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Idioma: Inglés
Publicado por Spektrum Akademischer Verlag Gmbh, 2016
ISBN 10: 3658139129 ISBN 13: 9783658139124
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Publicado por Springer Fachmedien Wiesbaden, 2016
ISBN 10: 3658139129 ISBN 13: 9783658139124
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
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Añadir al carritoTaschenbuch. Condición: Neu. Solving Network Design Problems via Decomposition, Aggregation and Approximation | Andreas Bärmann | Taschenbuch | xv | Englisch | 2016 | Springer Gabler | EAN 9783658139124 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Publicado por Springer Fachmedien Wiesbaden Jun 2016, 2016
ISBN 10: 3658139129 ISBN 13: 9783658139124
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 220 pp. Englisch.
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Añadir al carritoCondición: New. PRINT ON DEMAND pp. 192.
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Publicado por Springer Fachmedien Wiesbaden, 2016
ISBN 10: 3658139129 ISBN 13: 9783658139124
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Añadir al carritoCondición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A Study in the Field of MathematicsDr. Andreas Baermann is currently working as a postdoctoral researcher at the Friedrich-Alexander-Universitaet Erlangen-Nuernberg (FAU) at the chair of Economics, Discrete Optimization and Mathematics. His research.
Idioma: Inglés
Publicado por Springer Gabler Jun 2016, 2016
ISBN 10: 3658139129 ISBN 13: 9783658139124
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 220 pp. Englisch.