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020 _a9789819735846
_9978-981-97-3584-6
024 7 _a10.1007/978-981-97-3584-6
_2doi
050 4 _aT57.6-.97
072 7 _aKJT
_2bicssc
072 7 _aKJMD
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072 7 _aBUS049000
_2bisacsh
072 7 _aKJT
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072 7 _aKJMD
_2thema
100 1 _aZhang, Zhen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980805
245 1 0 _aModeling Complex Linguistic Information to Support Group Decision Making Under Uncertainty
_h[electronic resource] :
_bTheories, Methods and Applications /
_cby Zhen Zhang, Wenyu Yu, Zhuolin Li.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIII, 198 p. 34 illus., 19 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 _aUncertainty and Operations Research,
_x2195-9978
505 0 _aLinguistic Computational Models for Computing with Words -- Basic Linguistic Computation Models for Computing with Words -- Linguistic Computational Models for Modeling Complex Linguistic Information -- MAGDM Methods Based on Multi-granular Linguistic Information -- Consensus-based Method for MAGDM with Multi-granular Unbalanced Linguistic Information -- MALGDM Method with Multi-granular Linguistic Information -- MAGDM Methods Based on Hesitant Fuzzy Linguistic Information -- MAGDM with Multi-granular HFLTSS: A Minimum Adjustment-Based Consensus Model -- Extended TODIM for MAGDM Based on Unbalanced HFLTSS -- A Linguistic Distribution-based Approach to MALGDM with Multi-granular Unbalanced Hesitant Fuzzy Linguistic Information -- PISs-Based Linguistic GDM Methods -- Consistency and Consensus Driven Method for GDM Based on PISs -- A PISs-Based Approach to SNGDM: Managing Non-Cooperative Behavior.
520 _aThis book systematically explores theories related to linguistic computational models and group decision making methods under uncertainty. It introduces innovative linguistic computational models capable of fusing complex linguistic information, including multi-granular linguistic information, unbalanced linguistic information and hesitant fuzzy linguistic information. Building upon the linguistic computational models, this book presents methods tailored to various types of group decision making problems under uncertainty. Additionally, it delves into group decision making problems where the personalized individual semantics of experts are considered. The book also showcases practical applications of the proposed group decision making methods, ranging from ERP system supplier selection to talent recruitment, subway line selection, and location selection for electric vehicle charging stations. By shedding light on novel models for modeling complex linguistic information and introducing new approaches to addressing linguistic group decision making challenges, this book offers valuable insights for engineers, researchers, and postgraduates interested in decision analysis, operations research, computational intelligence, management science and engineering, and related fields.
650 0 _aOperations research.
_957943
650 0 _aManagement science.
_957941
650 1 4 _aOperations Research and Decision Theory.
_967143
650 2 4 _aOperations Research, Management Science.
_965527
700 1 _aYu, Wenyu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980806
700 1 _aLi, Zhuolin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980807
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819735839
776 0 8 _iPrinted edition:
_z9789819735853
776 0 8 _iPrinted edition:
_z9789819735860
830 0 _aUncertainty and Operations Research,
_x2195-9978
_977657
856 4 0 _uhttps://doi.org/10.1007/978-981-97-3584-6
912 _aZDB-2-BUM
912 _aZDB-2-SXBM
999 _c61702
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