Development of a hybrid fuzzy decision support system for assessing the effectiveness of artillery fire in conditions of uncertain disturbances

Authors

DOI:

https://doi.org/10.15587/2706-5448.2026.351953

Keywords:

artillery fire, efficiency assessment, barrel wear, decision support system, fuzzy logic

Abstract

The object of research is the processes of determining the effectiveness of artillery fire under conditions of uncertain disturbances, which include wear of the gun barrel, deterioration of the quality of charges and shells of a certain batch. This work addresses the problem of ensuring the adequacy of assessing the effectiveness of artillery fire in cases where the parameters of the gun barrel, the quality of powder charges or shells deviate from the nominal values and are determined inaccurately.

The research used fuzzy logic methods to formalize decision-making processes under conditions of uncertainty, as well as methods of mathematical modeling and statistical analysis to simulate firing sequences and determine effectiveness estimates.

A hybrid fuzzy-logical decision support system (DSS) has been developed and tested, which allows for a comprehensive and highly accurate assessment of the effectiveness of artillery fire. When forming estimates, the DSS takes into account three key parameters that characterize the most significant sources of uncertainty: barrel wear, deterioration of the quality of charges, deterioration of the quality of shells.

The results of computational experiments for various realistic artillery fire scenarios were obtained. In turn, artillery installations with initial barrel wear values of 0.1 and 0.25 were studied when using charges and shells of different quality. During the experiments, it was established that the proposed system provides an adequate, practically useful assessment of the fire efficiency of artillery installations under realistic conditions of uncertainty. In particular, the calculated efficiency values during the entire firing process changed by no more than 12% in the first three experiments and no more than 21% in the next three.

The developed DSS can be used in modern artillery complexes to increase the efficiency of making control decisions, reduce the proportion of misses, save scarce ammunition and reduce the risk of damage to equipment and personnel.

Author Biographies

Maksym Maksymov, Odesа Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Computer Technologies of Automation

Oleksiy Kozlov, Petro Mohyla Black Sea National University

Doctor of Technical Sciences, Professor

Department of Intelligent Information Systems

Oleksiy Maksymov, Institute of Naval Forces of the National university «Odesa Maritime Academy»

