A Decision Aiding Framework for Two-Way Selection of Cloud Vendors in Medical Centers With Generalized Orthopair Fuzzy Information

With the increase of data and computation in the healthcare industry, officials are looking for reliable alternatives to compete globally. Cloud technology is a viable option that reduces data and computation overhead at the host side. Due to the fast growth of cloud vendors (CVs), the apt selection of a vendor becomes crucial. Extant CV selection models 1) have reduced scope for minimizing subjective randomness; 2) cannot offer agents with flexible preference window; and 3) have higher human intervention during the decision process. Driven by these lacunae and to counter the same, a novel scientific model is developed in this article. Generalized orthopair fuzzy information is adopted to express preferences. Later, the attitudinal-CRITIC approach is put forward to determine the significance of functional factors. Agents' attitude values are calculated by extending the variance approach. Agent wise personalized prioritization algorithm is developed to rank CVs based on individual perceptions, query vectors, and cumulative rank factors. The scientific model's usefulness is verified by adopting a case study of CV selection in a private medical unit in Trichy. Finally, sensitivity analysis and comparison with extant models help understand the merits and limitations of the proposed work.