Score Function Algorithmic Approach in Optimizing Dual Hesitant Fuzzy Multiobjective Transportation Models

Multiobjective transportation problem (MOTP) seeks to optimize multiple, often conflicting objectives within transportation and logistics systems. However, in real‐world scenarios, parameters such as transportation costs, time, availability, and demand are inherently uncertain. To address these uncertainties, various extensions of fuzzy logic such as intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), Fermatean fuzzy sets (FFSs), and hesitant fuzzy sets (HFSs) and more have already been applied to MOTPs. Despite these advancements, the dual HFS (DHFS) has not yet been incorporated into MOTP. This research article introduces the novel concept of dual hesitant fuzzy MOTP (DHFMOTP), utilizing DHFS for the first time in this MOTP. A new score function for DHFS and an accompanying algorithm are developed effectively to solve the DHFMOTP. To validate the proposed methodology, two numerical problems are solved. Computed results demonstrate the significant improvements. The proposed score function and algorithm for DHFMOTP consistently yielded more optimal results than the existing methods. Future studies could explore the integration of other advanced fuzzy extensions and hybrid algorithms for further improvement of decision‐making efficiency in transportation problems under uncertainty.