Specific Types of q-Rung Picture Fuzzy Yager Aggregation Operators for Decision-Making

Authors

Peide Liu^{1, *,} , Gulfam Shahzadi², Muhammad Akram^2,

¹School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, Shandong, China

²Department of Mathematics, University of the Punjab, New Campus, Lahore, Pakistan

^*Corresponding author. Email: peide.liu@gmail.com

DOI

10.2991/ijcis.d.200717.001How to use a DOI?

Keywords

q-rung picture fuzzy numbers; Yager operators; Arithmetic; Geometric; Multi-attribute decision-making problems

Abstract

q-rung picture fuzzy sets can handle complex fuzzy and impression information by changing a parameter q based on the different hesitation degree, and Yager operator is a useful aggregation technology that can control the uncertainty of valuating data from some experts and thus get intensive information in the process of decision-making. Thus, in this paper, we develop specific types of operators, namely, q-rung picture fuzzy Yager weighted average, q-rung picture fuzzy Yager ordered weighted average, q-rung picture fuzzy Yager hybrid weighted average, q-rung picture fuzzy Yager weighted geometric, q-rung picture fuzzy Yager ordered weighted geometric and q-rung picture fuzzy Yager hybrid weighted geometric operators. We propose q-rung picture fuzzy Yager aggregation operators to handle multiple attribute decision-making problems in a modernize way. Moreover, we discuss the effect of parameter on the decision-making results. To demonstrate the superiority and advantage of our proposed method, a comparison with existing methods is presented.

Copyright

© 2020 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Peide Liu
AU  - Gulfam Shahzadi
AU  - Muhammad Akram
PY  - 2020
DA  - 2020/08/01
TI  - Specific Types of q-Rung Picture Fuzzy Yager Aggregation Operators for Decision-Making
JO  - International Journal of Computational Intelligence Systems
SP  - 1072
EP  - 1091
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200717.001
DO  - 10.2991/ijcis.d.200717.001
ID  - Liu2020
ER  -