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Moreno-Rodriguez J.M.,University of Murcia | Cabrerizo F.J.,Distance Learning University of Spain | Herrera-Viedma E.,University of Granada
Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 | Year: 2010

Healthcare organisations using the European Foundation for Quality Management (EFQM) Excellence Model for self-assessment have found an opportunity to work more effectively and a powerful driver for improvement. Nevertheless, when these organisations address self-assessment processes for the first time the initial effort needed presents many difficulties. The aim of this paper is to offer a consensus support methodology based on fuzzy logic under a linguistic approach that would undoubtedly contribute to conduct self-assessment processes with questionnaires. We assume qualitative evaluation, through linguistic labels, to facilitate the individual responses, and we use the concept of fuzzy majority to calculate the measures which guide the consensus reaching process. © 2010 IEEE. Source


Cabrerizo F.J.,Distance Learning University of Spain | Moreno J.M.,University of Murcia | Perez I.J.,University of Granada | Herrera-Viedma E.,University of Granada
Soft Computing | Year: 2010

Two processes are necessary to solve group decision making problems: a consensus process and a selection process. The consensus process is necessary to obtain a final solution with a certain level of agreement between the experts, while the selection process is necessary to obtain such a final solution. Clearly, it is preferable that the set of experts reach a high degree of consensus before applying the selection process. In order to measure the degree of consensus, different approaches have been proposed. For example, we can use hard consensus measures, which vary between 0 (no consensus or partial consensus) and 1 (full consensus), or soft consensus measures, which assess the consensus degree in a more flexible way. The aim of this paper is to analyze the different consensus approaches in fuzzy group decision making problems and discuss their advantages and drawbacks. Additionally, we study the future trends. © 2009 Springer-Verlag. Source


Herrera-Viedma E.,University of Granada | Cabrerizo F.J.,Distance Learning University of Spain | Perez I.J.,University of Granada | Cobo M.J.,University of Granada | And 2 more authors.
Studies in Fuzziness and Soft Computing | Year: 2011

In Group Decision Making (GDM) the automatic consensus models are guided by different consensus measures which usually are obtained by aggregating similarities observed among experts' opinions. Most GDM problems based on linguistic approaches use symmetrically and uniformly distributed linguistic term sets to express experts' opinions.However, there exist problemswhose assessments need to be represented by means of unbalanced linguistic term sets, i.e., using term sets which are not uniformly and symmetrically distributed. The aim of this paper is to present different Linguistic OWA Operators to compute the consensus measures in consensusmodels for GDMproblems with unbalanced fuzzy linguistic information. © 2011 Springer-Verlag Berlin Heidelberg. Source


Perez I.J.,University of Granada | Cabrerizo F.J.,Distance Learning University of Spain | Alonso S.,University of Granada | Herrera-Viedma E.,University of Granada
IEEE Transactions on Systems, Man, and Cybernetics: Systems | Year: 2014

In the literature, we find that the consensus models proposed for group decision making problems are guided by consensus degrees and/or similarity measures and/or consistency measures [1]. When we work in heterogeneous group decision making frameworks, we have importance degrees associated with the experts by expressing their different knowledge levels on the problem. Usually, the importance degrees are applied in the weighted aggregation operators developed to solve the decision situations. In this paper, we study another application possibility, i.e., to use heterogeneity existing among experts to guide the consensus model. Thus, the main goal of this paper is to present a new consensus model for heterogeneous group decision making problems guided also by the heterogeneity criterion. It is also based on consensus degrees and similarity measures, but it presents a new feedback mechanism that adjusts the amount of advice required by each expert depending on his/her own relevance or importance level. © 2013 IEEE. Source


Perez I.J.,University of Granada | Cabrerizo F.J.,Distance Learning University of Spain | Alonso S.,University of Granada | Herrera-Viedma E.,University of Granada
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Usually, in a group decision context, the importance level, confidence degree and amount of knowledge are very different among individuals. So, when all the individuals have to reach agreement, is quite important to model these kind of features in order to get more appropriate decisions. Last related works are focussed in the selection process to model the importance of the experts, but such approach, under some circumstances, can behave badly. In this contribution, we present a new adaptive consensus reaching model specifically designed to undertake group decision making situations in which the experts have different importance or confidence levels. © 2010 Springer-Verlag Berlin Heidelberg. Source

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