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Solving Network Design Problems via Decomposition, Aggregation and Approximation
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...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
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Wiesbaden :
Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum,
2016.
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| Edición: | 1st ed. 2016. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Solving Network Design Problems via Decomposition, Aggregation and Approximation |h [electronic resource] / |c by Andreas Bärmann. |
| 250 | |a 1st ed. 2016. | ||
| 264 | 1 | |a Wiesbaden : |b Springer Fachmedien Wiesbaden : |b Imprint: Springer Spektrum, |c 2016. | |
| 300 | |a XV, 203 p. 32 illus., 28 illus. in color. |b online resource. | ||
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| 505 | 0 | |a Decomposition for Multi-Period Network Design -- Solving Network Design Problems via Aggregation -- Approximate Second-Order Cone Robust Optimization. | |
| 520 | |a 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. Contents Decomposition for Multi-Period Network Design Solving Network Design Problems via Aggregation Approximate Second-Order Cone Robust Optimization Target Groups Researchers, teachers and students in mathematical optimization and operations research Network planners in the field of logistics and beyond < About the Author 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. | ||
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Operations research. | |
| 650 | 0 | |a Business logistics. | |
| 650 | 1 | 4 | |a Optimization. |
| 650 | 2 | 4 | |a Operations Research and Decision Theory. |
| 650 | 2 | 4 | |a Logistics. |
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| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783658139124 |
| 776 | 0 | 8 | |i Printed edition: |z 9783658139148 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-658-13913-1 |z Texto Completo |
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