Managing Multi-Granular Linguistic Distribution Assessments in
  Large-Scale Multi-Attribute Group Decision Making
  Linguistic large-scale group decision making (LGDM) problems are more and
more common nowadays. In such problems a large group of decision makers are
involved in the decision process and elicit linguistic information that are
usually assessed in different linguistic scales with diverse granularity
because of decision makers' distinct knowledge and background. To keep maximum
information in initial stages of the linguistic LGDM problems, the use of
multi-granular linguistic distribution assessments seems a suitable choice,
however to manage such multigranular linguistic distribution assessments, it is
necessary the development of a new linguistic computational approach. In this
paper it is proposed a novel computational model based on the use of extended
linguistic hierarchies, which not only can be used to operate with
multi-granular linguistic distribution assessments, but also can provide
interpretable linguistic results to decision makers. Based on this new
linguistic computational model, an approach to linguistic large-scale
multi-attribute group decision making is proposed and applied to a talent
selection process in universities.
