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03579nam a22005295i 4500 |
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978-3-319-51107-8 |
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20210617110017.0 |
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170119s2017 gw | s |||| 0|eng d |
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|a 9783319511078
|9 978-3-319-51107-8
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|a 10.1007/978-3-319-51107-8
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|a Lodwick, Weldon A.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Flexible and Generalized Uncertainty Optimization
|h [electronic resource] :
|b Theory and Methods /
|c by Weldon A. Lodwick, Phantipa Thipwiwatpotjana.
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| 250 |
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|a 1st ed. 2017.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a X, 190 p. 32 illus., 16 illus. in color.
|b online resource.
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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| 490 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 696
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| 505 |
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|a 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. .
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|a 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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|a Computational intelligence.
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|a Operations research.
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|a Management science.
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|a Probabilities.
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|a Computational Intelligence.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/T11014
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|a Operations Research, Management Science.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M26024
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| 650 |
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|a Probability Theory and Stochastic Processes.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/M27004
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| 700 |
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|a Thipwiwatpotjana, Phantipa.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783319511054
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| 776 |
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|i Printed edition:
|z 9783319511061
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| 830 |
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|a Studies in Computational Intelligence,
|x 1860-949X ;
|v 696
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| 856 |
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|u https://doi.org/10.1007/978-3-319-51107-8
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|a ZDB-2-ENG
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|a ZDB-2-SXE
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| 950 |
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|a Engineering (SpringerNature-11647)
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| 950 |
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|a Engineering (R0) (SpringerNature-43712)
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