Complex Fermatean fuzzy extended TOPSIS method and its applications in decision making

Zaman, Muhammad; Ghani, Fazal; Khan, Asghar; Abdullah, Saleem; Khan, Faisal

doi:10.1016/j.heliyon.2023.e19170

Cited by 9 publications

(1 citation statement)

References 57 publications

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“…Deveci et al (2023) assessed the risks impacting sustainable mining in Greece, employing a new FF score function with SWARA for risk weighting to find the most crucial risks, aligning with SDG12 and identifying "Risk to Environment" as the highest risk category, thereby offering a roadmap for sustainable mining practices. Zaman et al (2023) developed new decision-making techniques based on complex Fermatean fuzzy numbers and introduced various aggregation operators like CFFEWAA ("complex Fermatean fuzzy Einstein weighted average aggregation"), CFFEOWAA ("complex Fermatean fuzzy Einstein ordered weighted average aggregation"), and CFFEHAA ("complex Fermatean fuzzy Einstein hybrid average aggregation"), applying them in an extended TOPSIS method for multi-attribute group decision-making, specifically for selecting an English language instructor.…”

Section: Fermatean Fuzzy Setsmentioning

confidence: 99%

Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo

Dhruva,

Krishankumar,

Zavadskas

et al. 2023

Informatica

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Cloud computing has emerged as a transformative technology in the healthcare industry, but selecting the most suitable CV (“cloud vendor”) remains a complex task. This research presents a decision framework for CV selection in the healthcare industry, addressing the challenges of uncertainty, expert hesitation, and conflicting criteria. The proposed framework incorporates FFS (“Fermatean fuzzy set”) to handle uncertainty and data representation effectively. The importance of experts is attained via the variance approach, which considers hesitation and variability. Furthermore, the framework addresses the issue of extreme value hesitancy in criteria through the LOPCOW (“logarithmic percentage change-driven objective weighting”) method, which ensures a balanced and accurate assessment of criterion importance. Personalized grading of CVs is done via the ranking algorithm that considers the formulation of CoCoSo (“combined compromise solution”) with rank fusion, providing a compromise solution that balances conflicting criteria. By integrating these techniques, the proposed framework aims to enhance the rationale and reduce human intervention in CV selection for the healthcare industry. Also, valuable insights are gained from the framework for making informed decisions when selecting CVs for efficient data management and process implementation. A case example from Tamil Nadu is presented to testify to the applicability, while sensitivity and comparison analyses reveal the pros and cons of the framework.

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Section: Fermatean Fuzzy Setsmentioning

confidence: 99%