An integrated multi criteria decision making method using dual hesitant fuzzy sets with application for unmanned aerial vehicle selection

Unmanned aerial vehicles (UAVs) have gained widespread attention in recent years due to their expanding applications across various industrial sectors. Selecting the most suitable UAV for a given task is a critical decision-making challenge, which is typically modeled as a multi-criteria decision-making (MCDM) problem. However, expert assessments in such selection processes often involve considerable uncertainty and hesitation. To address this, this paper proposes a novel integrated MCDM framework that combines dual hesitant fuzzy sets (DHFSs), the best-worst method (BWM), and the MULTIMOORA method to evaluate and rank UAV alternatives. In the proposed method, DHFSs are employed to capture both membership and non-membership degrees of expert assessments under uncertainty, while expert weights are objectively determined based on the entropy of their assessments. Criteria weights are then calculated using an extended dual hesitant fuzzy BWM. Subsequently, the MULTIMOORA method is extended into the dual hesitant fuzzy environment, where UAV alternatives are evaluated from three perspectives: the ratio system, the extended reference point approach, and the full multiplicative form, and the evaluation results are aggregated to generate a comprehensive and reliable final ranking. To demonstrate the practicality and effectiveness of the proposed method, a case study on UAV selection for power line inspection is presented. The results show that the proposed approach effectively handles uncertainty, produces stable and consistent rankings, and offers reliable decision support under uncertain and fuzzy conditions. The proposed method provides a flexible and systematic decision-making tool that can assist decision-makers in solving UAV selection problems in complex, real-world scenarios.