"Consensual Group-AHPSort: Applying consensus to GAHPSort in sustainable development and industrial engineering"
International Journal
Year of publication
2021
Volume
"152"
Pagination
"107013"
DOI
https://doi.org/10.1016/j.cie.2020.107013
Journal
"Computers & Industrial Engineering"
ISSN Number
0360-8352
URL
http://www.sciencedirect.com/science/article/pii/S0360835220306835
Labella, Á., et al. «"Consensual Group-Ahpsort: Applying Consensus To Gahpsort In Sustainable Development And Industrial Engineering"». "Computers & Industrial Engineering", 2021, p. "107013".
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Cuartil
Q1
A new visualization for preferences evolution in group decision making
Year of publication
2018
Conference Name
13th International FLINS conference
Volume
11
Pagination
235-242
Date Published
09/2018
Publisher
World Scientific
Place Published
Belfast (UK)
ISBN Number
978-981-3273-22-1
URL
https://www.worldscientific.com/doi/pdf/10.1142/9789813273238_0032
DOI
https://doi.org/10.1142/9789813273238_0032
Labella, Á., y L. Martínez. «A New Visualization For Preferences Evolution In Group Decision Making». 09/2018d. C. 13Th International Flins Conference, World Scientific, 2018, pp. 235-242.
International Conference
Documento
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AFRYCA 3.0: An Improved Framework for Consensus Analysis in Group Decision Making
Year of publication
2018
Conference Name
International Conference on Intelligent Decision Technologies 
Volume
97
Pagination
76-86.
Date Published
05/2018
Publisher
Springer, Cham
ISBN Number
978-3-319-92027-6
URL
https://link.springer.com/chapter/10.1007/978-3-319-92028-3_8
DOI
https://doi.org/10.1007/978-3-319-92028-3_8
Labella, Á., y L. Martínez. «Afryca 3.0: An Improved Framework For Consensus Analysis In Group Decision Making». 05/2018d. C. International Conference On Intelligent Decision Technologies , Springer, Cham, 2018, pp. 76-86.
International Conference
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Using Computing with Words for Managing Non-cooperative Behaviors in Large Scale Group Decision Making
Resumen
Normally, in group decision making problems, groups are composed by individuals or experts with different goals and points of view. For these reasons, they may adopt distinct behaviors in order to achieve their own aims. Nonetheless, in such problems in general, specially those demanding a certain degree of consensus, each expert should comply with a collaboration contract in order to find a common solution for the decision problem.When decision groups are small, all experts usually attempt to fulfill the collaboration contract. However, nowadays technologies such as social media allow to make consensus-driven decisions with larger groups, in which many experts are involved, hence the possibility that some of them try to break the collaboration contract might be greater. In order to prevent the group solution from being biased by these experts, it is necessary to detect and manage their non-cooperative behaviors in this kind of problems. Recent proposals in the literature suggest managing non-cooperative behavior by reducing the importance of expert opinions. These proposals present drawbacks such as, the inability of an expert to recover his/her importance if behavior improves; and the lack of experts behavior measures across the time. This chapter introduces a methodology based on fuzzy sets and computing with words, with the aim of identifying and managing those experts whose behavior does not contribute to reach an agreement in consensus reaching processes. Such a methodology is characterized by allowing the importance recovery of experts and taking into account the evolution of their behavior across the time.
Year of Publicaion
2015
Volume
10
Pagination
97-121
Publisher
Springer International Publishing
ISBN Number
978-3-319-16828-9
URL
http://dx.doi.org/10.1007/978-3-319-16829-6_5
DOI
10.1007/978-3-319-16829-6_5
Quesada, F. J., et al. Using Computing With Words For Managing Non-Cooperative Behaviors In Large Scale Group Decision Making. Springer International Publishing, 2015, pp. 97-121.
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