Fr. 89.00

Flexible and Generalized Uncertainty Optimization - Theory and Approaches

Anglais · Livre de poche

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Description

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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.

Table des matières

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. 

A propos de l'auteur










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.


Détails du produit

Auteurs Weldon A Lodwick, Weldon A. Lodwick, Luiz L Salles-Neto, Luiz L. Salles-Neto
Edition Springer, Berlin
 
Langues Anglais
Format d'édition Livre de poche
Sortie 27.01.2022
 
EAN 9783030611828
ISBN 978-3-0-3061182-8
Pages 193
Dimensions 155 mm x 11 mm x 235 mm
Illustrations IX, 193 p. 34 illus., 30 illus. in color.
Thème Studies in Computational Intelligence
Catégorie Sciences naturelles, médecine, informatique, technique > Technique > Général, dictionnaires

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