Modeling Complex Linguistic Information to Support Group Decision Making under Uncertainty (Uncertainty and Operations Research)

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Modeling Complex Linguistic Information to Support Group Decision Making under Uncertainty (Uncertainty and Operations Research)

Zhang, Zhen/ Yu, Wenyu/ Li, Zhuolin

ウェブストア価格 ¥35,183（本体¥31,985）
Springer Nature（2024/08発売）
外貨定価 US$ 169.99
ポイント 319pt

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製本 Hardcover:ハードカバー版／ページ数 230 p.
言語 ENG
商品コード 9789819735839

Full Description

This 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.

Linguistic 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.