计算机科学
群体决策
过程(计算)
选择(遗传算法)
相似性(几何)
比例(比率)
聚类分析
模糊逻辑
质量(理念)
偏爱
运筹学
人工智能
数据挖掘
机器学习
工程类
物理
量子力学
政治学
法学
哲学
认识论
经济
图像(数学)
微观经济学
操作系统
作者
Ying‐Ming Wang,Shi-Fan He,Diego García‐Zamora,Xiaohong Pan,Luis Martı́nez
标识
DOI:10.1016/j.eswa.2022.119107
摘要
The development of New Energy Vehicles (NEVs) has contributed to the alleviation of environmental pollution in the transportation sector. Although NEVs have some advantages, such as energy saving, being environmentally friendly, and low noise, they are restricted by their cruising range or recharging-related problems. To minimize such disadvantages, an optimal plan for the charging station site is necessary. This paper proposes a Large-Scale Group Decision-Making (LSGDM) method to select the best location for such charging stations. This method involves a large group of experts providing their preferences according to their knowledge and background. To facilitate the elicitation, our method uses Hesitant Fuzzy Linguistic Term Sets (HFLTS) and Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) model to improve the elicitation process, guarantee precise computing processes and obtain interpretable results. A social network is then constructed based on experts’ preference similarity and trust relationships, which reflects both their relationship and its strength simultaneously. Afterwards, a social analysis-based clustering process groups the experts, and a Three-Way Decision (TWD)-based Consensus Reaching Process (CRP) is introduced to improve the group’s agreement. Finally, a selection of charging station site case studies is conducted, and a comparative analysis is carried out to illustrate the quality of the proposal.
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