Theory And Approaches Of Group Decision Making With Uncertain Linguistic Expressions

This book mainly introduces a series of theory and approaches of group decision-making based on several types of uncertain linguistic expressions and addresses their applications. The book pursues three major objectives: (1) to introduce some techniques to model several types of natural linguistic expressions; (2) to handle these expressions in group decision-making; and (3) to clarify the involved approaches by practical applications. The book is especially valuable for readers to understand how linguistic expressions could be employed and operated to make decisions, and motivates researchers to consider more types of natural linguistic expressions in decision analysis under uncertainties.

This book provides a systematic introduction to linguistic aggregation operators, linguistic preference relations, and various models and approaches to multi-attribute decision making with linguistic information. Offers practical examples, tables and figures.

Tackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-making process by employing complex networks and studying various methods of aggregating preference information from various unascertained, time-series based decision-making groups' structures. Written by Jianjun Zhu, director of the Chinese Society for Optimization, Overall Planning, and Economic Mathematics, the book starts by examining case studies of group decision making. It discusses consistency measuring and ranking methods of interval number reciprocal judgment matrix and interval number complementary judgment matrix. Next, it presents an unascertained number preference and a three-point interval number preference. Investigating linguistic preferences, the text provides two consistencies definitions. It considers the aggregating methods of several uncertain preferences as well as the multistage aggregating model of uncertain preference. The final chapter proposes an aggregating model of multistage linguistic information based on TOPSIS. The research in this book is supported by a number of scientific foundations from the People’s Republic of China, including the National Natural Science Foundation of China; the Social Science Foundation of China, Jiangsu Province; and the Soft Science Foundation of China.

Existing intuitionistic linguistic variables can describe the linguistic information of both the truth/membership and falsity/non-membership degrees, but it cannot represent the indeterminate and inconsistent linguistic information. To deal with the issue, this paper originally proposes the concept of a linguistic neutrosophic number (LNN), which is characterized independently by the truth, indeterminacy, and falsity linguistic variables. Then, we define the basic operational laws of LNNs and the score and accuracy functions of LNN for comparing LNNs. Next, we develop an LNN-weighted arithmetic averaging (LNNWAA) operator and an LNN-weighted geometric averaging (LNNWGA) operator to aggregate LNN information and investigate their properties. Further, a multiple attribute group decision-making method based on the proposed LNNWAA or LNNWGA operator is established under LNN environment. Finally, an illustrative example about selecting problems of investment alternatives is presented to demonstrate the application and effectiveness of the developed approach.

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.

This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

Tackling the question of how to effectively aggregate uncertain preference information in multiple structures given by decision-making groups, Theory and Approaches of Unascertained Group Decision-Making focuses on group aggregation methods based on uncertainty preference information. It expresses the complexity existing in each group decision-maki

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