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020 _a9783319610078
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024 7 _a10.1007/978-3-319-61007-8
_2doi
040 _aISI Library, Kolkata
050 4 _aQA402.5-402.6
072 7 _aPBU
_2bicssc
072 7 _aMAT003000
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072 7 _aPBU
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082 0 4 _a519.6
_223
100 1 _aPardalos, Panos M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aNon-Convex Multi-Objective Optimization
_h[electronic resource] /
_cby Panos M. Pardalos, Antanas Žilinskas, Julius Žilinskas.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXII, 192 p. 18 illus., 4 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v123
505 0 _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.
520 _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. .
650 0 _aMathematical optimization.
650 0 _aAlgorithms.
650 1 4 _aOptimization.
_0http://scigraph.springernature.com/things/product-market-codes/M26008
650 2 4 _aAlgorithms.
_0http://scigraph.springernature.com/things/product-market-codes/M14018
650 2 4 _aMathematical Applications in Computer Science.
_0http://scigraph.springernature.com/things/product-market-codes/M13110
700 1 _aŽilinskas, Antanas.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aŽilinskas, Julius.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319610054
776 0 8 _iPrinted edition:
_z9783319610061
776 0 8 _iPrinted edition:
_z9783319869810
830 0 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v123
856 4 0 _uhttps://doi.org/10.1007/978-3-319-61007-8
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c427213
_d427213