000 03449nam a22005535i 4500
001 978-3-319-51107-8
003 DE-He213
005 20220801222155.0
007 cr nn 008mamaa
008 170119s2017 sz | s |||| 0|eng d
020 _a9783319511078
_9978-3-319-51107-8
024 7 _a10.1007/978-3-319-51107-8
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLodwick, Weldon A.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960101
245 1 0 _aFlexible and Generalized Uncertainty Optimization
_h[electronic resource] :
_bTheory and Methods /
_cby Weldon A. Lodwick, Phantipa Thipwiwatpotjana.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aX, 190 p. 32 illus., 16 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v696
505 0 _a1 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. .
520 _aThis 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. .
650 0 _aComputational intelligence.
_97716
650 0 _aOperations research.
_912218
650 0 _aManagement science.
_98316
650 0 _aProbabilities.
_94604
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aOperations Research, Management Science .
_931720
650 2 4 _aProbability Theory.
_917950
700 1 _aThipwiwatpotjana, Phantipa.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_960102
710 2 _aSpringerLink (Online service)
_960103
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319511054
776 0 8 _iPrinted edition:
_z9783319511061
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v696
_960104
856 4 0 _uhttps://doi.org/10.1007/978-3-319-51107-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c80477
_d80477