Affiliations: [a] School of Mathematical Sciences, Sichuan Normal University, Chengdu, P.R. China
| [b] School of Business, Sichuan Normal University, Chengdu, P.R. China
| [c] School of Finance, Southwestern University of Finance and Economics, Chengdu, P.R. China
Correspondence:
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Corresponding author. Yanfeng Guo, School of Finance, Southwestern University of Finance and Economics, Chengdu, 611130, P.R. China. E-mail: [email protected].
Abstract: The emergence of new energy electric vehicles (NEEV) can effectively reduce vehicle fuel consumption and alleviate the contradiction between fuel supply and demand. It has made great contributions to improving the atmospheric environment and promoting the development of environmental protection. However, the insufficient number of new energy electric vehicle charging stations (NEEVCSs) and unreasonable coverage areas have become obstacles to the large-scale promotion of new energy electric vehicles. Therefore, we build a multi-attribute decision making (MADM) model based on probabilistic double hierarchy linguistic weight Maclaurin symmetric mean (PDHLWMSM) operator and a MADM model based on probabilistic double hierarchy linguistic weight power Maclaurin symmetric mean (PDHLWPMSM) operator to select the best charging station construction point from multiple alternative sites. In addition, the model constructed in this paper is compared with the existing MADM models to verify the scientificity of the model proposed in this paper.
Keywords: Multiple attribute decision making (MADM), probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWMSM operator, PDHLWPMSM operator, new energy electric vehicle charging station (NEEVS)
