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<p>In many real-life decision-making problems, uncertainty complicate the selection of the best alternative. To address these challenges, this study introduces a novel decision-making approach that integrates distance measure within the framework of trapezoidal fuzzy multi-numbers (TFM-numbers). Based on these measures, a comprehensive multi-criteria decision-making (MCDM) framework is constructed, in which the closeness between each alternative and the ideal solutions is quantified to generate a clear and objective ranking of alternatives under uncertainty. The proposed framework systematically aggregates criteria evaluations expressed by TFM-numbers and determines the final ranking according to the obtained similarity and distance-based scores. To demonstrate the applicability and practicality of the proposed approach, a numerical example using artificial data in the field of agricultural irrigation is provided. Furthermore, robustness and sensitivity analyses are conducted to examine the stability and consistency of the ranking results under varying conditions. Finally, a comparative analysis with several existing MCDM methods defined on TFM-numbers is performed, confirming the proposed approach’s robustness, accuracy, flexibility, and superior capability in modeling uncertainty and decision-makers’ preferences.</p>

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A generalized similarity-based hybrid approach for multi-criteria decision-making with trapezoidal fuzzy multi-numbers and its application

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月24日

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  • Vakkas Uluçay

摘要

In many real-life decision-making problems, uncertainty complicate the selection of the best alternative. To address these challenges, this study introduces a novel decision-making approach that integrates distance measure within the framework of trapezoidal fuzzy multi-numbers (TFM-numbers). Based on these measures, a comprehensive multi-criteria decision-making (MCDM) framework is constructed, in which the closeness between each alternative and the ideal solutions is quantified to generate a clear and objective ranking of alternatives under uncertainty. The proposed framework systematically aggregates criteria evaluations expressed by TFM-numbers and determines the final ranking according to the obtained similarity and distance-based scores. To demonstrate the applicability and practicality of the proposed approach, a numerical example using artificial data in the field of agricultural irrigation is provided. Furthermore, robustness and sensitivity analyses are conducted to examine the stability and consistency of the ranking results under varying conditions. Finally, a comparative analysis with several existing MCDM methods defined on TFM-numbers is performed, confirming the proposed approach’s robustness, accuracy, flexibility, and superior capability in modeling uncertainty and decision-makers’ preferences.