Consensus and Consistency Level Optimization of Fuzzy Preference Relation: A Soft Computing Approach

TL;DR

This paper introduces a simulated annealing-based soft computing method to optimize consistency and consensus levels in fuzzy preference relations for group decision making, enhancing opinion agreement and preference quality without moderator intervention.

Contribution

It proposes a novel optimization approach for fuzzy preference relations that improves consistency and consensus levels in group decision making.

Findings

Effective optimization of consensus and consistency levels achieved.

Method allows experts to modify preferences independently.

Improves decision quality by enhancing agreement among experts.

Abstract

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.