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Decomposition Methods for Complex Factory Scheduling Problems, Irfan M. Ovacik; Reha Uzsoy


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Автор: Irfan M. Ovacik; Reha Uzsoy
Название:  Decomposition Methods for Complex Factory Scheduling Problems
ISBN: 9781461379065
Издательство: Springer
Классификация:



ISBN-10: 1461379067
Обложка/Формат: Paperback
Страницы: 215
Вес: 0.33 кг.
Дата издания: 29.10.2012
Язык: English
Размер: 234 x 156 x 12
Основная тема: Business and Management
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: The factory scheduling problem, that of allocating machines to competing jobs in manufacturing facilities to optimize or at least improve system performance, is encountered in many different manufacturing environments.


Solving Network Design Problems via Decomposition, Aggregation and Approximation

Автор: B?rmann
Название: Solving Network Design Problems via Decomposition, Aggregation and Approximation
ISBN: 3658139129 ISBN-13(EAN): 9783658139124
Издательство: Springer
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Цена: 9141.00 р.
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Описание:

Andreas B?rmann 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.
Domain Decomposition Methods in Science and Engineering XVIII

Автор: Michel Bercovier; Martin Gander; Ralf Kornhuber; O
Название: Domain Decomposition Methods in Science and Engineering XVIII
ISBN: 364226025X ISBN-13(EAN): 9783642260254
Издательство: Springer
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Цена: 25155.00 р.
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Описание: th This volume contains a selection of 41 refereed papers presented at the 18 International Conference of Domain Decomposition Methods hosted by the School of ComputerScience and Engineering(CSE) of the Hebrew Universityof Jerusalem, Israel, January 12-17, 2008.

Domain Decomposition Methods in Science and Engineering XX

Автор: Bank Randolph
Название: Domain Decomposition Methods in Science and Engineering XX
ISBN: 364235274X ISBN-13(EAN): 9783642352744
Издательство: Springer
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Цена: 23058.00 р.
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Описание: Domain decomposition methods are iterative methods for solving the often very large linearor nonlinear systems of algebraic equations that arise when various problems in continuum mechanics are discretized using finite elements.

Domain Decomposition Methods in Science and Engineering XXI

Автор: Jocelyne Erhel; Martin J. Gander; Laurence Halpern
Название: Domain Decomposition Methods in Science and Engineering XXI
ISBN: 331905788X ISBN-13(EAN): 9783319057880
Издательство: Springer
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Цена: 23757.00 р.
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Описание: This volume contains a selection of papers presented at the 21st international conference on domain decomposition methods in science and engineering held in Rennes, France, June 25-29, 2012.

Decomposition Techniques in Mathematical Programming

Автор: Antonio J. Conejo; Enrique Castillo; Roberto Mingu
Название: Decomposition Techniques in Mathematical Programming
ISBN: 3642066070 ISBN-13(EAN): 9783642066078
Издательство: Springer
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Цена: 22201.00 р.
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Описание: Optimization plainly dominates the design, planning, operation, and c- trol of engineering systems. This is a book on optimization that considers particular cases of optimization problems, those with a decomposable str- ture that can be advantageously exploited. Those decomposable optimization problems are ubiquitous in engineering and science applications. The book considers problems with both complicating constraints and complicating va- ables, and analyzes linear and nonlinear problems, with and without in- ger variables. The decomposition techniques analyzed include Dantzig-Wolfe, Benders, Lagrangian relaxation, Augmented Lagrangian decomposition, and others. Heuristic techniques are also considered. Additionally, a comprehensive sensitivity analysis for characterizing the solution of optimization problems is carried out. This material is particularly novel and of high practical interest. This book is built based on many clarifying, illustrative, and compu- tional examples, which facilitate the learning procedure. For the sake of cl- ity, theoretical concepts and computational algorithms are assembled based on these examples. The results are simplicity, clarity, and easy-learning. We feel that this book is needed by the engineering community that has to tackle complex optimization problems, particularly by practitioners and researchersinEngineering, OperationsResearch, andAppliedEconomics.The descriptions of most decomposition techniques are available only in complex and specialized mathematical journals, di?cult to understand by engineers. A book describing a wide range of decomposition techniques, emphasizing problem-solving, and appropriately blending theory and application, was not previously available.

Stochastic Decomposition

Автор: Julia L. Higle; S. Sen
Название: Stochastic Decomposition
ISBN: 0792338405 ISBN-13(EAN): 9780792338406
Издательство: Springer
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Цена: 20257.00 р.
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Описание: Summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. This title is suitable for researchers in mathematical optimization, including those working in telecommunications and electric power generation.

DECOMP: an Implementation of Dantzig-Wolfe Decomposition for Linear Programming

Автор: James K. Ho; Rangaraja P. Sundarraj
Название: DECOMP: an Implementation of Dantzig-Wolfe Decomposition for Linear Programming
ISBN: 0387971548 ISBN-13(EAN): 9780387971544
Издательство: Springer
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Цена: 12157.00 р.
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Описание: For linear optimization models that can be formulated as linear programs with the block-angular structure, i.e. It can also be used as supplemental material in a second course in linear programming, computational mathematical programming, or large-scale systems.

Nonlinear Mode Decomposition

Автор: Dmytro Iatsenko
Название: Nonlinear Mode Decomposition
ISBN: 331938712X ISBN-13(EAN): 9783319387123
Издательство: Springer
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Цена: 13059.00 р.
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Описание: This work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random fluctuations or noise.

Stochastic Decomposition

Автор: Julia L. Higle; S. Sen
Название: Stochastic Decomposition
ISBN: 1461368456 ISBN-13(EAN): 9781461368458
Издательство: Springer
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Цена: 13974.00 р.
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Описание: Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models.

Domain Decomposition Methods in Science and Engineering XXI

Автор: Jocelyne Erhel; Martin J. Gander; Laurence Halpern
Название: Domain Decomposition Methods in Science and Engineering XXI
ISBN: 3319355481 ISBN-13(EAN): 9783319355481
Издательство: Springer
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Цена: 23757.00 р.
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Описание: This volume contains a selection of papers presented at the 21st international conference on domain decomposition methods in science and engineering held in Rennes, France, June 25-29, 2012.

Nonlinear Mode Decomposition

Автор: Dmytro Iatsenko
Название: Nonlinear Mode Decomposition
ISBN: 3319200151 ISBN-13(EAN): 9783319200156
Издательство: Springer
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Цена: 14365.00 р.
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Описание: This work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random fluctuations or noise.


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