Enhancing decision-making in cloud service provider selection using probabilistic p, q-rung orthopair fuzzy model

Abstract

Desktop cloud technology has revolutionized modern computing by enabling remote desktop functionality through cloud computing and virtualization. However, traditional fuzzy set theories struggle with the uncertainties inherent in these environments. This study addresses this gap by introducing the probabilistic p, q-rung orthopair fuzzy model, a novel extension that integrates probabilistic elements to improve precision and robustness in decision-making. Key contributions include the development of advanced aggregation operators, such as probabilistic weighted averaging and geometric operators, and their application in a multi-attribute decision-making algorithm. The model is validated through a case study on cloud service provider selection, demonstrating its effectiveness in supporting sustainable development and planning. The results show that the proposed model outperforms existing approaches, offering enhanced accuracy and reliability. This contribution advances decision-making frameworks in desktop cloud environments, fostering sustainability and improving the efficiency of daily office tasks.