Consensus Modelling on Interval-Valued Fuzzy Preference Relations with Normal Distribution

Abstract

This paper investigates the consensus decision making problem of the interval-valued fuzzy preference relation with distribution characteristics. The proposed group consensus decision making model is constructed by considering the scenarios in which the DMs are respectively equally and non-equally weighted and the DM’s preferences are randomly distributed. The goal is to find the minimum deviation between an ideal DM and all individual DMs. Accordingly, the objective function is the maximum consensus with a certain probability. The interactive process simulates the DM’s uncertainty judgment information more effectively. The Pareto optimization solution derived using a genetic algorithm and Monte Carlo approach is closer to reality. In the process of solving the model in this study, the essence of the Monte Carlo simulation method is an interactive process involving decision information. Therefore, this study provides a reference for the framework and optimization algorithm of the interactive decision support system.

Keywords

• Group decision making (GDM)
• interval-valued fuzzy preference relation
• normal distribution
• genetic algorithm (GA)
• group consensus