A novel decision support system for sustainable logistics management in interval-valued fermatean picture fuzzy structure

Determining a sustainable logistics management system constitutes an extensive decision-making process characterized by fundamental volatility. Interval-valued Fermatean picture fuzzy sets (IVFPFS) propose a more versatile and detailed structure to represent obscure and unreliable data, making them more suitable for such concerns. Each possibility in this structure is examined based on significant criteria, including infrastructure development, logistics, economic performance, operational management, and technological innovation. This study describes and analyses the properties of two proficient aggregation operators: the interval-valued Fermatean picture fuzzy Einstein weighted average (IVFPFEWA) and the interval-valued Fermatean picture fuzzy Einstein weighted geometric (IVFPFEWG) operators. A new multi-attribute decision-making (MADM) methodology is presented, implementing Einstein-based operators in an IVFPFS structure to boost decision-making procedures for sustainable logistics management. The comparative and sensitivity analyses confirm the consistency and potency of the presented strategy, indicating that it remains more realistic and functional than conventional strategies. The outcomes demonstrate that the laid out technique delivers a realistic decision to the obstacles of maintaining a resilient logistics system.