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Flexible and Generalized Uncertainty Optimization [electronic resource] : Theory and Methods / by Weldon A. Lodwick, Phantipa Thipwiwatpotjana.

By: Lodwick, Weldon A [author.].
Contributor(s): Thipwiwatpotjana, Phantipa [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 696Publisher: Cham : Springer International Publishing : Imprint: Springer, 2017Edition: 1st ed. 2017.Description: X, 190 p. 32 illus., 16 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319511078.Subject(s): Computational intelligence | Operations research | Management science | Probabilities | Computational Intelligence | Operations Research, Management Science | Probability TheoryAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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. .
In: Springer Nature eBookSummary: 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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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. .

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