000			03432nam a22004575i 4500
999			_c330785 _d330785 _x1
001			330785
003			ES-MaUEC
005			20230102114615.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			210112s2021 gw | s |||| 0|eng d
020			_a9783030611804
024	7		_a10.1007/978-3-030-61180-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _erda _dES-MaUEC
050		4	_aQA402.5 _b2021 EB
100	1		_aLodwick, Weldon A. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678156
245	1	0	_aFlexible and Generalized Uncertainty Optimization : _bTheory and Approaches _cby Weldon A. Lodwick, Luiz L. Salles-Neto.
250			_aSecond edition 2021.
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (IX, 193 páginas) _b34 ilustraciones, 30 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2
490	0		_aStudies in Computational Intelligence _x1860-949X _v696
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aAn Introduction to Generalized Uncertainty Optimization -- Generalized Uncertainty Theory: A Language for Information Deficiency -- The Construction of Flexible and Generalized Uncertainty Optimization Input Data -- An Overview of Flexible and Generalized Uncertainty Optimization -- Flexible Optimization -- 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 are 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 the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.
988			_aSpringer_Robotics_2021
650		7	_aOptimización matemática _2embne _9145705
650		7	_aIncertidumbre (Teoría de la información) _2embne _9667868
650		7	_aConjuntos difusos _2embne _9145903
700	1		_aSalles-Neto, Luiz L _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678157
776	0	8	_iPrinted edition: _z9783030611798
776	0	8	_iPrinted edition: _z9783030611811
776	0	8	_iPrinted edition: _z9783030611828
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-61180-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b04/2021 _dz _eo _zSI