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Flexible and Generalized Uncertainty Optimization

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This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.

Availability
Inventory Code Barcode Call Number Location Status
1908000736EB0001316519.6 Lod fCentral Library (OPAC)Available
Detail Information
Series Title
Studies in Computational Intelligence
Call Number
519.6 Lod f
Publisher
Switzerland : Springer Cham.,
Collation
ix, 193p.:Ill
Language
English
ISBN/ISSN
978-3-030-61180-4
Classification
519.6
Content Type
Ebook
Media Type
-
Carrier Type
online resource
Edition
2
Subject(s)
Mathematical optimization
Specific Detail Info
-
Statement of Responsibility
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No other version available

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