Grey relational analysis method based on cumulative prospect theory for intuitionistic fuzzy multi-attribute group decision making

2021 ◽  
Vol 41 (2) ◽  
pp. 3783-3795
Author(s):  
Shanshan Zhang ◽  
Hui Gao ◽  
Guiwu Wei ◽  
Xudong Chen
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Prospect Theory ◽  
Group Decision Making ◽  
Grey Relational Analysis ◽  
Group Decision ◽  
Cumulative Prospect Theory ◽  
Relational Analysis ◽  
Intuitionistic Fuzzy ◽  
Grey Relational

The Multi-attribute group decision making (MAGDM) problem is an interesting everyday problem full of complexity and ambiguity. As an extended form of fuzzy sets, intuitionistic fuzzy sets (IFSs) can provide decision-makers (DMs) with a wider range of preferences for MAGDM. The grey relational analysis (GRA) is an effective method for dealing with MAGDM problems. However, in view of the incomplete and asymmetric information and the influence of DMs’ psychological factors on the decision result, we develop a new model that GRA method based on cumulative prospect theory (CPT) under the intuitionistic fuzzy environment. Moreover, the weight of attribute is calculated by entropy weight, so as to distinguish the importance level of attributes, which greatly improves the credibility of the selected scheme. simultaneously, the proposed method is used to the selection of optimal green suppliers for testifying the availability of this new model and the final comparison between this new method and the existing methods further verify the reliability. In addition, the proposed method provides some references for other selection problems.

scholarly journals Group Decision-making Method Based on Grey Relational Analysis and Evidence Theory for XAI

2021 ◽  
Author(s):  
Decai Sun ◽  
Dang Luo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Grey Relational Analysis ◽  
Group Decision ◽  
Evidence Theory ◽  
Mass Function ◽  
Decision Makers ◽  
Relational Analysis ◽  
Grey Relational ◽  
Ranking Analysis

Abstract For the uncertainty and complexity ingroup decision making and the differences of decision makers’ reliabilities, a group decision making method based on grey relational analysis and evidence theory is proposed. Combining grey relational analysis with evidence theory, a novel decision-making method extracting the degree of ignorance for individual decision makers’ information and constructing the Mass function is presented based on the comprehensive grey relational analysis (CGRA) method. We should also address how AI systems make their black box decisions, which calls for research into Explainable AI (XAI) by pursuing reverse engineering and self-explainability in AI. Considering the differences of decision makers’ reliabilities, the Mass function is modified by the evidence weight, and the group decision information is fused by the Dempster’s combination rule. On this basis, the Mass function is further transformed into the probability by the Pignistic probability transformation, which issued for ranking analysis of group decision making. Finally, the proposed method is applied to the green supplier selection, and the comparative analysis is further performed to verify the rationality and effectiveness of the proposed method.

scholarly journals Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 93
Author(s):  
Marcelo Loor ◽  
Ana Tapia-Rosero ◽  
Guy De Tré
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Intuitionistic Fuzzy Sets ◽  
Heterogeneous Group ◽  
Intuitionistic Fuzzy ◽  
High Level ◽  
Consensus Reaching ◽  
Novel Algorithm

A flexible attribute-set group decision-making (FAST-GDM) problem consists in finding the most suitable option(s) out of the options under consideration, with a general agreement among a heterogeneous group of experts who can focus on different attributes to evaluate those options. An open challenge in FAST-GDM problems is to design consensus reaching processes (CRPs) by which the participants can perform evaluations with a high level of consensus. To address this challenge, a novel algorithm for reaching consensus is proposed in this paper. By means of the algorithm, called FAST-CR-XMIS, a participant can reconsider his/her evaluations after studying the most influential samples that have been shared by others through contextualized evaluations. Since exchanging those samples may make participants’ understandings more like each other, an increase of the level of consensus is expected. A simulation of a CRP where contextualized evaluations of newswire stories are characterized as augmented intuitionistic fuzzy sets (AIFS) shows how FAST-CR-XMIS can increase the level of consensus among the participants during the CRP.

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