Multiattribute Group Decision-Making Method Using a Genetic K-Means Clustering Algorithm

Liu Qingguo,Wu Jian,Li Yaxiong,Liu Xinxue

doi:10.1155/2020/8313892

Abstract

Multiattribute group decision-making (MAGDM) problems are characterized by the large number, uneven levels, and bounded rationality of decision-makers; multiple attributes and fuzziness of decision problems; and complex group behaviours. Considering these characteristics, we propose a MAGDM method using a genetic K-means clustering algorithm. First, we briefly review the traditional multiattribute decision-making method based on prospect theory (PT) and trapezoidal intuitionistic fuzzy numbers (TrIFNs) under the premise of human bounded rationality and uncertain decision environment. Then, the aggregation model of decision information given by decision-makers is established using the genetic K-means algorithm in order to determine optimal clustering results. Each clustering center represents decision information given by decision-makers in each cluster, and the weight of each clustering center is determined by considering the tightness of decision information within a cluster and the count of decision-makers in each cluster. Finally, the ranking of schemes is obtained according to the comparison rules of TrIFNs. We design comparison simulation experiments to test the proposed method and the simulation results demonstrate that the proposed method is apprehensible and feasible to solve MAGDM problems.

Highlights

Multiattribute group decision-making (MAGDM) is a problem in which multiple decision-makers make decisions on multiple schemes under the premise of multiple attributes [1,2,3]. e basis of the MAGDM problem is multiattribute decision-making (MADM)
Considering the above shortcomings of the MAGDM methods based on clustering algorithms, we propose a MAGDM method using a genetic K-means clustering algorithm on the basis of Prospect theory (PT) and trapezoidal intuitionistic fuzzy numbers (TrIFNs). is paper is organized as follows
Brief Review of the Prospect Theory and Intuitionistic Trapezoidal Fuzzy Number e traditional method to solve MADM problems is based on PT and TrIFNs, which lays the foundation of our proposed MADM method

Summary

Introduction

Multiattribute group decision-making (MAGDM) is a problem in which multiple decision-makers make decisions on multiple schemes under the premise of multiple attributes [1,2,3]. e basis of the MAGDM problem is multiattribute decision-making (MADM). Under the premise of human bounded rationality and uncertain decision-making environment, the representative papers that systematically study MAGDM methods are as follows: Li and Chen [19] proposed a novel TOPSIS based on PT and TrIFNs, and the aggregation operator and ranking strategy can effectively obtain the final decision scheme. 2. Brief Review of the Prospect Theory and Intuitionistic Trapezoidal Fuzzy Number e traditional method to solve MADM problems is based on PT and TrIFNs, which lays the foundation of our proposed MADM method. E values of attribute and weight are given by decisionmakers in the form of TrIFN

Proposed MAGDM Method

Simulations and Analyses of Results

Method

Conclusion and Further Research