Development of the method of multi-criteria evaluation of hierarchical systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.331018

Keywords:

system of indicators, vulnerability tree, penguin swarm algorithm, destabilizing factors, military (force) grouping

Abstract

Multicriteria evaluation offers undeniable advantages over single-criterion assessment methods. The object of the study is hierarchical systems. The subject of the study is the process of multicriteria evaluation of the state of hierarchical systems. A method for multicriteria evaluation of hierarchical systems is proposed. The originality of the method lies in the application of additional advanced procedures that allow for the following:

– verification of input data and refinement of inter-element connections within the hierarchical system using an enhanced penguin swarm algorithm. This minimizes the risk of errors resulting from incorrect data input in the assessment of the operational military (force) grouping;

– description of external and internal factors affecting the hierarchical system subject to multicriteria evaluation through the use of fuzzy cognitive models;

– adaptation to the type of hierarchical system via multilevel adjustment of the system of indicators and evaluation criteria;

– reduction of uncertainty through the use of interval-valued Pythagorean fuzzy sets, thereby improving the reliability of multicriteria assessment of hierarchical system states;

– identification of the most vulnerable elements within the hierarchical system using a fault tree analysis;

– adaptation of the membership function type depending on the system’s available computational resources, which ensures compatibility with existing computational capacities.

An example of the method’s application is demonstrated through the multicriteria evaluation of an operational military (force) grouping. The proposed method provides an average improvement of 35% in accuracy and efficiency, while ensuring a high convergence rate of results at the level of 93.17%

Author Biographies

Oleg Sova, National Defense University of Ukraine

Doctor of Technical Sciences, Professor, Head of Center

Simulation Modeling Center

Oleksandr Stanovskyi, Odesa Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Design Information Technologies and Design

Taras Hurskyi, Research Institute of Military Intelligence

PhD, Associate Professor, Head of the Research Department

Valentyn Olshanskyi, Military Institute of Telecommunications and Informatization named after Heroes of Kruty

PhD, Associate Professor

Department of Combat Use of Communication Units

Oleksandr Volkov, Yevhenii Bereznyak Military Academy

PhD, Senior Researcher, Senior Lecturer

Special Department

Serhii Shostak, National University of Life and Environmental Sciences of Ukraine

PhD, Associate Professor

Department of Higher and Applied Mathematics

Vitalii Bezuhlyi, National Defense University of Ukraine

PhD, Professor

Department of Personnel Management and Training of Troops (Forces)

Command and Staff Institute for the Use of Troops (Forces)

Hryhorii Tikhonov, National Defense University of Ukraine

PhD, Senior Researcher, Head of Department

Department of Personnel Management and Training of Troops (Forces)

Command and Staff Institute for the Use of Troops (Forces)

Olena Chaikovska, National Defense University of Ukraine

Senior Lecturer

Department of Personnel Management and Training of Troops (Forces)

Command and Staff Institute for the Use of Troops (Forces)

