000			03226cam a2200409Ii 4500
001			95276
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005			20230102112655.0
006			m o d
007			cr cnu|||unuuu
008			170127s2017 sz a ob 000 0 eng d
020			_a3319511076 _q(electronic bk.)
020			_a9783319511078 _q(electronic bk.)
020			_z331951105X
020			_z9783319511054 _q(print)
035			_a(OCoLC)970394110 _z(OCoLC)971522918 _z(OCoLC)971588163 _z(OCoLC)971960901 _z(OCoLC)974650886 _z(OCoLC)981114134 _z(OCoLC)981814075 _z(OCoLC)1005777197 _z(OCoLC)1011791987
040			_aGW5XE _cGW5XE _dYDX _dOCLCF _dUAB _dNJR _dUPM _dVT2 _dUWO _dIOG _dESU _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA402.5 _bL639 2017 EB
100	1		_aLodwick, Weldon A., _eautor
245	1	0	_aFlexible and generalized uncertainty optimization : _btheory and methods _cWeldon A. Lodwick, Phantipa Thipwiwatpotjana.
264		1	_aCham, Switzerland _bSpringer _c2017
300			_a1 recurso en línea (x, 190 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aStudies in computational intelligence _x1860-949X _vvolume 696
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
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	3		_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		7	_aConjuntos difusos _2embne _0(OCoLC)fst00936812 _0 _9145903
700	1		_aThipwiwatpotjana, Phantipa, _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-51107-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017B
998			_b02/2018 _dz _e- _zSI
999			_c95276 _d95276 _x1