Home > Research > Publications & Outputs > Algorithms and concepts for robust optimization

Links

View graph of relations

Algorithms and concepts for robust optimization

Research output: ThesisDoctoral Thesis

Published

Standard

Algorithms and concepts for robust optimization. / Goerigk, Marc.
Göttingen: Georg-August-Universität Göttingen, 2013. 213 p.

Research output: ThesisDoctoral Thesis

Harvard

APA

Goerigk, M. (2013). Algorithms and concepts for robust optimization. [Doctoral Thesis, Georg-August Universität Göttingen]. Georg-August-Universität Göttingen.

Vancouver

Goerigk M. Algorithms and concepts for robust optimization. Göttingen: Georg-August-Universität Göttingen, 2013. 213 p.

Author

Goerigk, Marc. / Algorithms and concepts for robust optimization. Göttingen : Georg-August-Universität Göttingen, 2013. 213 p.

Bibtex

@phdthesis{c444906d52de478f8e6b0bcf79409c74,
title = "Algorithms and concepts for robust optimization",
abstract = "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.",
author = "Marc Goerigk",
year = "2013",
month = jan,
day = "14",
language = "English",
publisher = "Georg-August-Universit{\"a}t G{\"o}ttingen",
school = "Georg-August Universit{\"a}t G{\"o}ttingen",

}

RIS

TY - BOOK

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 -