Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Thesis › Doctoral Thesis
Algorithms and concepts for robust optimization. / Goerigk, Marc.
Göttingen : Georg-August-Universität Göttingen, 2013. 213 p.Research output: Thesis › Doctoral Thesis
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TY - THES
T1 - Algorithms and concepts for robust optimization
AU - Goerigk, Marc
PY - 2013/1/14
Y1 - 2013/1/14
N2 - In this work we consider uncertain optimization problems where no probability distribution is known. We introduce the approaches RecFeas and RecOpt to such a robust optimization problem, using a location theoretic point of view, and discuss both theoretical and algorithmic aspects. We then consider both continuous and discrete problem applications of robust optimization: Linear programs from the Netlib benchmark set, and the aperiodic timetabling problem on the continuous side; intermodal load planning, Steiner trees, periodic timetabling, and timetable information on the discrete side. Finally, we present the software library ROPI as a framework for robust optimization with support for most established mixed-integer programming solvers.
AB - In this work we consider uncertain optimization problems where no probability distribution is known. We introduce the approaches RecFeas and RecOpt to such a robust optimization problem, using a location theoretic point of view, and discuss both theoretical and algorithmic aspects. We then consider both continuous and discrete problem applications of robust optimization: Linear programs from the Netlib benchmark set, and the aperiodic timetabling problem on the continuous side; intermodal load planning, Steiner trees, periodic timetabling, and timetable information on the discrete side. Finally, we present the software library ROPI as a framework for robust optimization with support for most established mixed-integer programming solvers.
M3 - Doctoral Thesis
PB - Georg-August-Universität Göttingen
CY - Göttingen
ER -