Distance-Based Multi-Criteria Group Decision-Making Approaches with Multi-Hesitant Fuzzy Linguistic Information

In this paper, the distance-based multi-criteria group decision-making (MCGDM) approaches using multi-hesitant fuzzy linguistic term sets (MHFLTSs) are proposed. MHFLTSs can contain nonconsecutive and repetitive linguistic terms, so as to deal with repeated linguistic values in group decision-making. A multi-hesitant fuzzy linguistic term element (MHFLTE) can be produced by collecting the evaluation values of several decision-makers or given by one person who has uncertainty in evaluation. The corresponding set operations and comparison rules are defined and the generalized hesitant fuzzy linguistic distance for MHFLTEs is given based on the linguistic scale function. Then this distance is embedded into the TOPSIS, VIKOR and TODIM approaches for the purpose of solving multi-criteria decision-making (MCDM) problems in the context of multi-hesitant fuzzy linguistic information. With increasing concerns about deterioration in environment, organizations are obliged to carry out more environmental sustainable activities than before, such as progressive practices in green supply chain management (GSCM). Therefore, with respect to the application of MHFLTSs in GSCM, two illustrations for evaluating the related alternatives are finally provided, together with the sensitivity and comparison analysis, to show the validity and effectiveness of our proposal.

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A BI-OBJECTIVE SCORE-VARIANCE BASED LINEAR ASSIGNMENT METHOD FOR GROUP DECISION MAKING WITH HESITANT FUZZY LINGUISTIC TERM SETS

Technological and Economic Development of Economy ◽

10.3846/20294913.2016.1275878 ◽

2018 ◽

Vol 24 (3) ◽

pp. 1125-1148 ◽

Cited By ~ 4

Author(s):

Seyed Hossein RAZAVI HAJIAGHA ◽

Meisam SHAHBAZI ◽

Hannan AMOOZAD MAHDIRAJI ◽

Hossein PANAHIAN

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Decision Makers ◽

Assignment Method ◽

Linguistic Term ◽

Criteria Weights ◽

Individual Evaluation ◽

Fuzzy Linguistic ◽

Linear Assignment

Decision makers usually prefer to express their preferences by linguistic variables. Classic fuzzy sets allowed expressing these preferences using a single linguistic value. Considering inevitable hesitancy of decision makers, hesitant fuzzy linguistic term sets allowed them to express individual evaluation using several linguistic values. Therefore, these sets improve the ability of humans to determine believes using their own language. Considering this feature, in this paper a method upon linear assignment method is proposed to solve group decision making problems using this kind of information, when criteria weights are known or unknown. The performance of the proposed method is illustrated in a numerical example and the results are compared with other methods to delineate the models efficiency. Following a logical and well-known mathematical logic along with simplicity of execution are the main advantages of the proposed method.

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A Novel Linguistic Group Decision-Making Model Based on Extended Hesitant Fuzzy Linguistic Term Sets

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488515500166 ◽

2015 ◽

Vol 23 (03) ◽

pp. 379-398 ◽

Cited By ~ 24

Author(s):

Cuiping Wei ◽

Na Zhao ◽

Xijin Tang

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Distance Measures ◽

Linguistic Terms ◽

Linguistic Information ◽

Linguistic Group ◽

Linguistic Term ◽

Decision Making Model ◽

Fuzzy Linguistic

Hesitant fuzzy linguistic term set (HFLTS) is a set with ordered consecutive linguistic terms, and is very useful in addressing the situations where people are hesitant in providing their linguistic assessments. Wang [H. Wang, Extended hesitant fuzzy linguistic term sets and their aggregation in group decision making, International Journal of Computational Intelligence Systems 8(1) (2015) 14–33.] removed the consecutive condition to introduce the notion of extended HFLTS (EHFLTS). The generalized form has wider applications in linguistic group decision-making. By introducing distance measures for EHFLTSs, in this paper we develop a novel multi-criteria group decision making model to deal with hesitant fuzzy linguistic information. The model collects group linguistic information by using EHFLTSs and avoids the possible loss of information. Moreover, it can assess the importance weights of criteria according to their subjective and objective information and rank alternatives based on the rationale of TOPSIS. In order to illustrate the applicability of the proposed algorithm, two examples are given and comparisons are made with the other existing methods.

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A Novel Comparative Linguistic Distance Measure Based on Hesitant Fuzzy Linguistic Term Sets and Its Application in Group Decision-Making

International Journal of Computational Intelligence Systems ◽

10.2991/ijcis.2018.125905643 ◽

2018 ◽

Vol 12 (1) ◽

pp. 227

Author(s):

Mei Cai ◽

Yiming Wang ◽

Zaiwu Gong ◽

Guo Wei

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Distance Measure ◽

Group Decision ◽

Linguistic Distance ◽

Linguistic Term ◽

Fuzzy Linguistic

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A CONSENSUS MODEL FOR GROUP DECISION MAKING PROBLEMS WITH UNBALANCED FUZZY LINGUISTIC INFORMATION

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622009003296 ◽

2009 ◽

Vol 08 (01) ◽

pp. 109-131 ◽

Cited By ~ 185

Author(s):

F. J. CABRERIZO ◽

S. ALONSO ◽

E. HERRERA-VIEDMA

Keyword(s):

Decision Making ◽

Group Decision Making ◽

Group Decision ◽

Feedback Mechanism ◽

Consensus Model ◽

Linguistic Information ◽

Linguistic Methodology ◽

Linguistic Term ◽

Proximity Measures ◽

Fuzzy Linguistic

Most group decision making (GDM) problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions. However, there exist problems whose assessments need to be represented by means of unbalanced linguistic term sets, i.e. using term sets that are not uniformly and symmetrically distributed. The aim of this paper is to present a consensus model for GDM problems with unbalanced fuzzy linguistic information. This consensus model is based on both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two consensus criteria, consensus degrees, and proximity measures. To do so, we use a new fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy linguistic information,1 (Int. J. Intell. Syst.22(11) (2007) 1197–1214), which uses the linguistic 2-tuple model as representation base of unbalanced fuzzy linguistic information. In addition, the consensus model presents a feedback mechanism to help experts for reaching the highest degree of consensus possible. There are two main advantages provided by this consensus model. First, its ability to cope with GDM problems with unbalanced fuzzy linguistic information overcoming the problem of finding different discrimination levels in linguistic term sets. Second, it supports the consensus process automatically, avoiding the possible subjectivity that the moderator can introduce in this phase.

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