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<p>The transportation sector is a leading contributor to global carbon emissions, reinforcing the urgency of sustainable alternatives such as EVs. Despite their potential, the high cost of EVs and the development of charging infrastructure remain significant barriers, making the optimal siting of charging stations vital for efficiency and service quality. To address the uncertainty inherent in this process, this study proposes the PC<i>q</i>-ROF model. By incorporating probability information with CVMG and CVNMG, the model extends conventional fuzzy set theories and enables a more comprehensive representation of uncertainty. Building on this foundation, novel aggregation operators namely the PC<i>q</i>-ROFWA and PC<i>q</i>-ROFWG operators are introduced, and their theoretical properties are rigorously analyzed. A MADM algorithm is then developed to assess and prioritize candidate locations for EV charging stations. The applicability and effectiveness of the proposed approach are demonstrated through a real-world case study. Comparative results highlight its advantages over existing methods in addressing multidimensional uncertainty, improving decision precision, and supporting robust prioritization. The findings establish the PC<i>q</i>-ROF model as a flexible and reliable tool to guide sustainable EV charging station planning.</p>

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Towards sustainable transportation: a probabilistic complex model for optimizing electric vehicle charging station site selection

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Pairote Yiarayong

摘要

The transportation sector is a leading contributor to global carbon emissions, reinforcing the urgency of sustainable alternatives such as EVs. Despite their potential, the high cost of EVs and the development of charging infrastructure remain significant barriers, making the optimal siting of charging stations vital for efficiency and service quality. To address the uncertainty inherent in this process, this study proposes the PCq-ROF model. By incorporating probability information with CVMG and CVNMG, the model extends conventional fuzzy set theories and enables a more comprehensive representation of uncertainty. Building on this foundation, novel aggregation operators namely the PCq-ROFWA and PCq-ROFWG operators are introduced, and their theoretical properties are rigorously analyzed. A MADM algorithm is then developed to assess and prioritize candidate locations for EV charging stations. The applicability and effectiveness of the proposed approach are demonstrated through a real-world case study. Comparative results highlight its advantages over existing methods in addressing multidimensional uncertainty, improving decision precision, and supporting robust prioritization. The findings establish the PCq-ROF model as a flexible and reliable tool to guide sustainable EV charging station planning.