PhD, Associate Professor

Department of Radio Engineering Armament, Communications and Robotics

Ruslan Riaboshapka, Odesа Polytechnic National University

Department of Computer Technologies of Automation

References

  1. Bartulović, V., Trzun, Z., Hoić, M. (2023). Use of Unmanned Aerial Vehicles in Support of Artillery Operations. Strategos, 7 (1), 71–92. Available at: https://hrcak.srce.hr/305562
  2. Chávez, K., Swed, O. (2023). Emulating underdogs: Tactical drones in the Russia-Ukraine war. Contemporary Security Policy, 44 (4), 592–605. https://doi.org/10.1080/13523260.2023.2257964
  3. Oprean, L.-G. (2023). Artillery and Drone Action Issues in the War in Ukraine. Scientific Bulletin, 28 (1), 73–78. https://doi.org/10.2478/bsaft-2023-0008
  4. Świętochowski, N. (2023). Field Artillery in the defensive war of Ukraine 2022-2023Part I. Combat potential, tasks and tactics. Scientific Journal of the Military University of Land Forces, 210 (4), 341–358. https://doi.org/10.5604/01.3001.0054.1631
  5. Świętochowski, N. (2024). Field Artillery in the defensive war of Ukraine 2022-2023. Part II. Methods of task implementation. Scientific Journal of the Military University of Land Forces, 211 (1), 57–76. https://doi.org/10.5604/01.3001.0054.4136
  6. Brzozowski, M., Pakowski, M., Nowakowski, M., Myszka, M., Michalczewski, M. (2019). Radars with the function of detecting and tracking artillery shells – selected methods of field testing. 2019 IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace). Turin, 429–434. https://doi.org/10.1109/metroaerospace.2019.8869656
  7. Kislitsyn, A., Dorofeev, N. (2021). Directions for the development of domestic self-propelled artillery systems based on the analysis of samples of artillery weapons from the leading countries of the world. Social Development and Security, 11 (6), 98–107. https://doi.org/10.33445/sds.2021.11.6.7
  8. Ganjiyev, S. J., Usmonov, S. R., Karimov, A. Kh. (2023). Use of artillery in modern war: a brief analysis of the Ukrainian conflict. Galaxy International Interdisciplinary Research Journal, 11 (3), 118–121. Available at: https://internationaljournals.co.in/index.php/giirj/article/view/3646
  9. Oprean, L.-G. (2020). Artillery from the Perspective of Firing Effects and Ensured Capabilities. Scientific Bulletin, 25 (2), 107–113. https://doi.org/10.2478/bsaft-2020-0015
  10. Petlyuk, I., Shchavinsky, Y. (2021). Use of simulation modeling systems for determination of appropriate characteristics of prospective artillery weapons. Collection of Scientific Works of Odesa Military Academy, 1 (14), 11–22. https://doi.org/10.37129/2313-7509.2020.14.1.11-22
  11. STANAG 4355 The Lieske modified point mass and five degrees of freedom trajectory models – AOP-4355 EDITION A (2022). Washington: United States Department of Defense. Available at: https://quicksearch.dla.mil/qsDocDetails.aspx?ident_number=107513
  12. Zhuravlev, А., Orlov, S., Shuliakov, S. (2020). Mathematical model of the flight path of a projectile of a long-range artillery system. Systems of Arms and Military Equipment, 3 (63), 62–68. https://doi.org/10.30748/soivt.2020.63.09
  13. Khalil, M. (2022). Study on modeling and production inaccuracies for artillery firing. Archive of Mechanical Engineering, 69 (1), 165–183. https://doi.org/10.24425/ame.2021.139802
  14. Khudov, H., Yuzova, I., Lisohorskyi, B., Solomonenko, Y., Mykus, S., Irkha, A. et al. (2021). Development of methods for determining the coordinates of firing positions of roving mortars by a network of counter-battery radars. EUREKA: Physics and Engineering, 3, 140–150. https://doi.org/10.21303/2461-4262.2021.001821
  15. Shim, Y., Atkinson, M. P. (2018). Analysis of artillery shoot‐and‐scoot tactics. Naval Research Logistics, 65 (3), 242–274. https://doi.org/10.1002/nav.21803
  16. Grishyn, M., Maksymova, O., Kirkopulo, K., Klymchuk, O. (2025). Development of methods of artillery control for suppression of an enemy amphibious operation in video game simulations. Technology Audit and Production Reserves, 1 (2 (81)), 26–33. https://doi.org/10.15587/2706-5448.2025.321797
