A Granular Consensus Model Based on Intuitionistic Reciprocal Preference Relations and Minimum Adjustment for Multi-Criteria Group Decision Making
- 10.2991/asum.k.210827.040How to use a DOI?
- Consensus, Granular computing, Intuitionistic reciprocal preference relations, Minimum adjustment, Multi-criteria group decision making
When a group of individuals try to collectively make a decision, it is important that all of them accept the decision adopted. It means, to improve consensus, some adjustments could be inevitably performed to the initial assessments given by the individuals. To do it, several models have been recently developed from the viewpoint of the granular computing paradigm. However, the models dealing with intuitionistic reciprocal preference relations do not consider that the modified assessments could be very different from the initial ones. The aim of this work is to develop a model based on the granular computing paradigm that tries to increase the consensus at the same time that tries to reduce the dissimilarity between the original assessments and the adjusted ones. In addition to it, this model is able to deal with multi-criteria group decision making problems.
- © 2021, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Francisco Javier Cabrerizo AU - José Ramón Trillo AU - Juan Antonio Morente-Molinera AU - Sergio Alonso AU - Enrique Herrera-Viedma PY - 2021 DA - 2021/08/30 TI - A Granular Consensus Model Based on Intuitionistic Reciprocal Preference Relations and Minimum Adjustment for Multi-Criteria Group Decision Making BT - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP) PB - Atlantis Press SP - 298 EP - 305 SN - 2589-6644 UR - https://doi.org/10.2991/asum.k.210827.040 DO - 10.2991/asum.k.210827.040 ID - Cabrerizo2021 ER -