TY - JOUR A1 - Bärmann, Andreas A1 - Heidt, Andreas A1 - Martin, Alex A1 - er, A1 - Pokutta, Sebastian A1 - Thurner, Christoph T1 - Polyhedral Approximation of Ellipsoidal Uncertainty Sets via Extended Formulations - a computational case study JF - Computational Management Science Y1 - 2016 UR - http://www.optimization-online.org/DB_HTML/2014/02/4245.html N1 - Additional Note: http://link.springer.com/article/10.1007/s10287-015-0243-0 DOI:10.1007/s10287-015-0243-0 N1 - URL of the PDF: http://link.springer.com/article/10.1007/s10287-015-0243-0 VL - 13 IS - 2 SP - 151 EP - 193 ER - TY - CHAP A1 - Bärmann, Andreas A1 - Pokutta, Sebastian A1 - Schneider, Oskar T1 - Emulating the Expert: Inverse Optimization through Online Learning T2 - Proceedings of the International Conference on Machine Learning (ICML) Y1 - 2017 N1 - URL of the PDF: http://proceedings.mlr.press/v70/barmann17a.html N1 - URL of the Poster: https://app.box.com/s/h3ychkv99il7oyzw7sym8la5lhit3elg N1 - URL of the Slides: https://app.box.com/s/7ti8sz8mf5s2znn1qo32in4jxy5gjnn1 N1 - URL of the Abstract: http://www.pokutta.com/blog/research/2018/11/25/expertLearning-abstract.html ER - TY - BOOK A1 - Abbink, Erwin A1 - Bärmann, Andreas A1 - Bešinovic, Nikola A1 - Bohlin, Markus A1 - Cacchiani, Valentina A1 - Caimi, Gabrio A1 - de Fabris, Stefano A1 - Dollevoet, Twan A1 - Fischer, Frank A1 - Fügenschuh, Armin A1 - Galli, Laura A1 - Goverde, Rob M.P. A1 - Hansmann, Ronny A1 - Homfeld, Henning A1 - Huisman, Dennis A1 - Johann, Marc A1 - Klug, Torsten A1 - Törnquist Krasemann, Johanna A1 - Kroon, Leo A1 - Lamorgese, Leonardo A1 - Liers, Frauke A1 - Mannino, Carlo A1 - Medeossi, Giorgio A1 - Pacciarelli, Dario A1 - Reuther, Markus A1 - Schlechte, Thomas A1 - Schmidt, Marie A1 - Schöbel, Anita A1 - Schülldorf, Hanno A1 - Stieber, Anke A1 - Stiller, Sebastian A1 - Toth, Paolo A1 - Zimmermann, Uwe ED - Borndörfer, Ralf ED - Klug, Torsten ED - Lamorgese, Leonardo ED - Mannino, Carlo ED - Reuther, Markus ED - Schlechte, Thomas T1 - Handbook of Optimization in the Railway Industry N2 - This book promotes the use of mathematical optimization and operations research methods in rail transportation. The editors assembled thirteen contributions from leading scholars to present a unified voice, standardize terminology, and assess the state-of-the-art. There are three main clusters of articles, corresponding to the classical stages of the planning process: strategic, tactical, and operational. These three clusters are further subdivided into five parts which correspond to the main phases of the railway network planning process: network assessment, capacity planning, timetabling, resource planning, and operational planning. Individual chapters cover: Simulation Capacity Assessment Network Design Train Routing Robust Timetabling Event Scheduling Track Allocation Blocking Shunting Rolling Stock Crew Scheduling Dispatching Delay Propagation Y1 - 2018 SN - 978-3-319-72152-1 U6 - https://doi.org/10.1007/978-3-319-72153-8 VL - 268 PB - Springer Verlag ER - TY - JOUR A1 - Bärmann, Andreas A1 - Martin, Alexander A1 - Pokutta, Sebastian A1 - Schneider, Oskar T1 - An Online-Learning Approach to Inverse Optimization Y1 - 2018 ER - TY - JOUR A1 - Kevin-Martin, Aigner A1 - Bärmann, Andreas A1 - Braun, Kristin A1 - Liers, Frauke A1 - Pokutta, Sebastian A1 - Schneider, Oskar A1 - Sharma, Kartikey A1 - Tschuppik, Sebastian T1 - Data-driven Distributionally Robust Optimization over Time JF - INFORMS Journal on Optimization N2 - Stochastic optimization (SO) is a classical approach for optimization under uncertainty that typically requires knowledge about the probability distribution of uncertain parameters. Because the latter is often unknown, distributionally robust optimization (DRO) provides a strong alternative that determines the best guaranteed solution over a set of distributions (ambiguity set). In this work, we present an approach for DRO over time that uses online learning and scenario observations arriving as a data stream to learn more about the uncertainty. Our robust solutions adapt over time and reduce the cost of protection with shrinking ambiguity. For various kinds of ambiguity sets, the robust solutions converge to the SO solution. Our algorithm achieves the optimization and learning goals without solving the DRO problem exactly at any step. We also provide a regret bound for the quality of the online strategy that converges at a rate of O(log T/T−−√), where T is the number of iterations. Furthermore, we illustrate the effectiveness of our procedure by numerical experiments on mixed-integer optimization instances from popular benchmark libraries and give practical examples stemming from telecommunications and routing. Our algorithm is able to solve the DRO over time problem significantly faster than standard reformulations. Y1 - 2023 U6 - https://doi.org/10.1287/ijoo.2023.0091 VL - 5 IS - 4 SP - 376 EP - 394 ER -