LEADER 03079oam 2200517 450 001 9910484954903321 005 20210617105120.0 010 $a3-030-61180-9 024 7 $a10.1007/978-3-030-61180-4 035 $a(CKB)4100000011716921 035 $a(MiAaPQ)EBC6455853 035 $a(DE-He213)978-3-030-61180-4 035 $a(PPN)253250730 035 $a(EXLCZ)994100000011716921 100 $a20210617d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFlexible and generalized uncertainty optimization $etheory and approaches /$fWeldon A. Lodwick, Luiz L. Salles-Neto 205 $aSecond edition. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (IX, 193 p. 34 illus., 30 illus. in color.) 225 1 $aStudies in Computational Intelligence ;$vVolume 696 311 $a3-030-61179-5 327 $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. . 330 $aThis book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method. 410 0$aStudies in computational intelligence ;$vVolume 696. 606 $aMathematical optimization 606 $aUncertainty (Information theory) 606 $aFuzzy sets 615 0$aMathematical optimization. 615 0$aUncertainty (Information theory) 615 0$aFuzzy sets. 676 $a519.3 700 $aLodwick$b Weldon A.$0988548 702 $aSalles-Neto$b Luiz L. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484954903321 996 $aFlexible and Generalized Uncertainty Optimization$92260463 997 $aUNINA