An Integrated AHP-MABAC Approach for Electric Vehicle Selection

Abstract

Electric vehicles (EVs) play a vital role in reducing greenhouse gas emissions resulting in a reduction of the problem of global warming. Many of the countries are towards the deployment of electric vehicles contributing to sustainable development. Despite the rapid expansion of electric vehicles worldwide, the selection of the best alternative among available variants of EVs is difficult. This paper aims to present an integrated approach of the analytic hierarchy process (AHP) and multi-attributive border approximation area comparison (MABAC) method as a multi-criterion decision-making tool (MCDM) for the selection and ranking of the best alternative of the electric vehicle. The AHP method is used to obtain weight coefficients of criteria, and the selection of EVs alternatives is evaluated using the MABAC method. For the study, a sample of six feasible alternatives has been considered. The novelty of this work lies in the combined approach of AHP-MABAC, which has not been applied previously in this domain. This study contributes by providing a real preference for a comprehensive set of selection criteria and assessing the best alternative from available electric vehicles.