Overview
- Unifies both fuzzy and possibilistic optimization
- Shows how to construct input data for use in flexible and generalized uncertainty optimization problems
- Presents practical, theoretical and historical approaches to flexible and generalized uncertainty optimization
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 696)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
Phantipa Thipwiwatpotjana is an Assistant Professor of Mathematics at the Chulalongkorn University, Bangkok, Thailand. She received her Ph. D. in Applied Mathematics from the University of Colorado Denver in 2010 for the dissertation titled “Linear programming problems for generalized uncertainty”. She received scholarships from the Development and Promotion of Science and Technology Talents Project and Thai Government to study Mathematics for both undergraduate and graduate levels. Her primary research interests are in optimization under uncertainty, uncertainty relationship, and their applications.
Bibliographic Information
Book Title: Flexible and Generalized Uncertainty Optimization
Book Subtitle: Theory and Methods
Authors: Weldon A. Lodwick, Phantipa Thipwiwatpotjana
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-51107-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG 2017
eBook ISBN: 978-3-319-51107-8Published: 17 January 2017
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: X, 190
Number of Illustrations: 16 b/w illustrations, 16 illustrations in colour
Topics: Computational Intelligence, Operations Research, Management Science, Probability Theory and Stochastic Processes