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<p>This study introduces the Advanced Gravitational Decision-Making (GRAD) approach, an innovative framework that enriches multi-criteria decision-making (MCDM) by integrating inter-alternative interactions, the standard deviations of criteria, and distance into a single model. Inspired by Newton’s universal law of gravitation, GRAD surpasses conventional methods by offering a more realistic assessment of alternatives, particularly when facing high uncertainty or closely matched alternatives. The method treats criteria with higher standard deviation as having “greater mass,” thus exerting stronger influence on the final decision. Simultaneously, distance calculations capture how similar or dissimilar alternatives “attract” or “repel” each other, leading to more nuanced rankings. In a demonstrative case, GRAD was compared against popular techniques like TOPSIS, VIKOR, and CoCoSo, revealing more robust outcomes under uncertainty. Sensitivity analyses further confirmed its adaptability to varying weight and risk preferences, while Monte Carlo simulations showed that slight data perturbations rarely altered GRAD’s overall rankings. Moreover, the method was applied to a real-world dataset on concrete compressive strength, a fundamental mechanical property, where GRAD successfully identified the most balanced mix design by accounting for complex trade-offs among multiple material and process variables. Organizations in diverse sectors can therefore benefit from GRAD by objectively evaluating the interplay among alternatives, ensuring more reliable and informed decisions. Overall, GRAD provides a comprehensive yet flexible tool that enhances the rigor of MCDM in uncertain, multidimensional scenarios.</p>

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Advanced gravitational decision-making method inspired by newton’s law of universal gravitation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Mehmet Akif Yerlikaya,
  • Hüseyin Beytüt,
  • Kürşat Yildiz,
  • Ömer Faruk Efe

摘要

This study introduces the Advanced Gravitational Decision-Making (GRAD) approach, an innovative framework that enriches multi-criteria decision-making (MCDM) by integrating inter-alternative interactions, the standard deviations of criteria, and distance into a single model. Inspired by Newton’s universal law of gravitation, GRAD surpasses conventional methods by offering a more realistic assessment of alternatives, particularly when facing high uncertainty or closely matched alternatives. The method treats criteria with higher standard deviation as having “greater mass,” thus exerting stronger influence on the final decision. Simultaneously, distance calculations capture how similar or dissimilar alternatives “attract” or “repel” each other, leading to more nuanced rankings. In a demonstrative case, GRAD was compared against popular techniques like TOPSIS, VIKOR, and CoCoSo, revealing more robust outcomes under uncertainty. Sensitivity analyses further confirmed its adaptability to varying weight and risk preferences, while Monte Carlo simulations showed that slight data perturbations rarely altered GRAD’s overall rankings. Moreover, the method was applied to a real-world dataset on concrete compressive strength, a fundamental mechanical property, where GRAD successfully identified the most balanced mix design by accounting for complex trade-offs among multiple material and process variables. Organizations in diverse sectors can therefore benefit from GRAD by objectively evaluating the interplay among alternatives, ensuring more reliable and informed decisions. Overall, GRAD provides a comprehensive yet flexible tool that enhances the rigor of MCDM in uncertain, multidimensional scenarios.