Solving Network Design Problems via Decomposition, Aggregation and Approximation

Buch | Softcover
XV, 203 Seiten
2016 | 1st ed. 2016
Springer Fachmedien Wiesbaden GmbH (Verlag)
978-3-658-13912-4 (ISBN)

Lese- und Medienproben

Solving Network Design Problems via Decomposition, Aggregation and Approximation - Andreas Bärmann
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Andreas Bärmann develops novel approaches for the solution of network design problems as they arise in various contexts of applied optimization. At the example of an optimal expansion of the German railway network until 2030, the author derives a tailor-made decomposition technique for multi-period network design problems. Next, he develops a general framework for the solution of network design problems via aggregation of the underlying graph structure. This approach is shown to save much computation time as compared to standard techniques. Finally, the author devises a modelling framework for the approximation of the robust counterpart under ellipsoidal uncertainty, an often-studied case in the literature. Each of these three approaches opens up a fascinating branch of research which promises a better theoretical understanding of the problem and an increasing range of solvable application settings at the same time.

Dr. Andreas Bärmann is currently working as a postdoctoral researcher at the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) at the chair of Economics, Discrete Optimization and Mathematics. His research is focussed on mathematical optimization, especially the optimization of logistic processes.

Decomposition for Multi-Period Network Design.- Solving Network Design Problems via Aggregation.- Approximate Second-Order Cone Robust Optimization.

Erscheinungsdatum
Zusatzinfo XV, 203 p. 32 illus., 28 illus. in color.
Verlagsort Wiesbaden
Sprache englisch
Maße 168 x 240 mm
Gewicht 398 g
Themenwelt Mathematik / Informatik Mathematik Angewandte Mathematik
Schlagworte Aggregation • Decomposition • Discrete Optimization • logistics • mathematics and statistics • network design • Operation Research/Decision Theory • Optimization • robust optimization
ISBN-10 3-658-13912-9 / 3658139129
ISBN-13 978-3-658-13912-4 / 9783658139124
Zustand Neuware
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