Evaluating UAV Alternatives for Post-Disaster Situations: An Integrated Decision-Making Methodology

The rapid and efficient deployment of unmanned aerial vehicles (UAVs) in post-disaster scenarios is recognized as critical for effective management and logistics. The problem of selecting the most suitable UAVs for three key tasks—monitoring, light load transportation, and heavy load transportation—is addressed in this study. An extensive review of related literature was conducted to identify specific evaluation criteria tailored to each task. The UAV evaluation problem has been divided into three sub-problems, and an integrated multi-criteria decision-making (MCDM) approach is proposed within a Pythagorean Fuzzy environment for evaluation. The Step-Wise Weight Assessment Ratio Analysis (SWARA) method was employed to determine criteria weights. To rank UAVs currently available on the market, the Evaluation Based on Distance from Average Solution (EDAS) and Combinative Distance-based Assessment (CODAS) techniques were applied, with the Copeland Score used to aggregate and finalize the ranking based on each UAV’s overall performance across criteria. The results highlight key UAV choices for each task, guiding decision-makers toward UAVs that best meet the specific operational needs of post-disaster logistics. This framework provides a robust, adaptable tool for enhancing the agility and effectiveness of disaster response strategies through informed UAV selection.