Leonid Razarionov, Kharkiv National Automobile and Highway University

PhD, Accociate Professor

Departmant of Operation, Testing, and Service of Construction and Road Machinery

References

  1. Sova, O., Radzivilov, H., Shyshatskyi, A., Shvets, P., Tkachenko, V., Nevhad, S. et al. (2022). Development of a method to improve the reliability of assessing the condition of the monitoring object in special-purpose information systems. Eastern-European Journal of Enterprise Technologies, 2 (3 (116)), 6–14. https://doi.org/10.15587/1729-4061.2022.254122
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. https://doi.org/10.21303/2461-4262.2020.001353
  5. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. https://doi.org/10.15587/1729-4061.2020.208554
  6. Wang, J., Neil, M., Fenton, N. (2020). A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model. Computers & Security, 89, 101659. https://doi.org/10.1016/j.cose.2019.101659
  7. Matheu-García, S. N., Hernández-Ramos, J. L., Skarmeta, A. F., Baldini, G. (2019). Risk-based automated assessment and testing for the cybersecurity certification and labelling of IoT devices. Computer Standards & Interfaces, 62, 64–83. https://doi.org/10.1016/j.csi.2018.08.003
  8. Henriques de Gusmão, A. P., Mendonça Silva, M., Poleto, T., Camara e Silva, L., Cabral Seixas Costa, A. P. (2018). Cybersecurity risk analysis model using fault tree analysis and fuzzy decision theory. International Journal of Information Management, 43, 248–260. https://doi.org/10.1016/j.ijinfomgt.2018.08.008
  9. Folorunso, O., Mustapha, O. A. (2015). A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments. Applied Computing and Informatics, 11 (2), 116–129. https://doi.org/10.1016/j.aci.2014.07.001
  10. Mohammad, A. (2020). Development of the concept of electronic government construction in the conditions of synergetic threats. Technology Audit and Production Reserves, 3 (2 (53)), 42–46. https://doi.org/10.15587/2706-5448.2020.207066
  11. Bodin, L. D., Gordon, L. A., Loeb, M. P., Wang, A. (2018). Cybersecurity insurance and risk-sharing. Journal of Accounting and Public Policy, 37 (6), 527–544. https://doi.org/10.1016/j.jaccpubpol.2018.10.004
  12. Cormier, A., Ng, C. (2020). Integrating cybersecurity in hazard and risk analyses. Journal of Loss Prevention in the Process Industries, 64, 104044. https://doi.org/10.1016/j.jlp.2020.104044
  13. Hoffmann, R., Napiórkowski, J., Protasowicki, T., Stanik, J. (2020). Risk based approach in scope of cybersecurity threats and requirements. Procedia Manufacturing, 44, 655–662. https://doi.org/10.1016/j.promfg.2020.02.243
  14. Perrine, K. A., Levin, M. W., Yahia, C. N., Duell, M., Boyles, S. D. (2019). Implications of traffic signal cybersecurity on potential deliberate traffic disruptions. Transportation Research Part A: Policy and Practice, 120, 58–70. https://doi.org/10.1016/j.tra.2018.12.009
  15. Promyslov, V. G., Semenkov, K. V., Shumov, A. S. (2019). A Clustering Method of Asset Cybersecurity Classification. IFAC-PapersOnLine, 52 (13), 928–933. https://doi.org/10.1016/j.ifacol.2019.11.313
  16. Zarreh, A., Saygin, C., Wan, H., Lee, Y., Bracho, A. (2018). A game theory based cybersecurity assessment model for advanced manufacturing systems. Procedia Manufacturing, 26, 1255–1264. https://doi.org/10.1016/j.promfg.2018.07.162
  17. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. https://doi.org/10.1016/s0020-7373(86)80040-2
  18. Koval, M., Sova, O., Shyshatskyi, A., Artabaiev, Y., Garashchuk, N., Yivzhenko, Y. et al. (2022). Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms. Eastern-European Journal of Enterprise Technologies, 6 (4 (120)), 6–13. https://doi.org/10.15587/1729-4061.2022.268621
  19. Maccarone, A. D., Brzorad, J. N., Stone, H. M. (2008). Characteristics And Energetics Of Great Egret And Snowy Egret Foraging Flights. Waterbirds, 31 (4), 541–549. https://doi.org/10.1675/1524-4695-31.4.541
  20. Litvinenko, O., Kashkevich, S., Shyshatskyi, A., Dmytriieva, O., Neronov, S., Plekhova, G. et al.; Shyshatskyi, A. (Ed.) (2024). Information and control systems: modelling and optimizations. Kharkiv: TECHNOLOGY CENTER PC, 180. https://doi.org/10.15587/978-617-8360-04-7
Development of the method of multi-criteria evaluation of hierarchical systems

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Published

2025-06-25

How to Cite

Sova, O., Stanovskyi, O., Hurskyi, T., Olshanskyi, V., Volkov, O., Shostak, S., Bezuhlyi, V., Tikhonov, H., Chaikovska, O., & Razarionov, L. (2025). Development of the method of multi-criteria evaluation of hierarchical systems. Eastern-European Journal of Enterprise Technologies, 3(4 (135), 18–24. https://doi.org/10.15587/1729-4061.2025.331018

Issue

Vol. 3 No. 4 (135) (2025): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2025 Oleg Sova, Oleksandr Stanovskyi, Taras Hurskyi, Valentyn Olshanskyi, Oleksandr Volkov, Serhii Shostak, Vitalii Bezuhlyi, Hryhorii Tikhonov, Olena Chaikovska, Leonid Razarionov

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