| Hesitant fuzzy linguistic term sets can be flexible and comprehensive characterization of experts’ qualitative decision information,and have a wide range of applications in the fields of decision-making,pattern recognition,medical diagnosis and the others.This paper mainly studies the distance measures of hesitant fuzzy linguistic term sets,and the consensus adjustment method of hesitant fuzzy decision matrices in multi-attribute group decision making problem.Firstly,we compare the distance formulas of the existing hesitant fuzzy linguistic term sets and give a new axiomatic definition.Then,based on the distance measures of hesitant fuzzy linguistic term sets,the level of consensus between hesitant fuzzy decision matrices and their related concepts are defined,and the consensus research process is introduced.It consists of four chapters.In chapter 1,we mainly introduce the research background and development status of the hesitant fuzzy linguistic term sets and its distance measures,as well as the research background and development status of the multi-attribute group decision-making method and the main results of this paper.Chapter 2 deals with the distance measures of hesitant fuzzy linguistic term sets.Firstly,we give the basic concept of hesitant fuzzy linguistic term sets as well as the basic operations and comparisons methods.Secondly,the axiomatic definitions and formulas of distance are summarized,as a result,a new axiomatic definition of the distance of hesitant fuzzy linguistic term sets is proposed,and then we compare the properties these distance formulas satisfy as well as the relationships between them.Chapter 3 is devoted to the experts’ consensus adjustment method in multiple attribute group decision making when the evaluation value given by expert is hesitant fuzzy linguistic information.Firstly,we give the consensus measure and consensus adjustment model of hesitant fuzzy linguistic term sets according to the method of aggregation and the distance measures between hesitant fuzzy linguistic term sets.The model uses a combination of feedback mechanism and automatic adjustment to provide experts with more information as much as possible,which is helpful for the experts to adjust information and reach group consensus.Finally,a numerical example is used to illustrate the feasibility and practicability of the proposed consensus model.The fourth chapter summarizes and prospects this paper. |
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