MCGDM Approach Using the Weighted Hyperbolic Sine Similarity Measure of Neutrosophic (Indeterminate Fuzzy) Multivalued Sets for the Teaching Quality Assessment of Teachers

Authors

  • Mailing Zhao
  • Jun Ye

Abstract

A neutrosophic (indeterminate fuzzy) multivalued set (NMS) can be effectively described
by neutrosophic number sequences with identical or different neutrosophic numbers zi = μi + vi ⊆
[0, 1] (i = 1, 2, …, q) for μ, v  R and I ∈ [I- , I+]. Therefore, NMS is a stronger and more valuable tool
for describing indeterminate fuzzy multivalued information. In this article, we propose the
weighted hyperbolic sine similarity measure of NMSs to deal with the multi-criteria group decisionmaking (MCGDM) issue of teaching quality assessment with different indeterminate ranges of
decision makers. To do so, first according to the hyperbolic sine function, we propose a hyperbolic
sine similarity measure of NMSs and a weighted hyperbolic sine similarity measure of NMSs and
investigate their desirable properties. Second, we develop a MCGDM approach with some
indeterminate ranges in terms of the proposed weighted hyperbolic sine similarity measure of
NMSs. Lastly, an illustrative example on the teaching quality assessment of teachers is presented to
illustrate the applicability of the developed approach, then the developed approach is compared
with the existing related approach to reveal the effectiveness of the developed approach for the
teaching quality assessment of teachers in the environment of NMSs. 

Downloads

Download data is not yet available.

Downloads

Published

2022-02-01

Issue

Section

SI#1,2024: Neutrosophical Advancements And Their Impact on Research

How to Cite

Mailing Zhao, & Jun Ye. (2022). MCGDM Approach Using the Weighted Hyperbolic Sine Similarity Measure of Neutrosophic (Indeterminate Fuzzy) Multivalued Sets for the Teaching Quality Assessment of Teachers. Neutrosophic Sets and Systems, 48, 1-8. https://fs.unm.edu/nss8/index.php/111/article/view/3939

Most read articles by the same author(s)

1 2 > >>