LEADER 04253nam 22006735 450 001 9910799219203321 005 20220418224651.0 010 $a3-319-61007-4 024 7 $a10.1007/978-3-319-61007-8 035 $a(CKB)4340000000062061 035 $a(MiAaPQ)EBC4930076 035 $a(DE-He213)978-3-319-61007-8 035 $a(MiAaPQ)EBC5577969 035 $a(Au-PeEL)EBL5577969 035 $a(OCoLC)1021254520 035 $z(PPN)25885510X 035 $a(PPN)203669320 035 $a(EXLCZ)994340000000062061 100 $a20170728d2017 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aNon-convex multi-objective optimization$b[electronic resource] /$fby Panos M. Pardalos, Antanas ?ilinskas, Julius ?ilinskas 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (192 pages) $cillustrations, tables 225 1 $aSpringer Optimization and Its Applications,$x1931-6828 ;$v123 311 $a3-319-61005-8 320 $aIncludes bibliographical references and index. 327 $a1. Definitions and Examples -- 2. Scalarization -- 3. Approximation and Complexity -- 4. A Brief Review of Non-Convex Single-Objective Optimization -- 5. Multi-Objective Branch and Bound -- 6. Worst-Case Optimal Algorithms -- 7. Statistical Models Based Algorithms -- 8. Probabilistic Bounds in Multi-Objective Optimization -- 9. Visualization of a Set of Pareto Optimal Decisions -- 10. Multi-Objective Optimization Aided Visualization of Business Process Diagrams. ?References -- Index. 330 $aRecent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management. . 410 0$aSpringer Optimization and Its Applications,$x1931-6828 ;$v123 606 $aMathematical optimization 606 $aAlgorithms 606 $aComputer science?Mathematics 606 $aComputer mathematics 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aAlgorithms$3https://scigraph.springernature.com/ontologies/product-market-codes/M14018 606 $aMathematical Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M13110 615 0$aMathematical optimization. 615 0$aAlgorithms. 615 0$aComputer science?Mathematics. 615 0$aComputer mathematics. 615 14$aOptimization. 615 24$aAlgorithms. 615 24$aMathematical Applications in Computer Science. 676 $a519.3 700 $aPardalos$b Panos M$4aut$4http://id.loc.gov/vocabulary/relators/aut$0318341 702 $a?ilinskas$b Antanas$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $a?ilinskas$b Julius$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910799219203321 996 $aNon-convex multi-objective optimization$93872650 997 $aUNINA