MixTOPSIS: A TOPSIS Extension Incorporating Gower Distances for Selecting Unmanned Aerial Vehicles with Mixed-Scale Attributes

UAVs come in many different platforms such as quadcopters, hexacopters, hybrid models, etc., with diverse attribute types which can be characterised as heterogeneous data. In MCDM, heterogeneous datasets pose several challenges, as most methods are designed for homogeneous datasets. To address these challenges, this paper introduces the MixTOPSIS method by integrating the Gower dissimilarity metric into the TOPSIS framework, enabling the evaluation of alternatives with mixed data types, including numerical, ordinal, and nominal criteria. Therefore, to demonstrate how the method extends TOPSIS for mixed data types, the UAV ranking problem was chosen as the application. According to the simulation results, the top-ranked alternative consistently matched the preferred nominal criteria, while tare and maximum takeoff weight attributes were limiting elements for all options. Sensitivity analyses with gradual and random perturbations of criterion weights were conducted to further examine ranking stability. For the gradual perturbations, the dominant ranking pattern was maintained in 92.78% of the 1441 weight assignments. The analysis with random perturbations also showed substantial ranking stability, with the dominant pattern maintained in 98.02% of cases. The primary originality of MixTOPSIS lies in: (i) evaluating mixed-scale (nominal, ordinal, and numerical) criteria within the TOPSIS distance framework without requiring artificial numerical encoding; (ii) offering a flexible preferred-level mechanism for qualitative attributes; and (iii) introducing criterion-wise contribution metrics that enhance decision explainability. Overall, MixTOPSIS performed effectively, offering a robust, practically useful decision-making tool with interpretable results for complex real-world applications involving diverse data types.