Multi-Attribute Group Decision Making Based on Hesitant Fuzzy Sets, Topsis Method and Fuzzy Preference Relations

Abstract

Hesitant fuzzy sets (HFSs) are widely applied in pattern recognition, classification, clustering, and multiple attribute decision making. In order to get more accurate decision results, the order relation of HFSs is particularly important. In this paper, some defects of the existing order relations for HFSs are discussed. In order to solve these problems, by employing a distance measure and the TOPSIS method, we propose a new order relation extraction method based on a new additive consistency fuzzy preference relation for hesitant fuzzy elements (HFEs). Then, the proposed additive consistency fuzzy preference relation is applied to integrate group decision information. In multi-attribute group decision making (MAGDM), it is particularly important to ensure the consensus of the decision-makers (DMs), and the consistency of the decision process is the precondition for DMs to reach consensus. The proposed method can maintain the consistency of the decision process for MAGDM under hesitant fuzzy environments, so as to get the consensus of DMs, besides, it can overcome the limitations of the existing order relations for HFSs. At the end of this paper, a numerical example is used to illustrate the effectiveness and feasibility of the new approach, and some comparative analyses are given. The obtained results confirm the theoretical and numerical analyses and emphasize the advantages, which can ensure the consistency of the whole decision process and avoid the original decision information change and loss of the proposed method, so as to be more in line with the actual situation.

TOPSIS Method for Developing Supplier Selection with Probabilistic Linguistic Information

“…Baky [8] defined the interactive TOPSIS algorithms to deal with decision-making problems with multi-level non-linear multi-objective. Lan et al [9] developed the TOPSIS method with hesitant fuzzy sets, TOPSIS method and fuzzy preference relations. Yu et al [10] supplied the GDM sustainable supplier selection by utilizing the extended TOPSIS within interval-valued Pythagorean fuzzy environment.…”

Consistency Fuzzy Sets and a Cosine Similarity Measure in Fuzzy Multiset Setting and Application to Medical Diagnosis

“…One of the generalizations of fuzzy sets is the concept of hesitant fuzzy set (HFS) [8], which is characterized by a membership (truth) function that is a set of crisp values in [0, 1]. A HFS can model uncertain data better than a fuzzy set, thanks to its handy structure, so it has been frequently preferred by researchers to solve multicriteria (group) decision-making or multiperiod medical diagnosis problems [9][10][11][12]. However, the concept of HFS eliminates and ignores repetitive information because of the nature of the crisp sets.…”

Managing Consensus by Multi-Stage Optimization Models With Linguistic Preference Orderings and Double Hierarchy Linguistic Preferences

“…For achieving consensus among a group of experts, lots of consensus reaching approaches and models over preference relations have been developed in recent years (Del Moral et al, 2018;Gou et al, 2018a;Kacprzyk & Fedrizzi, 1988;Kamis et al, 2018;Lan et al, 2018;Liao et al, 2016;Morente-Molinera et al, 2018Zhang & Chen, 2019;Zhu & Xu, 2018). However, most of the consensus models are generally iterative models that utilize heuristics as their calculation tools such as some automatic improvement models (Gou et al, 2018a;Liao et al, 2016;Zhang & Chen, 2019) and feedback mechanism-based improvement models (Gou et al, 2018a;Liao et al, 2016;.…”