TY - JOUR
T1 - Estimating incomplete information in group decision making
T2 - A framework of granular computing
AU - Cabrerizo, Francisco Javier
AU - Al-Hmouz, Rami
AU - Morfeq, Ali
AU - Martínez, María Ángeles
AU - Pedrycz, Witold
AU - Herrera-Viedma, Enrique
N1 - Funding Information:
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia , under grant no. ( KEP-7-135-39 ). The authors, therefore, acknowledge with thanks DSR technical and financial support.
Funding Information:
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia, under grant no. (KEP-7-135-39). The authors, therefore, acknowledge with thanks DSR technical and financial support.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1
Y1 - 2020/1
N2 - A general assumption in group decision making scenarios is that of all individuals possess accurate knowledge of the entire problem under study, including the abilities to make a distinction of the degree up to which an alternative is better than other one. However, in many real world scenarios, this may be unrealistic, particularly those involving numerous individuals and options to choose from conflicting and dynamics information sources. To manage such a situation, estimation methods of incomplete information, which use own assessments provided by the individuals and consistency criteria to avoid discrepancy, have been widely employed under fuzzy preference relations. In this study, we introduce the information granularity concept to estimate missing values supporting the objective of obtaining complete fuzzy preference relations with higher consistency levels. We use the concept of granular preference relations to form each missing value as a granule of information in place of a crisp number. This offers the flexibility that is required to estimate the missing information so that the consistency levels related to the complete fuzzy preference relations are as higher as possible.
AB - A general assumption in group decision making scenarios is that of all individuals possess accurate knowledge of the entire problem under study, including the abilities to make a distinction of the degree up to which an alternative is better than other one. However, in many real world scenarios, this may be unrealistic, particularly those involving numerous individuals and options to choose from conflicting and dynamics information sources. To manage such a situation, estimation methods of incomplete information, which use own assessments provided by the individuals and consistency criteria to avoid discrepancy, have been widely employed under fuzzy preference relations. In this study, we introduce the information granularity concept to estimate missing values supporting the objective of obtaining complete fuzzy preference relations with higher consistency levels. We use the concept of granular preference relations to form each missing value as a granule of information in place of a crisp number. This offers the flexibility that is required to estimate the missing information so that the consistency levels related to the complete fuzzy preference relations are as higher as possible.
KW - Consistency
KW - Fuzzy preference relation
KW - Group decision making
KW - Incomplete information
KW - Information granularity
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U2 - 10.1016/j.asoc.2019.105930
DO - 10.1016/j.asoc.2019.105930
M3 - Article
AN - SCOPUS:85075387572
SN - 1568-4946
VL - 86
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 105930
ER -