LEADER 04132nam 22005775 450 001 9910299470403321 005 20200703022614.0 010 $a3-319-03398-0 024 7 $a10.1007/978-3-319-03398-3 035 $a(CKB)3710000000089111 035 $a(DE-He213)978-3-319-03398-3 035 $a(SSID)ssj0001186887 035 $a(PQKBManifestationID)11702437 035 $a(PQKBTitleCode)TC0001186887 035 $a(PQKBWorkID)11242102 035 $a(PQKB)10009183 035 $a(MiAaPQ)EBC3092063 035 $a(PPN)176749608 035 $a(EXLCZ)993710000000089111 100 $a20140207d2014 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFuzzy-Like Multiple Objective Multistage Decision Making /$fby Jiuping Xu, Ziqiang Zeng 205 $a1st ed. 2014. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2014. 215 $a1 online resource (XXIV, 378 p. 111 illus.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v533 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-03397-2 320 $aIncludes bibliographical references and index. 327 $aMultiple Objective Multistage Decision Making -- Elements of Fuzzy-Like MOMSDM -- Fuzzy MOMSDM for Dynamic Machine Allocation -- Fuzzy MOMSDM for Closed Multiclass Queuing Networks -- Fuzzy Random MOMSDM for Inventory Management -- Fuzzy Random MOMSDM for Facilities Planning -- Fuzzy Random MOMSDM for Transportation Assignment. 330 $aDecision has inspired reflection of many thinkers since the ancient times. With the rapid development of science and society, appropriate dynamic decision making has been playing an increasingly important role in many areas of human activity including engineering, management, economy and others. In most real-world problems, decision makers usually have to make decisions sequentially at different points in time and space, at different levels for a component or a system, while facing multiple and conflicting objectives and a hybrid uncertain environment where fuzziness and randomness co-exist in a decision making process. This leads to the development of fuzzy-like multiple objective multistage decision making. This book provides a thorough understanding of the concepts of dynamic optimization from a modern perspective and presents the state-of-the-art methodology for modeling, analyzing and solving the most typical multiple objective multistage decision making practical application problems under fuzzy-like uncertainty, including the dynamic machine allocation, closed multiclass queueing networks optimization, inventory management, facilities planning and transportation assignment. A number of real-world engineering case studies are used to illustrate in detail the methodology. With its emphasis on problem-solving and applications, this book is ideal for researchers, practitioners, engineers, graduate students and upper-level undergraduates in applied mathematics, management science, operations research, information system, civil engineering, building construction and transportation optimization. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v533 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 676 $a006.3 700 $aXu$b Jiuping$4aut$4http://id.loc.gov/vocabulary/relators/aut$0865879 702 $aZeng$b Ziqiang$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299470403321 996 $aFuzzy-Like Multiple Objective Multistage Decision Making$91934730 997 $aUNINA