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<p>The transition to electric vehicles (EVs) is a critical component of global sustainability efforts, necessitating robust evaluation methodologies for informed decision-making. This study introduces a novel double normalization framework within the multi-criteria decision-making (MCDM) paradigm, integrating linear and nonlinear normalization techniques to enhance ranking consistency and mitigate data variability issues. The proposed approach employs logarithmic percentage change objective weighting (LOPCOW) to determine objective criteria weights and the root assessment method (RAM) to facilitate alternative rankings, ensuring a rigorous and unbiased assessment of EVs. Comparative analysis against established MCDM methods, including TOPSIS, MOORA, COPRAS, WSM, and WASPAS, validates the consistency and reliability of the proposed technique. Furthermore, a sensitivity analysis demonstrates the adaptability of the framework across different weighting schemes, reinforcing its robustness for decision support in electric vehicle (EV) selection. The findings underscore the effectiveness of double normalization as a refined MCDM approach, offering a structured and objective decision support system for sustainable transportation planning. The proposed double normalization framework integrates the strengths of linear and nonlinear normalization to reduce scale distortion and improve fairness. Combined with LOPCOW’s objective weighting and RAM’s compensatory ranking logic, the model provides a robust, interpretable, and generalizable MCDM tool.</p>

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A Double Normalization Framework for Sustainable Electric Vehicle Selection: Integrating LOPCOW and RAM in Multi-Criteria Decision-Making

  • 期刊论文

  • 发表日期：

    2025年11月2日

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  • Yalamanda Babu Gopisetty,
  • Hanumanth Rao Sama,
  • Tirupathi Rao Padi,
  • Lakshmi Patibandla

摘要

The transition to electric vehicles (EVs) is a critical component of global sustainability efforts, necessitating robust evaluation methodologies for informed decision-making. This study introduces a novel double normalization framework within the multi-criteria decision-making (MCDM) paradigm, integrating linear and nonlinear normalization techniques to enhance ranking consistency and mitigate data variability issues. The proposed approach employs logarithmic percentage change objective weighting (LOPCOW) to determine objective criteria weights and the root assessment method (RAM) to facilitate alternative rankings, ensuring a rigorous and unbiased assessment of EVs. Comparative analysis against established MCDM methods, including TOPSIS, MOORA, COPRAS, WSM, and WASPAS, validates the consistency and reliability of the proposed technique. Furthermore, a sensitivity analysis demonstrates the adaptability of the framework across different weighting schemes, reinforcing its robustness for decision support in electric vehicle (EV) selection. The findings underscore the effectiveness of double normalization as a refined MCDM approach, offering a structured and objective decision support system for sustainable transportation planning. The proposed double normalization framework integrates the strengths of linear and nonlinear normalization to reduce scale distortion and improve fairness. Combined with LOPCOW’s objective weighting and RAM’s compensatory ranking logic, the model provides a robust, interpretable, and generalizable MCDM tool.