On extended power geometric operator for proportional hesitant fuzzy linguistic large-scale group decision-making

Abstract

The unduly low or high data are commonly regarded as the outliers in the classical power geometric operator. However, in many cases, these types of data may be significantly important to the aggregated results. This study aims at expanding the practical application scope of the power geometric operator and then utilizing it to develop a proportional hesitant fuzzy linguistic large-scale group decision-making (LSGDM) model. The extended power geometric (EPG) operator is first introduced, in which these outliers can be distinguished as sufficiently important or “false/biased” data in accordance with the decision-making context. Several useful properties and application characteristics of the EPG operator are investigated. Subsequently, the proportional hesitant fuzzy linguistic normalized Manhattan distance is proposed, and it forms a basic concept to the construction of the proportional hesitant fuzzy linguistic extended power geometric (PHFLEPG) operator. Combined with the clustering model for decision makers, a PHFLEPG-operator-based consensus reaching approach is provided to simplify and rationalize the decision-making process. Furthermore, the comprehensive LSGDM result is derived in the use of the PHFLEPG operator. Eventually, a case study on regulatory capacity evaluation for the Civil Aviation Safety Regulatory Authority of China (CASRAC) is performed to validate the feasibility and effectiveness of the established LSGDM model.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Power geometric operator, Extended power geometric operator, Proportional hesitant fuzzy linguistic term sets, Large-scale group decision-making

Citation

Xiong, S-H., Zhu, C-Y., Chen, Z-S., Deveci, M., Chiclana, F. and Skibniewski, M.J. (2023) On extended power geometric operator for proportional hesitant fuzzy linguistic large-scale group decision-making. Information Sciences, 632, pp. 637-663

Rights

Research Institute

Institute of Artificial Intelligence (IAI)