A consensus model for group decision making problems with unbalanced fuzzy linguistic information
Identificadores
URI: http://hdl.handle.net/10481/5676Metadatos
Mostrar el registro completo del ítemMateria
Fuzzy linguistic modeling Unbalanced linguistic term set Group decision making Consensus
Fecha
2009Referencia bibliográfica
F.J. Cabrerizo, S. Alonso, E. Herrera-Viedma, A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information. International Journal of Information Technology & Decision Making, 8:1 (2009), 109-131
Resumen
Most group decision making problems based on linguistic approaches use symmetrically
and uniformly distributed linguistic term sets to express experts opinions. However,
there exist problems whose 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 a consensus model for group decision making
problems with unbalanced fuzzy linguistic information. This consensus model is based on
both a fuzzy linguistic methodology to deal with unbalanced linguistic term sets and two
consensus criteria, consensus degrees and proximity measures. To do so, we use a new
fuzzy linguistic methodology improving another approach to manage unbalanced fuzzy
linguistic information,1 which uses the linguistic 2-tuple model as representation base
of unbalanced fuzzy linguistic information. In addition, the consensus model presents a
feedback mechanism to help experts for reaching the highest degree of consensus possible.
There are two main advantages provided by this consensus model. Firstly, its ability to
cope with group decision making problems with unbalanced fuzzy linguistic information
overcoming the problem of finding different discrimination levels in linguistic term sets.
And, secondly, it supports the consensus process automatically, avoiding the possible
subjectivity that the moderator can introduce in this phase.