Simple Selection Index (SSI) Method in Electric Vehicle Selection for Logistics Companies

Abstract

The rapid development of electric vehicles (EVs) has encouraged various industrial sectors, including logistics, to transition from fossil fuel-based vehicles to more environmentally friendly solutions. While EVs offer advantages such as energy efficiency, reduced carbon emissions, and lower operating costs, selecting the right electric vehicle for a logistics company is not a straightforward task. The main challenge lies in the wide variety of available models, each with different technical and operational specifications. This complexity increases as companies must consider multiple criteria such as price, payload capacity, vehicle width, battery capacity, and cargo volume. Therefore, a systematic approach is needed to support decision-making. One commonly used approach is the Multi-Criteria Decision-Making (MCDM) method. This study introduces the Simple Selection Index (SSI) method, a newly developed MCDM approach designed as a simplified version of the Preference Selection Index (PSI) method. The novelty of SSI lies in its ability to eliminate complex steps such as the calculation of preference variation values and preference deviation scores, making the ranking process more concise and easier to apply—without compromising the accuracy of the results. The study aims to evaluate the performance of the SSI method in selecting the most suitable electric vehicle by directly comparing its results with those of the PSI method, using a dataset comprising four vehicle alternatives and five key criteria: price, payload, width, battery capacity, and cargo volume. The findings show that the SSI method produces an identical ranking to the PSI method, with EV-4 as the top recommendation and EV-1 as the second-best alternative. With its more efficient process, the SSI method holds strong potential for application in fast and straightforward multi-criteria decision-making scenarios.