Methodology for assessing the economic efficiency of using an ontology-based decision support system for selecting aircraft-type unmanned aerial vehicles

Iurii Vorobiov, Kateryna Maiorova, Andriy Sosunov

Abstract


The subject of this study is the process of the formalized selection of fixed-wing unmanned aerial vehicles (UAVs) for specific missions using an ontology-based decision support system (ODSS) and the methods for analyzing the economic effectiveness of its implementation. The relevance of the research is attributed to the rapid expansion of UAV platform varieties, the heterogeneity of their tactical, technical, and operational characteristics, and the need for prompt, well-grounded decision-making under conditions of incomplete and contradictory information. The aim of the study is to develop a methodology for assessing the economic efficiency of using an ODSS for mission-oriented UAV selection, treating it as an investment project that accounts for life-cycle costs and expected economic benefits. To achieve this aim, the following tasks were addressed: analyzing the current state of ontology-based and multi-criteria approaches to UAV selection; developing a total cost of ownership model for the ODSS; identification of the structure of direct and indirect economic benefits resulting from its application; developing an algorithm for calculating integrated efficiency indicators; and presenting a numerical example demonstrating the practical application of the proposed methodology. The research results consist of a development formalized methodology that integrates domain ontology modeling with discounted economic analysis tools and enables quantitative assessment of the effects of reducing the labor input required for UAV selection and mitigating expected losses caused by erroneous decisions. The conclusions confirm that an ontology-based decision support system can be considered not only as a tool for improving the quality of UAV selection, but also as an economically viable investment. The scientific novelty of the study lies in the formalization of the economic effect of using an ODSS in UAV selection tasks. The practical significance of the results lies in the applicability of the proposed methodology for the techno-economic justification of the implementing ontology-based decision support systems in research organizations, aviation enterprises, and UAVs operators.

Keywords


UAV; ontology; decision support system; economic efficiency; total cost of ownership; NPV; ROI; multi-criteria selection

References


Martín-Lammerding, D., Astrain, J. J., & Córdoba, A. Dronetology: A domain ontology for UAS applications. Knowledge-Based Systems, 2025, vol. 327, article no. 114074. DOI: 10.1016/j.knosys.2025.114074.

Martín-Lammerding, D., Astrain, J. J., Córdoba, A., & Villadangos, J. An ontology-based system to avoid UAS flight conflicts and collisions in dense traffic scenarios. Expert Systems with Applications, 2023, vol. 215, article no. 119027. DOI: 10.1016/j.eswa.2022.119027.

Insaurralde, C. C., & Blasch, E. Ontological airspace-situation awareness for decision system support. Aerospace, 2024, vol. 11, iss. 11, article no. 942. DOI: 10.3390/aerospace11110942.

Kizielewicz, B., Wątróbski, J., & Sałabun, W. Multi-criteria decision support system for the evaluation of UAV intelligent agricultural sensors. Artificial Intelligence Review, 2025, vol 58, article no. 194. DOI: 10.1007/s10462-025-11201-1.

Gao, F. An integrated multi criteria decision making method using dual hesitant fuzzy sets with application for unmanned aerial vehicle selection. Scientific Reports, 2025, vol. 15, article no. 12637. DOI: 10.1038/s41598-025-95981-0.

Turanoğlu Şirin, B. Evaluation of unmanned combat aerial vehicles using q-rung orthopair fuzzy entropy based multi-attribute border approximation area comparison method Operational Research, 2025, vol. 25, article no. 71. DOI: 10.1007/s12351-025-00952-y.

Nguyen, K.-H., Comans, T., Nguyen, T. T., Simpson, D., Woods, L., Wright, C., & Sullivan, C. Cashing in: cost-benefit analysis framework for digital hospitals. BMC Health Services Research, 2024, vol. 24. Article no. 694. DOI: 10.1186/s12913-024-11132-7.

Novikova, T., Guo, Y., Abou Jaoude, J., & Tannous, W. Economic evaluation of digital transformation projects using NPV and IRR. Frontiers in Public Health, 2023, vol. 11, article no. 1073964. DOI: 10.3389/fpubh.2023.1073964.

Abdel-Aty, T. A., & Negri, E. Conceptualizing the digital thread for smart manufacturing: a systematic literature review. Journal of Intelligent Manufacturing, 2024, vol. 35, pp. 3629–3653. DOI: 10.1007/s10845-024-02407-1.

Duverger, E., Aubry, A., Levrat, E., & Arista, R. Early concurrent engineering in the aerospace industry supported by a digital thread framework. IFAC-PapersOnLine, 2024, vol. 58, iss. 19, pp. 510–515. DOI: 10.1016/j.ifacol.2024.09.263.

Kabashkin, I. Ontology-driven digital twin framework for aviation maintenance and operations. Mathematics, 2025, vol. 13, iss. 17, article no. 2817. DOI: 10.3390/math13172817.

Howdon, D., Lomas, J., Paulden, M., & et al. Economic Evaluation Evidence for Resource-Allocation Decision Making: Bridging the Gap for Local Decision Makers Using English Case Studies. Appl Health Econ Health Policy, 2022, vol. 20, pp. 783–972. DOI: 10.1007/s40258-022-00756-7.

Riaño, D., Real, F., López-Vallverdú, J. A., Campana, F., & et al. An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. Journal of Biomedical Informatics, 2022, vol. 45, iss. 3, pp. 429-446. DOI: 10.1016/j.jbi.2011.12.008.

Vrandečić, D., & Krötzsch, M. Wikidata: a free collaborative knowledgebase. Communications of the ACM, 2014, vol. 57, iss. 10, pp. 78–85. DOI: 10.1145/2629489.

McDaniel, M., & Storey, V. C. Evaluating domain ontologies: clarification, classification, and challenges. ACM Computing Surveys, 2019, vol. 52, iss. 4, article no. 76. DOI: 10.1145/3329124.

Slipchenko, V., Poliahushko, L., & Kravchuk, D. Decision-making models for complex the eco-energy-economic monitoring system. Computer Systems and Information Technologies, 2024, vol. 4, pp. 107–115. DOI: 10.31891/csit-2024-4-13.




DOI: https://doi.org/10.32620/aktt.2026.1.08