Abstract
The use of unmanned aerial vehicles (UAVs), popularly named drones, has increased substantially recently, highlighting their applications in various missions, such as intelligence, surveillance, reconnaissance (ISR), communications, electronic warfare, and cargo delivery. This study aims to present a methodology capable of assisting in selecting ISR drones for use in modern wars, especially the Russia–Ukraine war. To assist in this task, a hybrid multicriteria decision analysis (MCDA) approach will be applied: the method based on the removal effects of criteria (MEREC) for calculating the weights of the criteria and the multi-attribute ideal-real comparative analysis (MAIRCA) for ranking the alternatives. The proposed analysis evaluated six UAVs and five strategic, logistical, and tactical criteria. The justification for this work is the scarce approach to ISR drones in SAR missions in the literature. It is fundamental because it aims to maximize the probability of success through the interoperability of ISR drones and conventional means. After applying the proposed hybrid methodology, the best ISR UAV was chosen in the context presented, which provides a valuable contribution to academia and society, as it represents the application of MCDA methods in state of the art to support the decision-making process in a real problem. It is also noteworthy that the application of MEREC–MAIRCA can be expanded to several areas of daily life in the public and private spheres.
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de Araújo Costa, A.P., de Pina Corriça, J.V., de Araújo Costa, I.P., Moreira, M.Â.L., dos Santos, M., Gomes, C.F.S. (2024). Operational Analysis of Unmanned Aerial Vehicles in Modern Wars’ Context: A Hybrid Multicriteria Approach by MEREC–MAIRCA Methods. In: Ullah, A., Anwar, S., Calandra, D., Di Fuccio, R. (eds) Proceedings of International Conference on Information Technology and Applications. ICITA 2022. Lecture Notes in Networks and Systems, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-99-8324-7_30
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DOI: https://doi.org/10.1007/978-981-99-8324-7_30
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