  17. Wang, X., Li, X., Sun, Q., Xia, C., Chen, Y.-H. (2025). Improved Manta Ray Foraging Optimization for PID Control Parameter Tuning in Artillery Stabilization Systems. Biomimetics, 10 (5), 266. https://doi.org/10.3390/biomimetics10050266
  18. Xiao, H., Yang, G., Ge, J. (2017). Surrogate-based multi-objective optimization of firing accuracy and firing stability for a towed artillery. Journal of Vibroengineering, 19 (1), 290–301. https://doi.org/10.21595/jve.2016.17108
  19. Mady, M., Khalil, M., Yehia, M. (2020). Modelling and Production of artillery firing-tables: case-study. Journal of Physics: Conference Series, 1507 (8), 082043. https://doi.org/10.1088/1742-6596/1507/8/082043
  20. Manev, N., Achkoski, J., Petreski, D., Gocic, M., Rancic, D. (2017). Smart field artillery information system: Model development with an emphasis on collisions in single sign-on authentication. Vojnotehnicki Glasnik, 65 (2), 442–463. https://doi.org/10.5937/vojtehg65-12703
  21. Sun, Y., Zhang, S., Lu, G., Zhao, J., Tian, J., Xue, J. (2022). Research on a Simulation Algorithm for Artillery Firepower Assignment According to Region. 2022 3rd International Conference on Computer Science and Management Technology (ICCSMT). Shanghai, 353–356. https://doi.org/10.1109/iccsmt58129.2022.00082
  22. Wang, L., Chen, Z., Yang, G. (2021). An Uncertainty Analysis Method for Artillery Dynamics with Hybrid Stochastic and Interval Parameters. Computer Modeling in Engineering & Sciences, 126 (2), 479–503. https://doi.org/10.32604/cmes.2021.011954
  23. Litsman, A., Nesterov, D. (2020) Definitions degree of influence of individual factors on mechanical equipment failure rate during artillery operation. Collection of scientific works of the National Academy of the State Border Guard Service of Ukraine. Series: Military and Technical Sciences, 80, 283–99. https://doi.org/10.32453/3.v80i2.204
  24. Kumar, D., Kalra, S., Jha, M. S. (2022). A concise review on degradation of gun barrels and its health monitoring techniques. Engineering Failure Analysis, 142, 106791. https://doi.org/10.1016/j.engfailanal.2022.106791
  25. Shen, C., Zhou, K., Lu, Y., Li, J. (2019). Modeling and simulation of bullet-barrel interaction process for the damaged gun barrel. Defence Technology, 15 (6), 972–986. https://doi.org/10.1016/j.dt.2019.07.009
  26. Dobrynin, Y., Maksymov, M., Boltenkov, V. (2020). Development of a method for determining the wear of artillery barrels by acoustic fields of shots. Eastern-European Journal of Enterprise Technologies, 3 (5 (105)), 6–18. https://doi.org/10.15587/1729-4061.2020.206114
  27. Boltenkov, V., Brunetkin, O., Dobrynin, Y., Maksymova, O., Kuzmenko, V., Gultsov, P. et al. (2021). Devising a method for improving the efficiency of artillery shooting based on the Markov model. Eastern-European Journal of Enterprise Technologies, 6 (3 (114)), 6–17. https://doi.org/10.15587/1729-4061.2021.245854
  28. Toshev, O., Kirkopulo, K., Klymchuk, O., Maksymov, M. (2025). Optimization of ammunition preparation strategies for modern artillery operations in computer simulation. Technology Audit and Production Reserves, 2 (2 (82)), 50–57. https://doi.org/10.15587/2706-5448.2025.326225
  29. Mendel, J. M. (2024). Explainable Uncertain Rule-Based Fuzzy Systems. Cham: Springer, 580. https://doi.org/10.1007/978-3-031-35378-9
  30. Kondratenko, Y., Kozlov, O., Zheng, Y., Wang, J., Kuzmenko, V., Aleksieieva, A. B. (2024) Bio-inspired optimization of fuzzy control system for inspection robotic platform: comparative analysis of hybrid swarm methods. CEUR Workshop Proceedings, 3711, 109–123. Available at: https://ceur-ws.org/Vol-3711/paper7.pdf
  31. Skakodub, O., Kozlov, O., Kondratenko, Y. (2021). Optimization of Linguistic Terms’ Shapes and Parameters: Fuzzy Control System of a Quadrotor Drone. 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 566–571. https://doi.org/10.1109/idaacs53288.2021.9660926
