Neu Neuware; original eingeschweisst; Rechnung mit MwSt.; new item, still sealed; -Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications. 248 pp. Englisch.

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Detalles bibliogr�ficos

T�tulo

Stochastic Decomposition: A Statistical Method for Large Scale Stochastic Linear Programming (Nonconvex Optimization and Its Applications)

Editor

Springer

Fecha de publicaci�n

2013

Idioma

Ingl�s

ISBN 10

1461368456

ISBN 13

9781461368458

Encuadernaci�n

Taschenbuch

Estado

Neu

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Sinopsis

Motivation Stochastic Linear Programming with recourse represents one of the more widely applicable models for incorporating uncertainty within in which the SLP optimization models. There are several arenas model is appropriate, and such models have found applications in air� line yield management, capacity planning, electric power generation planning, financial planning, logistics, telecommunications network planning, and many more. In some of these applications, modelers represent uncertainty in terms of only a few seenarios and formulate a large scale linear program which is then solved using LP software. However, there are many applications, such as the telecommunications planning problem discussed in this book, where a handful of seenarios do not capture variability well enough to provide a reasonable model of the actual decision-making problem. Problems of this type easily exceed the capabilities of LP software by several orders of magnitude. Their solution requires the use of algorithmic methods that exploit the structure of the SLP model in a manner that will accommodate large scale applications.

Cr�ticas

`... can be highly recommended to anyone who wants to understand what stochastic decomposition is all about ...'
Mathematical Reviews, 98d

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