A New Decision-Making Framework for Site Selection of Electric Vehicle Charging Station With Heterogeneous Information and Multigranular Linguistic Terms

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multigranular linguistic term sets. In this article, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multigranular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multigranular linguistic terms into interval-valued belief structures. Afterward, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning (ER) algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.