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Flexible and Generalized Uncertainty Optimization

Theory and Methods

  • Weldon A. Lodwick
  • Phantipa Thipwiwatpotjana

Part of the Studies in Computational Intelligence book series (SCI, volume 696)

Table of contents

  1. Front Matter
    Pages i-x
  2. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 1-35
  3. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 37-69
  4. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 71-108
  5. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 109-134
  6. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 135-156
  7. Weldon A. Lodwick, Phantipa Thipwiwatpotjana
    Pages 157-175
  8. Back Matter
    Pages 177-190

About this book

Introduction

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. 

Keywords

Fuzzy Intervals Possibility Intervals Necessity Measures Interval-valued probabilities Kolmogorov-Smirnov bounds Cumulative probability bounds Random sets Cloudy vector Optimization models Flexible optimization Fuzzy optimization Application of fuzzy intervals

Authors and affiliations

  • Weldon A. Lodwick
    • 1
  • Phantipa Thipwiwatpotjana
    • 2
  1. 1.Department of Mathematical and Statistical SciencesUniversity of Colorado DenverDenverUSA
  2. 2.Department of Mathematics and Computer ScienceChulalongkorn University Faculty of ScienceBangkokThailand

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-51107-8
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-51105-4
  • Online ISBN 978-3-319-51107-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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