Optimizing decision precision with linguistic Pythagorean fuzzy Dombi models

Last-mile distribution is a subject that has drawn significant attention from both academic and industry researchers. There are several reasons for the adoption of drone delivery technology, including the growing number of customers who want more flexible and faster delivery options. Currently, there is a large selection of these models on the market. Therefore, there is a need to develop efficient methods to select the most appropriate drone delivery service. This research employs Dombi aggregation operators (AOs) within the context of linguistic Pythagorean fuzzy sets (LPFS) to tackle issues in drone delivery operations. The incorporation of linguistic concepts within the Pythagorean fuzzy framework improves the precision and dependability of delivery data analysis by providing a more thorough representation of uncertainty, consistent with human intuition and qualitative assessments. The present study presents two novel aggregation operators: the linguistic Pythagorean fuzzy Dombi weighted averaging (LPFDWA) and the linguistic Pythagorean fuzzy Dombi weighted geometric (LPFDWG) operators. Essential structural characteristics of these operators are demonstrated, and important particular cases are described. Furthermore, we developed a systematic approach for handling multi-attribute decision-making issues that incorporate LPF data through the use of the suggested operators. In order to showcase the effectiveness of the developed approaches, we provide a numerical illustration that identifies the top drone delivery service. Finally, we execute an in-depth comparative assessment to evaluate the efficacy of the proposed methods in relation to several established procedures.