule, 8, 1-17. https://doi.org/10.1016/j.joule.2023.12.010
- Wagner, F., Egger, D., & Liers, F. (2024). Optimized Noise Suppression for Quantum Circuits. (Unpublished, In review).
2023
- Adelhütte, D., Braun, K., Liers, F., & Tschuppik, S. (2023). Minimizing delays of patient transports with incomplete information: A modeling approach based on the Vehicle Routing Problem.
- Aigner, K.-M., Burlacu, R., Liers, F., & Martin, A. (2023). Solving AC Optimal Power Flow with Discrete Decisions to Global Optimality. Informs Journal on Computing. https://doi.org/10.1287/ijoc.2023.1270
- Aigner, K.-M., Bärmann, A., Braun, K., Liers, F., Pokutta, S., Schneider, O.,... Tschuppik, S. (2023). Data-driven Distributionally Robust Optimization over Time. INFORMS Journal on Optimization. https://doi.org/10.1287/ijoo.2023.0091
- Frank, U., Dienstbier, J., Tischer, F., Wawra, S., Gromotka, L., Walter, J.,... Peukert, W. (2023). Multidimensional Fractionation of Particles. Separations, 10(4). https://doi.org/10.3390/separations10040252
- Glomb, L., Liers, F., & Rösel, F. (2023). A stochastic optimization approach for optimal Tail Assignment with knowledge-based predictive maintenance. CEAS Aeronautical Journal. https://doi.org/10.1007/s13272-023-00663-0
- Glomb, L., Liers, F., & Rösel, F. (2023). Optimizing integrated aircraft assignment and turnaround handling. European Journal of Operational Research, 310(3), 1051 - 1071. https://doi.org/10.1016/j.ejor.2023.03.036
- Gutina, D., Bärmann, A., Roeder, G., Schellenberger, M., & Liers-Bergmann, F. (2023). Optimization over decision trees: a case study for the design of stable direct-current electricity networks. Optimization and Engineering. https://doi.org/10.1007/s11081-023-09788-x
- Wagner, F., Bärmann, A., Liers, F., & Weissenbäck, M. (2023). Improving Quantum Computation by Optimized Qubit Routing. Journal of Optimization Theory and Applications. https://doi.org/10.1007/s10957-023-02229-w
2022
- Aigner, K.-M., Clarner, J.-P., Liers, F., & Martin, A. (2022). Robust Approximation of Chance Constrained DC Optimal Power Flow under Decision-Dependent Uncertainty. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2021.10.051
- Aigner, K.-M., Schaumann, P., von Loeper, F., Martin, A., Schmidt, V., & Liers, F. (2022). Robust DC Optimal Power Flow with Modeling of Solar Power Supply Uncertainty via R-Vine Copulas. Optimization and Engineering. https://doi.org/10.1007/s11081-022-09761-0
- Anjos, M., Hante, F.M., & Liers-Bergmann, F. (2022). Special issue on Energy Networks. Mathematical Methods of Operations Research. https://doi.org/10.1007/s00186-022-00791-z
- Biefel, C., Liers, F., Rolfes, J., Schewe, L., & Zöttl, G. (2022). Robust Market Equilibria under Uncertain Cost. European Journal of Operational Research, 302(3), 1230-1241. https://doi.org/10.1016/j.ejor.2022.02.030
- Biefel, C., Liers, F., Rolfes, J., & Schmidt, M. (2022). Affinely Adjustable Robust Linear Complementarity Problems. SIAM Journal on Optimization, 32(1), 152-172. https://doi.org/10.1137/20M1359778
- Hoch, B., & Liers, F. (2022). An integrated rolling horizon and adaptive-refinement approach for disjoint trajectories optimization. Optimization and Engineering. https://doi.org/10.1007/s11081-022-09719-2
- Hoch, B., & Liers, F. (2022). An Integrated Rolling Horizon and Adaptive-Refinement Approach for Disjoint Trajectories Optimization. Optimization and Engineering. https://doi.org/10.1007/s11081-022-09719-2
- Kreimeier, T., Kuchlbauer, M., Liers, F., Stingl, M., & Walther, A. (2022). Towards the Solution of Robust Gas Network Optimization Problems Using the Constrained Active Signature Method. In Proceedings of the 10th International Network Optimization Con- ference (pp. 45 - 50). Aachen, Germany.
