Selection of Cloud Vendors for Medical Centers Using Personalized Ranking With Evidence-Based Fuzzy Decision-Making Algorithm

Abstract

Cloud is becoming an attractive buzzword in information technology due to its on-demand pay-as-you-go mechanism. Many service providers emerge in the market with attractive services/offers. In this study, a new integrated decision approach is developed for the cloud vendor (CV) selection problem. First, a double hierarchy structure is adopted to model natural language preferences from experts. Second, the reliability values of experts are determined by using the criteria importance through the intercriteria correlation approach by effectively capturing interactions among experts. Third, the evidence-driven Bayesian technique is presented to calculate the criteria weights that aid in rating CVs. Fourth, a personalized ranking algorithm with the compromise ranking of alternatives from distance to ideal solution approach is proposed to resemble close to human decision-making. A practical example of CV selection for a private medical center in Tamil Nadu is testified to demonstrate the usefulness of the developed framework. Finally, sensitivity analysis and comparison reveal the promising strengths of the developed framework. The results present that assurance, accountability, agility, and usability are the foremost drivers for CV selection. This work can enrich the theory and application of the evaluation issue of information and communication technologies and multicriteria analysis.