TY - JOUR
T1 - Optimal location selection for electric vehicle car-sharing stations using Fermatean fuzzy decision-making methodology
AU - Yildirim, Betul
AU - Ayyildiz, Ertugrul
AU - Aydin, Nezir
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/12/25
Y1 - 2024/12/25
N2 - This study aims to determine the optimal location for electric vehicle (EV) car-sharing stations by introducing a Fermatean fuzzy multi-criteria evaluation approach. Selecting the optimal location for EV car-sharing stations is crucial for maximizing accessibility, convenience, and user adoption, promoting sustainable urban mobility. By strategically placing stations, the study demonstrates how environmental impacts can be minimized, traffic congestion reduced, and the efficiency of urban transportation improved. The proposed methodology integrates the Fermatean Fuzzy Pivot Pairwise Relative Criteria Importance Assessment (FF-PIPRECIA) method for weighting criteria with the Fermatean Fuzzy IseKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR) method for ranking alternative locations, marking the first application of these techniques in combination. A decision matrix is constructed to standardize the evaluation of potential locations, enabling a structured comparison between alternatives. Furthermore, this study introduces the FF-PIPRECIA into decision-making literature, filling a gap by providing a robust tool for handling uncertainty in multi-criteria evaluations of sustainable transportation infrastructure. Key findings revealed that proximity to high-demand areas and energy infrastructure were among the most favorable criteria for selecting EV car-sharing locations. The method's effectiveness was validated by identifying optimal EV car-sharing locations contributing to sustainability and urban mobility goals. The findings offer valuable insights for urban planners and policymakers, enhancing the practical usability of multi-criteria decision-making methods in sustainable transportation planning.
AB - This study aims to determine the optimal location for electric vehicle (EV) car-sharing stations by introducing a Fermatean fuzzy multi-criteria evaluation approach. Selecting the optimal location for EV car-sharing stations is crucial for maximizing accessibility, convenience, and user adoption, promoting sustainable urban mobility. By strategically placing stations, the study demonstrates how environmental impacts can be minimized, traffic congestion reduced, and the efficiency of urban transportation improved. The proposed methodology integrates the Fermatean Fuzzy Pivot Pairwise Relative Criteria Importance Assessment (FF-PIPRECIA) method for weighting criteria with the Fermatean Fuzzy IseKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR) method for ranking alternative locations, marking the first application of these techniques in combination. A decision matrix is constructed to standardize the evaluation of potential locations, enabling a structured comparison between alternatives. Furthermore, this study introduces the FF-PIPRECIA into decision-making literature, filling a gap by providing a robust tool for handling uncertainty in multi-criteria evaluations of sustainable transportation infrastructure. Key findings revealed that proximity to high-demand areas and energy infrastructure were among the most favorable criteria for selecting EV car-sharing locations. The method's effectiveness was validated by identifying optimal EV car-sharing locations contributing to sustainability and urban mobility goals. The findings offer valuable insights for urban planners and policymakers, enhancing the practical usability of multi-criteria decision-making methods in sustainable transportation planning.
KW - Car-sharing
KW - Decision-making under uncertainty
KW - Electric vehicle
KW - Location selection
KW - Multi-criteria decision-making
KW - Sustainability
UR - http://www.scopus.com/inward/record.url?scp=85211111675&partnerID=8YFLogxK
U2 - 10.1016/j.jclepro.2024.144400
DO - 10.1016/j.jclepro.2024.144400
M3 - Article
AN - SCOPUS:85211111675
SN - 0959-6526
VL - 485
JO - Journal of Cleaner Production
JF - Journal of Cleaner Production
M1 - 144400
ER -