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Selection of Suitable Cloud Vendors for Health Centre: A Personalized Decision Framework with Fermatean Fuzzy Set, LOPCOW, and CoCoSo
Volume 35, Issue 1 (2024), pp. 65–98
Sundararajan Dhruva   Raghunathan Krishankumar   Edmundas Kazimieras Zavadskas   Kattur Soundarapandian Ravichandran   Amir H. Gandomi  

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https://doi.org/10.15388/23-INFOR537
Pub. online: 23 November 2023      Type: Research Article      Open accessOpen Access

Received
1 September 2023
Accepted
1 November 2023
Published
23 November 2023

Abstract

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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Biographies

Dhruva Sundararajan
cb.sc.i5das19016@cb.students.amrita.edu

S. Dhruva is currently a final year student of MSc, Integrated Data Science, at Amrita Vishwa Vidyapeetham, Coimbatore, India. His research interests include multi-criteria decision making, machine learning, deep learning and optimization. His research work has been accepted in SCIE-indexed journals and an IEEE Conference in 2023.

Krishankumar Raghunathan
raghunathan.k@iimbg.ac.in

R. Krishankumar is an assistant professor of information technology systems and analytics area, Indian Institute of Management Bodh Gaya, Bodh Gaya 824234, Bihar, India. His area of interests is multi-criteria decision-making and soft computing. He has published more than 50 articles in peer reviewed journals and serves in the EB of peer-reviewed journals. He has been nominated as one of the world’s top 2% scientists based on the data from Scopus and Stanford University.

Zavadskas Edmundas Kazimieras
edmundas.zavadskas@vilniustech.lt

E.K. Zavadskas, PhD, DSc, Dr. habil, Dr. H. C. multi, prof. chief researcher of Institute of Sustainable Construction, Faculty of Civil Engineering, Vilnius Gediminas Technical University, Lithuania. PhD in building structures (1973). Dr. Sc. (1987) in building technology and management. Dr. Habil (1993). Founder of Vilnius Gediminas Technical University (1990). Member of the Lithuanian Academy of Science; member of several foreign Academies of Sciences; Honorary doctor from Poznan, Saint-Petersburg, and Kyiv universities. Member of international organizations; member of steering and programme committees at many international conferences; chairman of EURO Working Group ORSDCE; associate editor, guest editor, or editorial board member for 40 international journals (Computers-Aided Civil and Infrastructure Engineering, Automation in Construction, Informatica, International Journal of Information Technology and Decision Making, Archives of Civil and Mechanical Engineering, International Journal of Fuzzy Systems, Symmetry, Sustainability, Applied Intelligence, Energy, Entropy and other); author and co-author of more than 600 papers and a number of monographs in Lithuanian, English, German and Russian. Founding editor of journals Technological and Economic Development of Economy, Journal of Civil Engineering and Management, International Journal of Strategic Property Management. He was a highly cited researcher in 2014, 2018, 2019, 2020. Research interests: multi-criteria decision making, civil engineering, sustainable development, fuzzy multi-criteria decision making.

Ravichandran Kattur Soundarapandian
ks_ravichandran@cb.amrita.edu

K.S. Ravichandran recently joined Amrita Vishwa Vidyapeetham in Coimbatore as a distinguished professor in the Department of Mathematics at the Amrita School of Physical Sciences, Tamil Nadu, India. Prior to this appointment, he held the position of the registrar at Rajiv Gandhi National Institute of Youth Development (RGNIYD), an Institute of National Importance under the Government of India located in Sriperumbudur, Kancheepuram. Additionally, he served as an associate dean of research at SASTRA University in Thanjavur, India. After obtaining his master’s degree in Computer Applications (MCA) and Master of Science in Mathematics (MSc) (Mathematics), Prof. Ravichandran completed his PhD in mathematics at Alagappa University, Tamil Nadu, India. His academic contributions are notable, with a publication record of more than 200 research articles. Among these, over 185 are indexed in SCOPUS, and more than 95 are indexed in SCI/SCIE/ABDC journals, boasting an average impact factor exceeding 4.25 and an H-index of 31. Professor Ravichandran specializes in various domains such as medical image processing, machine learning, deep learning, multi-criteria decision-making, and computational intelligence and its applications. He completed two research-funded projects as principle investigator worth INR 78 Lakhs. He currently serves as an associate editor for the International Journal of Information Technology, a SCOPUS-indexed journal published by Springer. Moreover, he actively contributes as a reviewer for over 50 SCI/SCIE-indexed journals.

Gandomi Amir H.
gandomi@uts.edu.au

A.H. Gandomi is among the world’s most cited researchers for his work in the fields of global optimization and big data analytics, in particular, using machine learning and evolutionary computations. He is an ARC DECRA Fellow at the Faculty of Engineering and Information Technology at UTS, where he is a professor of data science. He has received multiple prestigious awards for his research excellence and impact, such as the 2023 Achenbach Medal and the 2022 Walter L. Huber Prize, the highest-level mid-career research award in all areas of civil engineering. Amir has published more than 350 journal papers and 14 books, which collectively have more than 43,000 citations (with an H-index of 93), and he has been named one of the world’s most influential scientific minds and highly cited researchers by the influential Clarivate Analytics for six consecutive years, to 2022. He is also ranked 17th among more than 15,000 researchers in the online computer science bibliography, Genetic Programming bibliography, and ranks first in Australia. In the recent most impactful researcher list, done by Stanford University and released by Elsevier, Prof. Amir H. Gandomi is ranked among the top 1,000 researchers (top 0.01%) and top 50 researchers in the AI and Image Processing subfield in 2021. He has served as an associate editor, editor, and guest editor in several prestigious journals, such as AE of IEEE TBD, IEEE Networks, and IEEE IoTJ. He regularly delivers keynote addresses at major conferences. Prior to joining UTS, Amir was an assistant professor at the School of Business at Stevens Institute of Technology in the US. He was also a distinguished research fellow in the BEACON Center at Michigan State University, where biologists, computer scientists, and engineers together study evolution and apply their knowledge to real-world problems.


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Keywords
cloud vendor selection Health 4.0 fermatean fuzzy set variance method LOPCOW method CoCoSo method

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