Consensus and Consistency Level Optimization of Fuzzy Preference
  Relation: A Soft Computing Approach
  In group decision making (GDM) problems fuzzy preference relations (FPR) are
widely used for representing decision makers' opinions on the set of
alternatives. In order to avoid misleading solutions, the study of consistency
and consensus has become a very important aspect. This article presents a
simulated annealing (SA) based soft computing approach to optimize the
consistency/consensus level (CCL) of a complete fuzzy preference relation in
order to solve a GDM problem. Consistency level indicates as expert's
preference quality and consensus level measures the degree of agreement among
experts' opinions. This study also suggests the set of experts for the
necessary modifications in their prescribed preference structures without
intervention of any moderator.
