Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Thilagasree, Chakkarapani Sumathi; Jayakumar, Thippan; Ramya, Lakshmanaraj; Suvitha, Krishnan; Pamucar, Dragan; Pedrycz, Witold; Kureethara, Joseph Varghese Title A name given to the resource Hesitant bipolar fuzzy set-based decision system for electric vehicle charging station location planning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Sustainable Energy, Grids and Networks;Volume;43;Issue;;Article No.;101756; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.segan.2025.101756" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.segan.2025.101756</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105007548517?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105007548517?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Thilagasree C.S., Department of Mathematics, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, India; Jayakumar T., Department of Mathematics, Sri Ramakrishna Mission Vidyalaya College of Arts and Science, Coimbatore, India; Ramya L., Department of Mathematics, Bharathiar University, Coimbatore, 641 046, India; Suvitha K., Centre for Nonlinear Systems, Chennai Institute of Technology, Tamilnadu, Chennai, India; Pamucar D., Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong, South Korea, Yuan Ze University, College of Engineering, Department of Industrial Engineering and Managemente, Taoyuan City, Taiwan, Industrial Engineering Department, Faculty of Engineering and Natural Sciences, ?stinye University, ?stanbul, Turkey; Pedrycz W., Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada; Kureethara J.V., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, India Description An account of the resource The selection of electric vehicle (EV) charging station locations is a critical challenge that significantly affects the growth and acceptance of the EV industry. As EVs offer a sustainable solution to fossil fuel depletion and environmental pollution, identifying optimal charging station sites involves dealing with uncertain, inconsistent, and conflicting criteria. To address these challenges, this paper presents an innovative decision-making framework based on Hesitant Bipolar-Valued Fuzzy Sets (HBVFSs), which account for both positive and negative hesitant membership values to better model uncertainty in expert judgments. A novel hybrid Multi-Criteria Decision-Making (MCDM) technique is proposed, combining the Step-wise Weight Assessment Ratio Analysis (SWARA) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA) methods to determine robust criteria weights within the HBVFS environment. The Preference Ranking Organization METHod for Enrichment Evaluation II (PROMETHEE-II) is employed for the final site ranking. This integrated approach enables a more comprehensive and reliable evaluation of potential locations by incorporating both qualitative and quantitative factors. The proposed methodology has practical applications in real-world infrastructure planning and supports more resilient decision-making in sustainable transportation networks. The results demonstrate the model's effectiveness and adaptability in addressing the site selection problem under uncertainty. 2025 Elsevier Ltd Subject The topic of the resource Bipolar fuzzy set; Electric vehicle; Hesitant fuzzy set; Multi-criteria decision-making; Site selection Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 23524677; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online