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Soft consensus measures in group decision making using unbalanced fuzzy linguistic information

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Abstract

An important question in group decision-making situations is how to estimate the consensus achieved within the group of decision makers. Dictionary meaning of consensus is a general and unanimous agreement among a group of individuals. However, most of the approaches deal with a more realistic situation of partial agreement. Defining a partial agreement of decision makers as a consensus up to some degree, the following question is how to obtain that soft degree of consensus. To do so, different approaches, in which the decision makers express their opinions by using symmetrical and uniformly distributed linguistic term sets, have been proposed. However, there exist situations in which the opinions are represented using unbalanced fuzzy linguistic term sets, in which the linguistic terms are not uniform and symmetrically distributed around the midterm. The aim of this paper was to study how to adapt the existing approaches obtaining soft consensus measures to handle group decision-making situations in which unbalanced fuzzy linguistic information is used. In addition, the advantages and drawbacks of these approaches are analyzed.

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Acknowledgments

This Project was funded by King Abdulaziz University (KAU), under Grant No. (27-135-35/HiCi). The authors, therefore, acknowledge technical and financial support of KAU.

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Authors and Affiliations

  1. Department of Software Engineering and Computer Systems, Universidad Nacional de Educación a Distancia, 28040, Madrid, Spain

    F. J. Cabrerizo

  2. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia

    R. Al-Hmouz, A. Morfeq, A. S. Balamash & E. Herrera-Viedma

  3. Department of Social Services and Work, University of Granada, 18071, Granada, Spain

    M. A. Martínez

  4. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    E. Herrera-Viedma

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  1. F. J. Cabrerizo
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  2. R. Al-Hmouz
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  3. A. Morfeq
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  4. A. S. Balamash
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  6. E. Herrera-Viedma
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Correspondence to F. J. Cabrerizo.

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Communicated by V. Loia.

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Cabrerizo, F.J., Al-Hmouz, R., Morfeq, A. et al. Soft consensus measures in group decision making using unbalanced fuzzy linguistic information. Soft Comput 21, 3037–3050 (2017). https://doi.org/10.1007/s00500-015-1989-6

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