Decision-Making on Green Vehicles Using a Hybrid Taxonomy Approach with Reference-Based Methods
Abstract
Nowadays, decision-making in the selection of green vehicles in Iran has become a major challenge, as choosing an appropriate vehicle has a significant impact on fuel consumption, air pollution, and environmental protection. Therefore, the use of suitable Multi-Criteria Decision-Making (MCDM) algorithms in this field appears to be essential. This study aims to analyze and propose an effective and efficient approach for decision-making related to the selection of green vehicles. In this research, a framework combining three MCDM methods, EDAS, VIKOR, and Taxonomy, has been employed to achieve optimal selection and facilitate the process of choosing green vehicles. By utilizing the characteristics of all three decision-making algorithms, the proposed algorithm enables accurate analysis through assigning weights to each criterion, thereby contributing to more intelligent economic and environmental decision-making in the selection of green vehicles. Based on the conducted analyses and the results obtained from the proposed EDAS–VIKOR–Taxonomy MCDM method, Plug-In Hybrid Electric Vehicles (PHEVs) were identified as the best green vehicles for entering the Iranian automotive market. The production of this type of vehicle in Iran's automotive industry can represent an effective and significant step toward environmental protection, air pollution control, and fuel consumption management.
Keywords:
Green vehicles, Multi-criteria decision-making, VIKOR decision-making method, EDAS decision-making method, Taxonomy decision-making methodReferences
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