04132nam 22005775 450 991029947040332120200703022614.03-319-03398-010.1007/978-3-319-03398-3(CKB)3710000000089111(DE-He213)978-3-319-03398-3(SSID)ssj0001186887(PQKBManifestationID)11702437(PQKBTitleCode)TC0001186887(PQKBWorkID)11242102(PQKB)10009183(MiAaPQ)EBC3092063(PPN)176749608(EXLCZ)99371000000008911120140207d2014 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierFuzzy-Like Multiple Objective Multistage Decision Making /by Jiuping Xu, Ziqiang Zeng1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (XXIV, 378 p. 111 illus.) Studies in Computational Intelligence,1860-949X ;533Bibliographic Level Mode of Issuance: Monograph3-319-03397-2 Includes bibliographical references and index.Multiple 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.Decision 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.Studies in Computational Intelligence,1860-949X ;533Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.006.3Xu Jiupingauthttp://id.loc.gov/vocabulary/relators/aut865879Zeng Ziqiangauthttp://id.loc.gov/vocabulary/relators/autBOOK9910299470403321Fuzzy-Like Multiple Objective Multistage Decision Making1934730UNINA