000			04111nam a22006015i 4500
001			978-3-319-51107-8
003			DE-He213
005			20210118125308.0
007			cr nn 008mamaa
008			170119s2017 gw | 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
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, _x1860-949X ; _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.
650		0	_aOperations research.
650		0	_aManagement science.
650		0	_aProbabilities.
650	1	4	_aComputational Intelligence. _0http://scigraph.springernature.com/things/product-market-codes/T11014
650	2	4	_aOperations Research, Management Science. _0http://scigraph.springernature.com/things/product-market-codes/M26024
650	2	4	_aProbability Theory and Stochastic Processes. _0http://scigraph.springernature.com/things/product-market-codes/M27004
700	1		_aThipwiwatpotjana, Phantipa. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319511054
776	0	8	_iPrinted edition: _z9783319511061
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v696
856	4	0	_uhttps://doi.org/10.1007/978-3-319-51107-8
912			_aZDB-2-ENG
999			_c446913 _d446913
942			_cEB
506			_aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506			_fOnline access from local network of NaUOA.
506			_fOnline access with authorization at https://link.springer.com/
506			_fОнлайн-доступ з локальної мережі НаУОА.
506			_fОнлайн доступ з авторизацією на https://link.springer.com/