Enhanced Industrial Control System of Decision-Making Using Spherical Hesitant Fuzzy Soft Yager Aggregation Information
Journal Title: Acadlore Transactions on Applied Mathematics and Statistics - Year 2023, Vol 1, Issue 3
Abstract
In the realm of emergency response, where time and information constraints are paramount, and scenarios often involve high levels of toxicity and uncertainty, the effective management of industrial control systems (ICS) is critical. This study introduces novel methodologies for enhancing decision-making processes in emergency situations, specifically focusing on ICS-security. Central to this research is the employment of spherical hesitant fuzzy soft set (SHFSS), a concept that thrives in the presence of ambiguity and incomplete information. The research adopts and extends the parametric families of t-norms and t-conorms, as introduced by Yager, to analyze these sets. This approach is instrumental in addressing multi-attribute decision-making (MADM) problems within the ICS-security domain. To this end, four distinct aggregation operators (AOs) are proposed: spherical hesitant fuzzy soft yager weighted averaging aggregation, spherical hesitant fuzzy soft yager ordered weighted averaging aggregation, spherical hesitant fuzzy soft yager weighted geometric aggregation, and spherical hesitant fuzzy soft yager ordered weighted geometric aggregation. These operators are tailored to harness the operational benefits of Yager's parametric families, thereby offering a robust framework for dealing with decision-making problems under uncertainty. Further, an algorithm specifically designed for MADM is presented, which integrates these AOs. The efficacy and precision of the proposed methodology are demonstrated through a numerical example, applied in the context of an ICS security supplier. This example serves as a testament to the superiority of the approach in handling complex decision-making scenarios inherent in ICS-security management.
Authors and Affiliations
Razia Choudhary, Shabana Ashraf, Jafar Anafi
Keywords
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- EP ID EP732908
- DOI 10.56578/atams010304
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