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- Author: Andreas Bärmann
- Publisher: Springer Spektrum
- Year: 2016
- Pages: 203
- ISBN-10: 3658139129
- ISBN-13: 9783658139124
- Format: 17 x 24.4 x 1.2 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Solving Network Design Problems Via Decomposition, Aggregation and Approximation (e-book) (used book) | bookbook.eu
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Description
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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- Author: Andreas Bärmann
- Publisher: Springer Spektrum
- Year: 2016
- Pages: 203
- ISBN-10: 3658139129
- ISBN-13: 9783658139124
- Format: 17 x 24.4 x 1.2 cm, softcover
- Language: English English
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.
Reviews