A multi-criteria framework for electric vehicle charging location selection using double hierarchy preferences and unknown weights

The adverse effects of traditional vehicles on the environment increase the demand for clean vehicles, such as electric vehicles (EVs). The correct positioning of the charging points for such vehicles certainly promotes the acceptance and spread of EVs. Indeed, selecting optimal locations for electric vehicle charging stations (EVCS) is crucial for shaping a sustainable future. This study introduces an integrated methodology under a double hierarchy linguistic context with a criteria importance through an inter-criteria correlation (CRITIC) technique for experts' reliability determination, an attitudinal Cronbach's method for criteria weight estimation, and a novel multi-criteria technique considering the compromise ranking of alternatives from distance to ideal solution (CRADIS) formulation for optimal EVCS location selection. Based on the results, criteria such as service capability, ecological impact, land cost, and traffic density are the most crucial, with Manapparai, India, as the optimal location for a new EVCS construction. Further, a crucial finding is that the social dimension is substantial compared to the economy and environment dimensions for EVCS location selection. The novelty of the paper is that (i) uncertainty and expression of choices in natural language form for locations for EVCS are modeled effectively using a double hierarchy structure, (ii) experts' weights are obtained methodically by considering hesitation and interactions among experts, (iii) interdependencies among criteria and importance of experts are considered during criteria weight determination, and (iv) locations are ranked by not only considering criteria type but also resembles closely to the human-centric decision process. A detailed sensitivity analysis is further conducted to prove the proposed approach's effectiveness and stability. In the context of sustainable transportation, the work could contribute to the relevant literature through a powerful decision-making tool.