- Kuchlbauer, M., Liers, F., & Stingl, M. (2022). Adaptive bundle methods for nonlinear robust optimization. Informs Journal on Computing, 34(4), 2106 - 2124. https://doi.org/10.1287/ijoc.2021.1122
- Kuchlbauer, M., Liers, F., & Stingl, M. (2022). Outer approximation for mixed-integer nonlinear robust optimization. Journal of Optimization Theory and Applications, 195, 1056–1086. https://doi.org/10.1007/s10957-022-02114-y
- Leithäuser, N., Adelhütte, D., Braun, K., Büsing, C., Comis, M., Gersing, T.,... Wrede, S. (2022). Decision-Support Systems for Ambulatory Care, including Pandemic Requirements: Using Mathematically Optimized Solutions. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-022-01866-x
2021
- Adelhütte, D., & Liers, F. (2021). Γ–counterparts for robust nonlinear combinatorial and discrete optimization.
- Antunovic, A., Amiri, E., Biefel, C., Steinbock, G., Aulin, J., Liers, F., & Gerstacker, W. (2021). Dynamic Sectorization in Multi-Panel Massive MIMO Systems. In Proceedings of the 2021 IEEE 4th 5G World Forum.
- Dienstbier, J., Aigner, K.-M., Rolfes, J., Peukert, W., Segets, D., Pflug, L., & Liers, F. (2021). Robust Optimization in Nanoparticle Technology: A Proof of Principle by Quantum Dot Growth in a Residence Time Reactor. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2021.107618
- Glomb, L., Hoch, B., Liers, F., & Rösel, F. (2021). Network planning and routing problems over time: Models, complexity and algorithms. In Petra Mutzel, Rasmus Pagh, Grzegorz Herman (Eds.), Leibniz International Proceedings in Informatics, LIPIcs. Lisbon, PT: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.
- Glomb, L., Liers, F., & Rösel, F. (2021). A rolling-horizon approach for multi-period optimization. European Journal of Operational Research, unbekannt(unbekannt). https://doi.org/10.1016/j.ejor.2021.07.043
- Liers, F., Martin, A., Merkert, M., Mertens, N., & Michaels, D. (2021). Solving mixed-integer nonlinear optimization problems using simultaneous convexification: a case study for gas networks. Journal of Global Optimization. https://doi.org/10.1007/s10898-020-00974-0
- Liers, F., Schewe, L., & Thürauf, J. (2021). Radius of Robust Feasibility for Mixed-Integer Problems. Informs Journal on Computing. https://doi.org/10.1287/ijoc.2020.1030
- Roeder, G., Ott, L., Maier, A., Wunder, B., Wienzek, P., Bärmann, A.,... Schellenberger, M. (2021). Analysis and Improvement of LVDC-Grid Stability using Circuit Simulation and Machine Learning - A Case Study. In Proceedings of the NEIS 2021 Conference on Sustainable Energy Supply and Energy Storage Systems. Hamburg.
2020
- Adelhütte, D., Aßmann, D., Gonzàlez Grandòn, T., Gugat, M., Heitsch, H., Liers, F.,... Wintergerst, D. (2020). Joint model of probabilistic/robust (probust) constraints with application to gas network optimization. Vietnam Journal of Mathematics. https://doi.org/10.1007/s10013-020-00434-y
- Ambrosius, M., Grimm, V., Kleinert, T., Liers, F., Schmidt, M., & Zöttl, G. (2020). Endogenous Price Zones and Investment Incentives in Electricity Markets: An Application of Multilevel Optimization with Graph Partitioning. Energy Economics. https://doi.org/10.1016/j.eneco.2020.104879
- Dienstbier, J., Schmölder, J., Burlacu, R., Liers, F., & Kaspereit, M. (2020). Global optimization of batch and steady-state recycling chromatography based on the equilibrium model. Computers & Chemical Engineering, 135, 106687. https://doi.org/10.1016/j.compchemeng.2019.106687
- Hupp, L., Kapolke, M., Liers, F., Martin, A., & Weismantel, R. (2020). Mixed-Integer Reformulations of Resource-Constrained Two-Stage Assignment Problems.
- Liers, F., Martin, A., Merkert, M., Mertens, N., & Michaels, D. (2020). Towards the Solution of Mixed-Integer Nonlinear Optimization Problems using Simultaneous Convexification.
News and Recent Accomplishments:
- Backs the Women in Datascience Conference‘s Student Organizing Committee
Exploring Digital Twin Applications in Accelerating PV Material Research
A Digital Twin to Overcome Long-time Challenges in Photovoltaics
FAU Solar members in the DFG review board
Video on Teaming-up across Disciplines @FAU Solar: A Dive into Photovoltaic Data Analysis
A Positive Semidefinite Safe Approximation of Multivariate Distributionally Robust Constraints Determined by Simple Functions
A Safe Approximation Based on Mixed-Integer Optimization for Non-Convex Distributional Robustness Governed by Univariate Indicator Functions
Women in Optimization (09. – 12. April 2024)
A Digital Twin to overcome long-time challenges in Photovoltaics
