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TOPSIS Methods for Probabilistic Hesitant Fuzzy MAGDM and Application to Performance Evaluation of Public Charging Service Quality
Volume 34, Issue 2 (2023), pp. 317–336
Pub. online: 26 April 2023      Type: Research Article      Open accessOpen Access
Received
1 April 2022
Accepted
1 November 2022
Published
26 April 2023

Abstract

The performance evaluation of public charging service quality is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In this paper, an extended TOPSIS model is established to provide new means to solve the performance evaluation of public charging service quality. The TOPSIS method integrated with FUCOM method in probabilistic hesitant fuzzy circumstance is applied to rank the optional alternatives and a numerical example for performance evaluation of public charging service quality is used to test the newly proposed method’s practicability in comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. The main results of this paper: (1) a novel PHF-TOPSIS method is proposed; (2) the extended TOPSIS method is developed in the probabilistic hesitant fuzzy environment; (3) the FUCOM method is used to obtain the attribute weight; (4) the normalization process of the original data has adapted the latest method to verify the precision; (5) The built models and methods are useful for other selection issues and evaluation issues.

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Biographies

Qi Quan-Song
qiquansong@swupl.edu.cn

Q.-S. Qi is from Gaoping City, Shanxi Province, China. He studied at China University of Geosciences (Wuhan) from 2008 to 2011 and obtained his doctor’s degree in 2011. At present, he works in Southwest University of Political Science and Law. 15 papers were published, of which 4 were included in SCI. His research interests are included crisis management and risk governance.


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Keywords
multiple attributes group decision making (MAGDM) probabilistic hesitant fuzzy sets (PHFS) FUCOM method TOPSIS method performance evaluation public charging service quality

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