• The novel hybrid MCDM approach is developed for more efficient decision. • Selecting the best electric vehicle among the available alternatives is considered. • All the alternative electric vehicles are ranked based on appraisal scores. • The proposed approach makes the best compromise solution of complex problem. This study proposes a hybrid multi-criteria decision-making method for purchasing electric vehicles, by combining the analytic hierarchy process (AHP) with the evaluation based on distance from the average solution (EDAS). AHP assigns criteria weights and derives the scores for qualitative criteria, while the technical specifications of the electric vehicles are considered as the decision data for quantitative criteria. EDAS is employed to rank the electric vehicles and determine the best option, based on the appraisal scores that indicate the overall performance of each vehicle, relative to that of the average solution. The proposed method is implemented for making electric-vehicle purchase decisions, in Thailand, based on real-world data and decision-makers’ perspectives. The findings reveal that the proposed method can make effective electric-vehicle purchase decisions. It straightforwardly elicits the decision-makers’ needs through criteria weights; the decision-makers place high emphasis on the comfort criterion, with a weight of 0.176. Furthermore, the proposed method successfully ranks all electric vehicles and appropriately identifies the best electric vehicle, with an appraisal score of 0.749, which demonstrates superior overall performance across all decision-making attributes. The theoretical novelty of this study lies in the ability of the proposed method to reflect the decision-makers’ judgments, analyze both quantitative and qualitative criteria, and systematically derive the best decision. This study offers practical benefits to consumers, manufacturers, and government agencies, by supporting electric-vehicle purchase decisions, guiding the development of new electric-vehicle models, and informing policy formulations to promote electric-vehicle adoption.
Koohathongsumrit et al. (Sun,) studied this question.