The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Le problème de prise de décision linguistique en groupe multicritère (MCGDM) implique divers types d’incertitudes. Pour résoudre ce problème, une nouvelle méthode linguistique MCGDM combinant modèle cloud et théorie des preuves est donc proposée. Le modèle cloud est d'abord utilisé pour gérer le flou et le caractère aléatoire du concept linguistique, en prenant en compte à la fois le niveau moyen et le degré de fluctuation du concept linguistique. Ainsi, une méthode est présentée pour transformer les variables linguistiques en nuages, puis un nuage synthétique pondéré asymétrique est proposé pour agréger les nuages des décideurs sur chaque critère. De plus, la théorie des preuves est utilisée pour gérer l'imprécision et l'incomplétude de l'évaluation du groupe, avec le degré de croyance et le degré d'ignorance. Par conséquent, la conversion du cloud en degré de croyance est étudiée, puis l'algorithme de raisonnement probant est adopté pour agréger les valeurs des critères. Enfin, l’utilité moyenne est appliquée pour classer les alternatives. Un exemple numérique, donné pour confirmer la validité et la faisabilité, montre également que la méthode proposée peut tirer parti du modèle de nuage et de la théorie des preuves pour traiter efficacement les incertitudes causées à la fois par le concept linguistique et par l'évaluation de groupe.
Jian ZHOU
Nanjing University of Posts and Telecommunications,Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
Chong HAN
Nanjing University of Posts and Telecommunications,Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
Lijuan SUN
Nanjing University of Posts and Telecommunications,Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
Fu XIAO
Nanjing University of Posts and Telecommunications,Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
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Jian ZHOU, Chong HAN, Lijuan SUN, Fu XIAO, "Linguistic Multi-Criteria Group Decision-Making Method Combining Cloud Model and Evidence Theory" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 4, pp. 845-855, April 2019, doi: 10.1587/transinf.2018EDP7288.
Abstract: The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7288/_p
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@ARTICLE{e102-d_4_845,
author={Jian ZHOU, Chong HAN, Lijuan SUN, Fu XIAO, },
journal={IEICE TRANSACTIONS on Information},
title={Linguistic Multi-Criteria Group Decision-Making Method Combining Cloud Model and Evidence Theory},
year={2019},
volume={E102-D},
number={4},
pages={845-855},
abstract={The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.},
keywords={},
doi={10.1587/transinf.2018EDP7288},
ISSN={1745-1361},
month={April},}
Copier
TY - JOUR
TI - Linguistic Multi-Criteria Group Decision-Making Method Combining Cloud Model and Evidence Theory
T2 - IEICE TRANSACTIONS on Information
SP - 845
EP - 855
AU - Jian ZHOU
AU - Chong HAN
AU - Lijuan SUN
AU - Fu XIAO
PY - 2019
DO - 10.1587/transinf.2018EDP7288
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2019
AB - The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.
ER -