The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making

2015 ◽  
Vol 26 ◽  
pp. 57-73 ◽  
Author(s):  
Ting-Yu Chen
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Multiple Criteria ◽  
Topsis Method ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

DATA CONSTRUCTION PROCESS AND QUALIFLEX-BASED METHOD FOR MULTIPLE-CRITERIA GROUP DECISION MAKING WITH INTERVAL-VALUED INTUITIONISTIC FUZZY SETS

2013 ◽  
Vol 12 (03) ◽  
pp. 425-467 ◽  
Author(s):  
TING-YU CHEN
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Intuitionistic Fuzzy ◽  
Uncertainty Indices ◽  
Interval Valued

Based on Jacquet-Lagreze's permutation method, QUALIFLEX is an outranking model that investigates all possible permutations of alternatives with respect to the consequences of all criteria. The purpose of this paper is to develop a QUALIFLEX-based method for multiple criteria group decision making within a decision environment of interval-valued intuitionistic fuzzy sets. We conduct a statistical inference approach with finite population correction to construct interval-valued intuitionistic fuzzy numbers. In addition, we incorporate the relative importance of decision makers and fuse individual opinions to form collective ratings using a modified method with weighted interval estimations. In view of diversiform preference types (weak order, strict order, difference order, interval bound, and ratio bound), we represent multiple decision makers' various forms of preference structures and assess criterion weights under incomplete information. By means of score functions, accuracy functions, membership-uncertainty indices, and hesitation-uncertainty indices, a ranking procedure is employed to identify a criterion-wise preference of alternatives. A QUALIFLEX-based model is then established to measure the level of concordance of the complete preference order for handling multiple criteria group decisions. The feasibility of the proposed method is illustrated by a practical problem relating to the selection of a landfill site. As indicated in the application, the proposed method is useful for handling complicated group decision-making problems that involve comprehensive criteria and limited alternatives.

scholarly journals Three-Way Decisions with Interval-Valued Intuitionistic Fuzzy Decision-Theoretic Rough Sets in Group Decision-Making

Symmetry ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 281 ◽  
Author(s):  
Dajun Ye ◽  
Decui Liang ◽  
Pei Hu
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Rough Sets ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Fuzzy Decision ◽  
Grey Correlation ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

In this article, we demonstrate how interval-valued intuitionistic fuzzy sets (IVIFSs) can function as extended intuitionistic fuzzy sets (IFSs) using the interval-valued intuitionistic fuzzy numbers (IVIFNs) instead of precision numbers to describe the degree of membership and non-membership, which are more flexible and practical in dealing with ambiguity and uncertainty. By introducing IVIFSs into three-way decisions, we provide a new description of the loss function. Thus, we firstly propose a model of interval-valued intuitionistic fuzzy decision-theoretic rough sets (IVIFDTRSs). According to the basic framework of IVIFDTRSs, we design a strategy to address the IVIFNs and deduce three-way decisions. Then, we successfully extend the results of IVIFDTRSs from single-person decision-making to group decision-making. In this situation, we adopt a grey correlation accurate weighted determining method (GCAWD) to compute the weights of decision-makers, which integrates the advantages of the accurate weighted determining method and grey correlation analysis method. Moreover, we utilize the interval-valued intuitionistic fuzzy weighted averaging (IIFWA) operation to count the aggregated scores and the accuracies of the expected losses. By comparing these scores and accuracies, we design a simple and straightforward algorithm to deduce three-way decisions for group decision-making. Finally, we use an illustrative example to verify our results.

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