00912nam0-22003131--450 99000829807040332120181023104254.0000829807FED01000829807(Aleph)000829807FED0120060320d1963----km-y0itay50------baengUSy-------001yyDomestic airline efficiencyan application of linear programmingRonald E. MillerCambridge ( Mass.)The MIT Press1963XIV, 174 p.24 cm<<The >>regional science studies series533011 rid.itaMiller,Ronald E.88802ITUNINARICAUNIMARCBK990008298070403321XV I 18875298FGBCO/19343DINTRFGBCDINTRDomestic airline efficiency746423UNINA03384oam 2200517 450 991048364120332120210530212830.0981-15-8546-610.1007/978-981-15-8546-3(CKB)4100000011645266(DE-He213)978-981-15-8546-3(MiAaPQ)EBC6422733(PPN)252512596(EXLCZ)99410000001164526620210530d2021 uy 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierNonlinear interval optimization for uncertain problems /Chao Jiang, Xu Han, Huichao Xie1st ed. 2021.Singapore :Springer,[2021]©20211 online resource (XII, 284 p. 103 illus., 58 illus. in color.) Springer Tracts in Mechanical Engineering981-15-8545-8 Includes bibliographical references.Introduction -- Fundamentals of interval number theory -- Mathematical transformation models of nonlinear interval optimization -- Interval optimization based on hybrid optimization algorithms -- Interval optimization based on interval structural analysis -- Interval optimization based on sequential linear programming -- Interval optimization based on surrogate models -- Interval multidisciplinary optimization design -- Interval optimization based on a novel interval possibility degree model -- Interval optimization considering parameter dependences -- Interval multi-objective optimization design -- Interval optimization considering tolerance design -- Interval differential evolution algorithm.This book systematically discusses nonlinear interval optimization design theory and methods. Firstly, adopting a mathematical programming theory perspective, it develops an innovative mathematical transformation model to deal with general nonlinear interval uncertain optimization problems, which is able to equivalently convert complex interval uncertain optimization problems to simple deterministic optimization problems. This model is then used as the basis for various interval uncertain optimization algorithms for engineering applications, which address the low efficiency caused by double-layer nested optimization. Further, the book extends the nonlinear interval optimization theory to design problems associated with multiple optimization objectives, multiple disciplines, and parameter dependence, and establishes the corresponding interval optimization models and solution algorithms. Lastly, it uses the proposed interval uncertain optimization models and methods to deal with practical problems in mechanical engineering and related fields, demonstrating the effectiveness of the models and methods.Springer tracts in mechanical engineering.Mathematical optimizationAerospace engineeringMathematical optimization.Aerospace engineering.519.3Jiang Chao1228285Han XuXie HuichaoMiAaPQMiAaPQUtOrBLWBOOK9910483641203321Nonlinear interval optimization for uncertain problems2851535UNINA