An Innovative Fuzzy-Neural Decision Analyzer for Qualitative Group Decision Making

2015 ◽  
Vol 14 (03) ◽  
pp. 659-696 ◽  
Author(s):  
Ki-Young Song ◽  
Gerald T. G. Seniuk ◽  
Janusz A. Kozinski ◽  
Wen-Jun Zhang ◽  
Madan M. Gupta
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Theory ◽  
Human Cognition ◽  
Group Decisions ◽  
Uncertain Environments ◽  
Fuzzy Neural ◽  
Professional Fields

Many qualitative group decisions in professional fields such as law, engineering, economics, psychology, and medicine that appear to be crisp and certain are in reality shrouded in fuzziness as a result of uncertain environments and the nature of human cognition within which the group decisions are made. In this paper, we introduce an innovative approach to group decision making in uncertain situations by using fuzzy theory and a mean-variance neural approach. The key idea of this proposed approach is to defuzzify the fuzziness of the evaluation values from a group, compute the excluded-mean of individual evaluations and weight it by applying a variance influence function (VIF); this process of weighting the excluded-mean by VIF provides an improved result in the group decision making. In this paper, a case study with the proposed fuzzy-neural approach is also presented. The results of this case study indicate that this proposed approach can improve the effectiveness of qualitative decision making by providing the decision maker with a new cognitive tool to assist in the reasoning process.

scholarly journals Excluded-Mean-Variance Neural Decision Analyzer for Qualitative Group Decision Making

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Ki-Young Song ◽  
Janusz Kozinski ◽  
Gerald T. G. Seniuk ◽  
Madan M. Gupta
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Human Cognition ◽  
Group Decisions ◽  
Uncertain Environments ◽  
Engineering Economics ◽  
Professional Fields ◽  
Mean Variance

Many qualitative group decisions in professional fields such as law, engineering, economics, psychology, and medicine that appear to be crisp and certain are in reality shrouded in fuzziness as a result of uncertain environments and the nature of human cognition within which the group decisions are made. In this paper we introduce an innovative approach to group decision making in uncertain situations by using a mean-variance neural approach. The key idea of this proposed approach is to compute the excluded mean of individual evaluations and weight it by applying a variance influence function (VIF); this process of weighting the excluded mean by VIF provides an improved result in the group decision making. In this paper, a case study with the proposed excluded-mean-variance approach is also presented. The results of this case study indicate that this proposed approach can improve the effectiveness of qualitative decision making by providing the decision maker with a new cognitive tool to assist in the reasoning process.

Group Decision Model Based on Ordered Weighted Distance to Aid Decisions on Logistics

2018 ◽  
Vol 26 (02) ◽  
pp. 233-254 ◽  
Author(s):  
Jose Leao E Silva Filho ◽  
Danielle Costa Morais
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Decision Model ◽  
Group Decision ◽  
Decision Rules ◽  
Decision Makers ◽  
Weighted Distance ◽  
Model Based ◽  
Decision Making Model

This paper presents a group decision-making model using a distance aggregator based on Ordered Weighted Distance (OWD) which offers a solution that can reduce disagreement between decision makers (DMs). This paper discusses decision rules and sets out measures to evaluate compensatory effects that have a bearing on DMs’ opinions. The model uses formulations of distances to reveal the differences in opinion among DMs and discusses the meanings of distance and the information presented by each DM. Finally, a case study of a logistics problem is used to illustrate how the model is applied.

Three-way group decisions under hesitant fuzzy linguistic environment for green supplier selection

Kybernetes ◽  
2020 ◽  
Vol 49 (12) ◽  
pp. 2919-2945 ◽  
Author(s):  
Weimin Ma ◽  
Wenjing Lei ◽  
Bingzhen Sun
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Supplier Selection ◽  
Group Decision ◽  
Group Decisions ◽  
Content Type ◽  
Green Supplier Selection ◽  
Strategic Role ◽  
Fuzzy Linguistic ◽  
Selection Of

Purpose The purpose of this paper is to propose a three-way group decision-making approach to address the selection of green supplier, by extending decision-theoretic rough set (DTRS) into hesitant fuzzy linguistic (HFL) environment, considering the flexible evaluation expression format of HFL term set (HFLTS) and the idea of minimum expected risk in DTRS. Design/methodology/approach Specifically, the authors first present the calculation method of the conditional probability and discuss the loss functions of DTRS with HFL element (HFLE), along with some associated properties being investigated in detail. Further, three-way group decisions rules can be deduced, followed by the cost of every green supplier candidate. Thus, based on these discussions, a novel green supplier selection DTRS model that combines multi-criteria group decision-making (MCGDM) and HFLTS is designed. Findings A numerical example of green supplier selection, the comparative analysis and associated discussions are conducted to illustrate the applicability and novelty of the presented model. Practical implications The selection of green supplier has played a critically strategic role in sustainable enterprise development due to continuous environmental concerns. This paper offers an insight for companies to select green supplier selection from the perspective of three-way group decisions. Originality/value This paper uses three-way decisions to address green supplier selection in the HFL context, which is considered as a MCGDM issue.

Sign in / Sign up

Export Citation Format

Share Document