UID:
almahu_9948639813902882
Format:
IX, 193 p. 34 illus., 30 illus. in color.
,
online resource.
Edition:
2nd ed. 2021.
ISBN:
9783030611804
Series Statement:
Studies in Computational Intelligence, 696
Content:
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.
Note:
An Introduction to Generalized Uncertainty Optimization -- Generalized Uncertainty Theory: A Language for Information Deficiency -- The Construction of Flexible and Generalized Uncertainty Optimization Input Data -- An Overview of Flexible and Generalized Uncertainty Optimization -- Flexible Optimization -- Generalized Uncertainty Optimization -- References. .
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030611798
Additional Edition:
Printed edition: ISBN 9783030611811
Additional Edition:
Printed edition: ISBN 9783030611828
Language:
English
DOI:
10.1007/978-3-030-61180-4
URL:
https://doi.org/10.1007/978-3-030-61180-4
URL:
Volltext
(URL des Erstveröffentlichers)
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