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Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption

Author

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  • Pasura Aungkulanon

    (Department of Materials Handling and Logistics Engineering, Faculty of Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand)

  • Walailak Atthirawong

    (School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

  • Pongchanun Luangpaiboon

    (Thammasat University Research Unit in Industrial Statistics and Operational Research, Department of Industrial Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand)

Abstract

In response to the requirement to address the global climate crisis in urban areas caused by the logistics sector, an increasing number of governments around the world have begun aggressive strategic actions to encourage manufacturers and consumers to adopt electric vehicle (EV) technology. One of the most beneficial aspects of driving an EV is that it reduces pollution while also reducing the use of fossil fuels, as well as improving public health by improving local air quality. Nevertheless, the level of EV adoption differs significantly across markets and geographies. EV adoption barriers slow the overall rate of electric mobility. This study ranks a list of obstacles and sub-hindrances to the adoption of electric vehicles in Thailand using the Fuzzy Analytical Hierarchy Process (FAHP), a Multi-Criteria Decision Making (MCDM) technique. The results showed that infrastructure policy barrier (A), which had the highest weight of 0.6058, was the biggest barrier to EV adoption, followed by technological barrier (B) with a weight of 0.2657, and then by market barrier with a weight of 0.1285. Insufficient charging infrastructure network (A3), lack of proper government support/incentives and collaboration (A1), insufficient electric power supply (A2), high capital cost (C3), and EV charging time (B3) were key sub-barriers to EV adoption in Thailand. Decision Making Systems (DMS) have additionally been created to assist executives in making decisions about the aforementioned barriers. The DMS is based on the concept of computer-aided decision making in that it allows for direct user interaction, analysis, and the ability to change circumstances and the decision-making process based on the executives’ own experience and abilities. Thus, the findings of this study aid in the formulation of market strategies for relevant stakeholders and shed light on potential policy responses.

Suggested Citation

  • Pasura Aungkulanon & Walailak Atthirawong & Pongchanun Luangpaiboon, 2023. "Fuzzy Analytical Hierarchy Process for Strategic Decision Making in Electric Vehicle Adoption," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:7003-:d:1129528
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    3. Meshal Almoshaogeh & Arshad Jamal & Irfan Ullah & Fawaz Alharbi & Sadaquat Ali & Md Niamot Alahi & Majed Alinizzi & Husnain Haider, 2025. "Interpretive Structural Modeling of Influential Factors Affecting Electric Vehicle Adoption in Saudi Arabia," Energies, MDPI, vol. 18(19), pages 1-30, September.
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    7. Pasura Aungkulanon & Walailak Atthirawong & Woranat Sangmanee & Pongchanun Luangpaiboon, 2023. "Fuzzy Techniques and Adjusted Mixture Design-Based Scenario Analysis in the CLMV (Cambodia, Lao PDR, Myanmar and Vietnam) Subregion for Multi-Criteria Decision Making in the Apparel Industry," Mathematics, MDPI, vol. 11(23), pages 1-32, November.

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