A lexicographic approach for solving fully LR-type interval-valued intuitionistic fuzzy transportation problems

The traditional approaches of the transportation problem (TP) often fall short in addressing the inherent uncertainty and vagueness in real-world data. In this paper, we have introduced an advanced method leveraging Exponential LR-type Interval-Valued Intuitionistic Fuzzy (IVIF) numbers. Using this, the Fully LR-type IVIF transportation problem model is formulated. We have enhanced the representation of uncertainty in our model, which provides a more flexible and realistic solution approach. The solution method utilizes Lexicographic ordering to prioritize and resolve ambiguities in decision-making, ensuring systematic and hierarchical handling of multiple criteria. Also, we have shown a real-world application of our model. It has been observed that our methodologies are more accurate in capturing the robustness in nuances of real-world transportation scenarios. This study not only contributes a novel computational technique to the field of fuzzy optimization but also offers practical implications for improving logistical efficiency under uncertain conditions.