Consensus and Consistency Level Optimization of Fuzzy Preference Relation: A Soft Computing Approach
This work addresses the need for reliable decision-making in group settings by enhancing the quality of expert preferences and agreement, though it appears incremental as it applies an existing optimization technique to a known bottleneck in fuzzy preference relations.
The paper tackles the problem of improving consistency and consensus in group decision making using fuzzy preference relations by proposing a simulated annealing-based soft computing approach to optimize the consistency/consensus level, resulting in a method that identifies experts needing modifications without moderator intervention.
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.