  32. Kozlov, O. (2021). Information Technology for Designing Rule bases of Fuzzy Systems using Ant Colony Optimization. International Journal of Computing, 20 (4), 471–486. https://doi.org/10.47839/ijc.20.4.2434
  33. Congxiang, L., Kozlov, O., Kondratenko, G., Aleksieieva, A. (2024). Decision Support System for Maintenance Planning of Vortex Electrostatic Precipitators Based on IoT and AI Techniques. Research Tendencies and Prospect Domains for AI Development and Implementation. New York: River Publishers, 87–105. https://doi.org/10.1201/9788770046947-5
  34. Solesvik, M., Kondratenko, Y., Kondratenko, G., Sidenko, I., Kharchenko, V., Boyarchuk, A. (2017). Fuzzy decision support systems in marine practice. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 1–6. https://doi.org/10.1109/fuzz-ieee.2017.8015471
  35. Maksymov, M., Kozlov, O., Dobrynin, Y., Sidelnykov, O., Kapalin, V., Vodichev, V. (2025). Verification of artillery systems’ shots: selection of the most appropriate sensors based on the fuzzy evaluation model. EUREKA: Physics and Engineering, 2, 199–210. https://doi.org/10.21303/2461-4262.2025.003662
  36. Pedrycz, W., Li, K., Reformat, M. (2015). Evolutionary Reduction of Fuzzy Rule-Based Models. Fifty Years of Fuzzy Logic and Its Applications. Cham: Springer, 459–481. https://doi.org/10.1007/978-3-319-19683-1_23
  37. Volna, E. (2017). Fuzzy-based decision strategy in real-time strategic games. AIP Conference Proceedings, 1906, 080002. https://doi.org/10.1063/1.5012346
  38. Werners, B., Kondratenko, Y. (2017). Alternative Fuzzy Approaches for Efficiently Solving the Capacitated Vehicle Routing Problem in Conditions of Uncertain Demands. Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Cham: Springer, 521–543. https://doi.org/10.1007/978-3-319-69989-9_31
  39. Raskin, L., Sira, O. (2020). Development of methods for extension of the conceptual and analytical framework of the fuzzy set theory. Eastern-European Journal of Enterprise Technologies, 6 (4 (108)), 14–21. https://doi.org/10.15587/1729-4061.2020.217630
  40. Kozlov, O. V. (2021). Optimal Selection of Membership Functions Types for Fuzzy Control and Decision Making Systems. CEUR Workshop Proceedings, 2853, 238–247. Available at: https://ceur-ws.org/Vol-2853/paper22.pdf
  41. Kozlov, O., Kondratenko, G., Aleksieieva, A., Maksymov, M. (2025). Complex Structural-Parametric Optimization of Fuzzy Control Systems Based on Bioinspired Algorithms. CEUR Workshop Proceedings, 4048, 1–15. Available at: https://ceur-ws.org/Vol-4048/paper01.pdf
  42. Kozlov, O., Kondratenko, G., Aleksieieva, A., Maksymov, M., Tarakhtij, O. (2024). Swarm optimization of the drone’s intelligent control system: comparative analysis of hybrid techniques. CEUR Workshop Proceedings, 3790, 1–12. https://ceur-ws.org/Vol-3790/paper01.pdf
  43. Maksymov, M., Kozlov, O., Shynder, A., Maksymova, O., Aleksieieva, A. (2025). Development of mathematical models for temperature control objects in thermal destruction systems based on transient process identification. EUREKA: Physics and Engineering, 3, 207–220. https://doi.org/10.21303/2461-4262.2025.003802
Development of a hybrid fuzzy decision support system for assessing the effectiveness of artillery fire in conditions of uncertain disturbances

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Published

2026-04-30

How to Cite

Maksymov, M., Kozlov, O., Maksymov, O., & Riaboshapka, R. (2026). Development of a hybrid fuzzy decision support system for assessing the effectiveness of artillery fire in conditions of uncertain disturbances. Technology Audit and Production Reserves, 2(2(88), 48–57. https://doi.org/10.15587/2706-5448.2026.351953

Issue

Vol. 2 No. 2(88) (2026): Information and control systems

Section

Systems and Control Processes

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Copyright (c) 2026 Maksym Maksymov, Oleksiy Kozlov, Oleksiy Maksymov, Ruslan Riaboshapka

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