Abstract

AbstractIn this paper we propose an efficient approach for group multi-criteria decision making (MCDM) based on intuitionistic multi-fuzzy set (IMFS). First we construct intuitionistic multi-fuzzy matrices for decision makers with respect to the criteria (attributes) of the alternatives. Based on intuitionistic multi-fuzzy matrices, we construct the aggregated intuitionistic multi-fuzzy matrix using the proposed intuitionistic multi-fuzzy weighted averaging (IMFWA) operator. Then we use Hamming distance and Euclidean distance measurements in the context of IMFS between the aggregated matrix and the specified sample matrix to reach the optimal decision. This paper also presents score function and accuracy function of IMFS with an application to MCDM. Finally, a real-life case study related to heart disease diagnosis problem is provided to illustrate the advantage of the proposed approach.

Highlights

  • Intuitionistic fuzzy set (IFS) was introduced by Atanassov [1] and can be shown as a generalization and extension of Zadeh's fuzzy set theory [2]

  • An algorithm for IMFS-based GDM we develop an algorithm for an intuitionistic multi-fuzzy set-based group decision making method using Hamming distance measurement technique

  • We have proposed an algorithmic approach which combines the opinions of individual decision makers to produce an aggregated matrix by using a group multi-criteria decision making method

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Summary

Introduction

Intuitionistic fuzzy set (IFS) was introduced by Atanassov [1] and can be shown as a generalization and extension of Zadeh's fuzzy set theory [2]. As a generalization of multi-set, Yager [14] introduced the concept of multi-fuzzy set (MFS). MFS theory is an extension of theories of fuzzy sets [2], L-fuzzy sets [15], and intuitionistic fuzzy sets [16]. Group multi-criteria decision making requires certain methods to aggregate the opinions provided by different experts. Yager [17] proposed an interesting and well-grounded approach, namely the ordered weighted averaging (OWA) [17], which enabled aggregation of the variables in terms of their order in the set

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