Applying MCDM methods for electric vehicle selection: a comparative study between CRADIS and PIV methods

The global automotive industry is undergoing a significant transformation towards electric vehicles to significantly reduce carbon emissions and contribute to a greener planet. The proliferation of EVs is not only a trend but also an urgent solution to address climate change. In the context of a world striving for sustainable development, selecting the right electric vehicle becomes a crucial decision for consumers as one of the urgent solutions. This study employs two methods, CRADIS and PIV, to rank ten electric vehicle models and identify the optimal choice. Each vehicle is characterized by seven criteria, and the weighting of these criteria is determined using four methods: Entropy, LOPCOW, WENSLO, and combined weighting. This research also compares the CRADIS and PIV methods based on various similarity measures such as SPE (Spearman's coefficient), WPSE (Weighted Spearman's coefficient), RS (Rank Similarity coefficient), and KE (Kendall's coefficient). The results indicate a slight advantage of the PIV method over the CRADIS method.