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Sinopsis
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.
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Acerca del autor
Weldon Alexander Lodwick is a Full Professor of Mathematics at the University of Colorado Denver. He holds a Ph.D. degree in mathematics (1980) from the Oregon State University. He is the co-editor of the book Fuzzy Optimization: Recent Developments and Applications, Studies in Fuzziness and Soft Computing Vol. 254, Springer-Verlag Berlin Heidelberg, 2010, and the author of the book Interval and Fuzzy Analysis: A Unified Approach in Advances in Imaging and Electronic Physics, Vol. 148, pp. 76–192, Elsevier, 2007. His current research interests include interval analysis, distance geometry, as well as flexible and generalized uncertainty optimization. Over the last thirty years he has taught applied mathematical modeling to undergraduate and graduate students, which covers topics such as radiation therapy of tumor, fuzzy and possibilistic optimization modeling, global optimization, optimal control, molecular distance geometry problems, and neural networks applied to control problems.
Luiz L. Salles-Neto received the M.Sc. degree in mathematics and the Ph.D. degree in computational and applied mathematics from the University of Campinas, Brazil, in 2000 and 2005, respectively. He was a Research Scholar at the Universidad de Sevilla, Spain, in 2009/2010, and a Research Scholar at the University of Colorado Denver, USA, in 2017. He is an Associate Professor at Federal University of São Paulo, Brazil.
De la contraportada
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.
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- EditorialSpringer
- Año de publicación2022
- ISBN 10 3030611825
- ISBN 13 9783030611828
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de edición2
- Número de páginas204
- Contacto del fabricanteno disponible
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Flexible and Generalized Uncertainty Optimization: Theory and Approaches (Studies in Computational Intelligence)
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Flexible and Generalized Uncertainty Optimization
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ISBN 10: 3030611825
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents the theory and methods of flexible and generalized uncertainty optimization.This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncert. Nº de ref. del artículo: 540229119
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Flexible and Generalized Uncertainty Optimization : Theory and Approaches
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 ofthe associated optimizationmodel 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 examplesand a section on interval multi-objective mini-max regret theory along with its solution method. Nº de ref. del artículo: 9783030611828
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