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Flexible and generalized uncertainty optimization = theory and methods /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Flexible and generalized uncertainty optimization/ by Weldon A. Lodwick, Phantipa Thipwiwatpotjana.
Reminder of title:	theory and methods /
Author:	Lodwick, Weldon A.
other author:	Thipwiwatpotjana, Phantipa.
Published:	Cham :Springer International Publishing : : 2017.,
Description:	x, 190 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Mathematical optimization. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-51107-8
ISBN:	9783319511078

Flexible and generalized uncertainty optimization = theory and methods /
Lodwick, Weldon A.

Flexible and generalized uncertainty optimizationtheory and methods /[electronic resource] :by Weldon A. Lodwick, Phantipa Thipwiwatpotjana. - Cham :Springer International Publishing :2017. - x, 190 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.6961860-949X ;. - Studies in computational intelligence ;v. 50. .

1 An Introduction to Generalized Uncertainty Optimization -- 2 Generalized Uncertainty Theory: A Language for Information Deficiency -- 3 The Construction of Flexible and Generalized Uncertainty Optimization Input Data -- 4 An Overview of Flexible and Generalized Uncertainty Optimization -- 5 Flexible Optimization -- 6 Generalized Uncertainty Optimization -- References.

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 that 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 such a model in detail. All in all, the book provides the readers with the necessary background to understand flexible and generalized uncertainty optimization and develop their own optimization model.

ISBN: 9783319511078

Standard No.: 10.1007/978-3-319-51107-8doiSubjects--Topical Terms:

527675
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Flexible and generalized uncertainty optimization = theory and methods /
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245 1 0 $a Flexible and generalized uncertainty optimization $h [electronic resource] : $b theory and methods / $c by Weldon A. Lodwick, Phantipa Thipwiwatpotjana.
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505 0 $a 1 An Introduction to Generalized Uncertainty Optimization -- 2 Generalized Uncertainty Theory: A Language for Information Deficiency -- 3 The Construction of Flexible and Generalized Uncertainty Optimization Input Data -- 4 An Overview of Flexible and Generalized Uncertainty Optimization -- 5 Flexible Optimization -- 6 Generalized Uncertainty Optimization -- References.
520 $a 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 that 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 such a model in detail. All in all, the book provides the readers with the necessary background to understand flexible and generalized uncertainty optimization and develop their own optimization model.
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