Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making

Yanru Zhong; Hong Gao; Xiuyan Guo; Yuchu Qin; Meifa Huang; Xiaonan Luo

doi:10.1371/journal.pone.0222007

Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers for multiple attribute group decision making

PLoS One. 2019 Oct 22;14(10):e0222007. doi: 10.1371/journal.pone.0222007. eCollection 2019.

Authors

Yanru Zhong¹, Hong Gao¹, Xiuyan Guo¹, Yuchu Qin², Meifa Huang³, Xiaonan Luo¹

Affiliations

¹ Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics, Guilin University of Electronic Technology, Guilin, PR China.
² School of Computing and Engineering, University of Huddersfield, Huddersfield, United Kingdom.
³ School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, PR China.

Abstract

In this paper, a set of Dombi power partitioned Heronian mean operators of q-rung orthopair fuzzy numbers (qROFNs) are presented, and a multiple attribute group decision making (MAGDM) method based on these operators is proposed. First, the operational rules of qROFNs based on the Dombi t-conorm and t-norm are introduced. A q-rung orthopair fuzzy Dombi partitioned Heronian mean (qROFDPHM) operator and its weighted form are then established in accordance with these rules. To reduce the negative effect of unreasonable attribute values on the aggregation results of these operators, a q-rung orthopair fuzzy Dombi power partitioned Heronian mean operator and its weighted form are constructed by combining qROFDPHM operator with the power average operator. A method to solve MAGDM problems based on qROFNs and the constructed operators is designed. Finally, a practical example is described, and experiments and comparisons are performed to demonstrate the feasibility and effectiveness of the proposed method. The demonstration results show that the method is feasible, effective, and flexible; has satisfying expressiveness; and can consider all the interrelationships among different attributes and reduce the negative influence of biased attribute values.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Decision Making*
Fuzzy Logic*
Group Processes
Humans
Linguistics / statistics & numerical data

Grants and funding

This work was supported by No. 61562016 and No. AA18118039-2 to YRZ, http://www.nsfc.gov.cn/english/site_1/; No. 51765012 to MFH, http://www.nsfc.gov.cn/english/site_1/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.