A robust framework for sustainable vehicle assessment: Integrating FN-TOPSIS with subjective weighting methods

This study presents a sustainable framework for vehicle assessment using FN-TOPSIS integrated with five weighting methods: RANCOM, AHP, LBWA, FUCOM, and BWM. By utilizing fuzzy normalization, the FN-TOPSIS model achieves greater stability in rankings and better alignment of criteria with goal-oriented objectives. The methodology was applied to a comprehensive dataset containing 7,385 vehicle alternatives, evaluated based on six key criteria, including environmental and technical factors such as CO 2 emissions and fuel efficiency. A comparative analysis of rankings using cosine similarity, WSC 2 , and the weighted Spearman rank correlation coefficient (r w ) revealed significant alignment and variability among the methods, highlighting the influence of weight determination on prioritization outcomes. The results demonstrate the effectiveness of FN-TOPSIS in integrating multidimensional criteria for sustainability, providing consistent and reliable rankings. Methods like AHP, FUCOM, and BWM prioritize dominant criteria, while LBWA adopts a more balanced approach, and RANCOM shows moderate variability. These findings emphasize the importance of selecting appropriate weighting methods tailored to the decision-making context. This framework offers a versatile tool for assessing vehicle sustainability, with potential applications in other fields, such as energy policy and urban planning. Future research could explore dynamic criteria, broader datasets, and stakeholder perspectives to further enhance the model’s adaptability and applicability in sustainability-focused decision-making.