A New Selection Process Based on Granular Computing for Group Decision Making Problems

2015 ◽  
pp. 13-24 ◽  
Author(s):  
Francisco Javier Cabrerizo ◽  
Raquel Ureña ◽  
Juan Antonio Morente-Molinera ◽  
Witold Pedrycz ◽  
Francisco Chiclana ◽  
...  
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Granular Computing ◽  
Group Decision ◽  
Selection Process

Estimating incomplete information in group decision making: A framework of granular computing

2020 ◽  
Vol 86 ◽  
pp. 105930 ◽  
Author(s):  
Francisco Javier Cabrerizo ◽  
Rami Al-Hmouz ◽  
Ali Morfeq ◽  
María Ángeles Martínez ◽  
Witold Pedrycz ◽  
...  
Keyword(s):  
Decision Making ◽  
Incomplete Information ◽  
Group Decision Making ◽  
Granular Computing ◽  
Group Decision

Multicriteria Group Decision Making Using Fuzzy Approach for Evaluating Criteria of Electrician

2016 ◽  
Vol 6 (5) ◽  
pp. 2462 ◽  
Author(s):  
Wiwien Hadikurniawati ◽  
Khabib Mustofa
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Fuzzy Numbers ◽  
Group Decision ◽  
Selection Process ◽  
Subjective Assessment ◽  
Fuzzy Approach ◽  
Multicriteria Group Decision Making ◽  
Alternative Ranking ◽  
Index Of Optimism

<p>This paper presents an approach of fuzzy multicriteria group decision making in determining alternatives to solve the selection problem of the electrician  through  a competency test.   Fuzzy approach is used to determine the highest priority of alternative electrician who has knowledge and ability that best fits the given parameters. Linguistic variables are presented by triangular fuzzy numbers. They are used to represent a subjective assessment of the decision-makers so that uncertainty and imprecision in the selection process can be minimized. Fuzzy approach require transforming crisp data to fuzzy numbers. Output of the best alternatives is generated by ranking method. Ranking has been made base on eight criteria which make the evaluation basis of each alternative. Ranking of the results is determined using different value of optimism index (). The fuzzy multi criteria decision making (FMCDM) calculation is using the best alternative using three value of optimism index. The result of calculation shows that the same alternative reached from different index of optimism. This alternative is the highest priority of decision making process.</p>

scholarly journals Group Decision Making Based on a Framework of Granular Computing for Multi-Criteria and Linguistic Contexts

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 54670-54681 ◽  
Author(s):  
Edwin Alberto Callejas ◽  
Jose Antonio Cerrada ◽  
Carlos Cerrada ◽  
Francisco Javier Cabrerizo
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Granular Computing ◽  
Group Decision

A CONSENSUS MODEL FOR GROUP DECISION-MAKING PROBLEMS WITH INTERVAL FUZZY PREFERENCE RELATIONS

2012 ◽  
Vol 11 (04) ◽  
pp. 709-725 ◽  
Author(s):  
J. M. TAPIA GARCÍA ◽  
M. J. DEL MORAL ◽  
M. A. MARTÍNEZ ◽  
E. HERRERA-VIEDMA
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Preference Relation ◽  
Feedback Mechanism ◽  
Consensus Model ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Fuzzy Preference

Interval fuzzy preference relations can be useful to express decision makers' preferences in group decision-making problems. Usually, we apply a selection process and a consensus process to solve a group decision situation. In this paper, we present a consensus model for group decision-making problems with interval fuzzy preference relations. This model is based on two consensus criteria, a consensus measure and a proximity measure, and also on the concept of coincidence among preferences. We compute both consensus criteria in the three representation levels of a preference relation and design an automatic feedback mechanism to guide experts in the consensus reaching process. We show an application example in social work.

A Granular Computing-Based Model for Group Decision-Making in Multi-Criteria and Heterogeneous Environments

2022 ◽  
Author(s):  
Francisco Cabrerizo ◽  
Juan Carlos González-Quesada ◽  
Ignacio Pérez ◽  
Enrique Herrera-Viedma
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Granular Computing ◽  
Group Decision ◽  
Heterogeneous Environments

Multi-Criteria Project Selection Using an Extended VIKOR Method with Interval Type-2 Fuzzy Sets

2015 ◽  
Vol 14 (05) ◽  
pp. 993-1016 ◽  
Author(s):  
Mehdi Keshavarz Ghorabaee ◽  
Maghsoud Amiri ◽  
Jamshid Salehi Sadaghiani ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Project Selection ◽  
Real Problem ◽  
Vikor Method ◽  
Criteria Weights ◽  
Interval Type

Project selection can be a real problem of the multi-criteria group decision making if a group of decision makers express their preferences depending on the nature of the alternatives and different criteria with respect to their knowledge about them. The purpose of the project selection process is to analyze project viability and to approve or reject project proposals based on established criteria. Such decisions are often complex, because they require the identification, consideration and analysis of many tangible and intangible factors. This paper presents a multi-criteria group decision-making approach for project selection problem in the type-2 fuzzy environment. The proposed method is an extended version of Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method with interval type-2 fuzzy numbers; it is called type-2 fuzzy VIKOR (T2F-VIKOR). A stepwise procedure is used for ranking and evaluating the alternatives in the developed method, and the best solution is selected considering both the beneficial and nonbeneficial criteria. An illustrative example is presented to show the applicability of the proposed approach in the project selection problems, and the results are analyzed. The results are compared with some existing methods to show the validity of the extended method. We also utilize six sets of criteria weights for analyzing the stability of the proposed method. These analyses show that the obtained results of the proposed method are relatively consistent with other methods and have good stability in different criteria weights.

A Consensus Model for Group Decision Making with Hesitant Fuzzy Information

2015 ◽  
Vol 23 (03) ◽  
pp. 459-480 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Wang ◽  
Xuedong Tian
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Selection Process ◽  
Feedback Mechanism ◽  
Mathematical Tool ◽  
Fuzzy Information ◽  
Consensus Process ◽  
Decision Matrix ◽  
High Level

Hesitant fuzzy sets, permitting the membership of an element to be a set of several possible values, can be used as an efficient mathematical tool for modeling people's hesitancy in daily life. The aim of this paper is to present a consensus support model for group decision making with hesitant fuzzy information. This model is composed of two processes: a consensus process and a selection process. The consensus process is carried out to reach a high level of consensus among experts' opinions before applying a selection process. We first aggregate the hesitant fuzzy decision matrix into a group decision matrix by using the additive aggregation (AA) operator. Then the consensus measure is used to design a feedback mechanism that generates advice to the experts on how they should change their preferences to obtain a solution with a high consensus degree. In the selection process, based on the consentaneous group decision matrix, the additive weighted aggregation (AWA) operator is utilized to derive the overall attribute values of alternatives, by which the most desirable alternative can be found out. Finally, a practical example is proposed to illustrate the application of the proposed model.

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