Energy supplier selection using Einstein aggregation operators in an interval-valued q-rung orthopair fuzzy hypersoft structure

The selection of energy suppliers is important for sustainable energy management, as selecting the most appropriate suppliers reduces the environmental impact and improves resource optimization through sustainable practices. Our primary objective of this work was to develop a system for identifying energy suppliers by assessing various characteristics and their associated sub-attributes. Interval-valued q-rung orthopair fuzzy hypersoft sets (IVq-ROFHSS) originate by developing an association among interval-valued q-rung orthopair fuzzy sets and hypersoft sets. It is a crucial resource to handle unpredictable situations, mainly when presenting a component in a real-life scenario. IVq-ROFHSS is a new structure developed to manage the sub-parametric values of the alternatives. We developed the Einstein operational laws for IVq-ROFHSS and extended the Interval-valued q-rung ortho-pair fuzzy hypersoft Einstein weighted average (IVq-ROFHSEWA) and interval-valued q-rung ortho-pair fuzzy hypersoft Einstein weighted geometric (IVq-ROFHSEWG) operators. Moreover, we used the developed operators to formulate a multi-attribute group decision-making strategy to choose the ideal provider in sustainable energy management. The presented fuzzy robust approach reliably reiterated the challenged energy supplier selection in supply chain management to regular activities while alleviating overall expenses and promising stable reliability.