A Framework for Multi-Attribute Group Decision-Making Using Double Hierarchy Hesitant Fuzzy Linguistic Term Set

2019 ◽  
Vol 21 (4) ◽  
pp. 1130-1143 ◽  
Author(s):  
R. Krishankumar ◽  
L. S. Subrajaa ◽  
K. S. Ravichandran ◽  
Samarjit Kar ◽  
Arsham Borumand Saeid
2018 ◽  
Vol 24 (3) ◽  
pp. 1125-1148 ◽  
Author(s):  
Seyed Hossein RAZAVI HAJIAGHA ◽  
Meisam SHAHBAZI ◽  
Hannan AMOOZAD MAHDIRAJI ◽  
Hossein PANAHIAN

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.


Author(s):  
Cuiping Wei ◽  
Na Zhao ◽  
Xijin Tang

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.


2017 ◽  
Vol 16 (04) ◽  
pp. 1069-1099 ◽  
Author(s):  
Jing Wang ◽  
Jian-Qiang Wang ◽  
Hong-Yu Zhang ◽  
Xiao-Hong Chen

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.


2019 ◽  
Vol 32 (7) ◽  
pp. 2879-2896 ◽  
Author(s):  
R. Krishankumar ◽  
K. S. Ravichandran ◽  
Manish Aggarwal ◽  
Sanjay K. Tyagi

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