Improving industrial automation selection with dynamic exponential distance in neutrosophic group decision-making framework

Amirhossein Nafei, Shu-Chuan Chen, Harish Garg, Chien-Yi Huang, Florentin Smarandache and Seyed Mohammadtaghi Azimi

Journal of the Operational Research Society, 2025, vol. 76, issue 12, 2474-2493

Abstract: In the fast-paced evolution of industrial technology, selecting optimal automation systems is essential for improving operational efficiency. Traditional decision-making frameworks often fall short in addressing the complex interplay of diverse criteria, especially under conditions of uncertainty and imprecision. This research proposes a decision-making methodology that integrates dynamic exponential distance (DED) within the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) framework, enhanced by neutrosophic logic. This integration provides a more accurate decision-making process by revolutionizing distance calculations with exponential transformations. The DED approach amplifies differences in extreme values, enhancing the ability to distinguish between alternatives. The motivation behind this research is the growing need for a more robust decision-making framework that can better handle complex, uncertain, and contradictory information. The contributions of this research include developing the DED approach to enhance precision in distance calculations within neutrosophic environments using exponential transformations, introducing a comprehensive methodology to address uncertainty and indeterminacy in decision-making scenarios, and demonstrating the method’s adaptability to real-world industrial automation challenges by improving discrimination. The comprehensive case study validates the proposed method’s effectiveness, showing better discrimination between alternatives and adaptability to dynamic decision contexts. The runtime analysis highlights a balanced trade-off between computational demands and decision-making performance compared to other methods.

Date: 2025
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DOI: 10.1080/01605682.2025.2477665

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