Application of Fermatean Vague Soft Sets in Multi-Criteria Decision-Making for Cargo UAV Selection

Abstract

Rapid technological advancements have significantly streamlined logistics and transport systems, with Unmanned Aerial Vehicles (UAVs) emerging as a practical alternative in regions where conventional transport infrastructure is limited or ineffective. However, selecting an optimal cargo drone requires balancing complex, often competing criteria across operational and financial factors. To address this, we apply a Fermatean Vague Soft Set (FVSS) framework within a multi-criteria decision-making (MCDM) process to identify the most suitable cargo UAV. By integrating the parameterization of soft sets with the nuanced truth and falsity bounds of vague sets, the FVSS model expands the operational space via the cubic constraint, providing a flexible and reliable tool to handle uncertain, incomplete, and ambiguous expert evaluations.