This paper develops a two-stage decision approach with probabilistic hesitant fuzzy data. Research challenges in earlier models are: (i) the calculation of occurrence probability; (ii) imputation of missing elements; (iii) consideration of attitude and hesitation of experts during weight calculation; (iv) capturing of interdependencies among experts during aggregation; and (v) ranking of alternatives with resemblance to human cognition. Driven by these challenges, a new group decision-making model is proposed with integrate methods for data curation and decision-making. The usefulness and superiority of the model is realized via an illustrative example of a logistic service provider selection.
Pub. online:23 Nov 2023Type:Research ArticleOpen Access
Journal:Informatica
Volume 35, Issue 1 (2024), pp. 65–98
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.