Article

Received 25.06.2025

Revised 02.11.2025

Accepted 27.11.2025

Retrieved from Vol. 29, No. 2, 2025

Pages 94 -104

Prokudin, G., Chupailenko, O., Kolesnyk, Yu., Kozlov, A., Svirin, D., & Holovatiuk, M. (2025). Digitisation of the processes of customs clearance of goods with the calculation of the mathematical model of fuzzy logic. The National Transport University Bulletin, 29(2), 94-104. https://doi.org/10.33744/2308-6645-2025-2-29-94-104

Digitisation of the processes of customs clearance of goods with the calculation of the mathematical model of fuzzy logic

Georgii Prokudin Oleksii Chupailenko Yurii Kolesnyk Arkadii Kozlov Dmytro Svirin Mykhailo Holovatiuk

The relevance of this study arises from the growing reliance on automated customs clearance systems that generate recommendations, alerts, and justifications in order to reduce processing time. However, the effectiveness of such systems depends on a scientifically grounded methodology for determining the optimal frequency of risk profile reviews and for improving quantitative risk assessment. The purpose of the study was to develop a mathematical model for verifying the accuracy of customs clearance using the apparatus of fuzzy logic in international cargo transportation. The research methods included the development of mathematical models and algorithms for their implementation. In particular, the mathematical apparatus of fuzzy logic was applied to determine the correctness of customs clearance of goods. It was shown that the identification of customs clearance objects using fuzzy logic apparatus allows to automate the processing of information that is given in the customs declaration, the consumer documents and technical documentation. And which is submitted to the customs authorities with the control of the legalness of the classification of goods using the apparatus of fuzzy logic. Recommendations were developed for the use of the fuzzy logic apparatus in an automated expert system to determine the correctness of customs clearance of goods. This system was intended to enhance control over the correctness of goods classification during customs clearance and to prevent evasion by participants of foreign economic activity from paying taxes in full and from complying with non-tariff regulation measures through violations of declaration rules for goods crossing the customs border of Ukraine. It was also aimed at reducing the time of customs clearance, providing direct solutions to complex and contradictory classification problems arising during clearance, and avoiding errors in the classification of goods

information automation; risk management; non-tariff regulation; logistics operators; transport monitoring
  1. Berestov, I., Shelehan, H., & Berestova, T. (2022). Digitalization of customs control processes and customs clearance of cargo on railway transport. Central Ukrainian Scientific Bulletin. Technical Sciences, 5(36), 291-297. doi: 10.32515/2664-262X.2022.5(36).
  2. Chen, B., van Rijnsoever, F.J., Molenhuis, M., van Dijk, D., Tan, Y.-H., & Rukanova, B. (2021). The use of machine learning to identify the correctness of HS code for the customs import declarations. In 2021 IEEE 8th international conference on data science and advanced analytics (DSAA). New York: IEEE. doi: 10.1109/DSAA53316.2021.9564203.
  3. Chupailenko, O.A., Bilokur, M.V., & Polishchuk, R.V. (2023). Identification of transport and customs risks in international freight transportation using the expert assessment method. Scientific Notes of V.I. Vernadsky TNU. Series: Technical Sciences, 34(73), 329-335. doi: 10.33744/2308-6645-2022-3-53-409-426.
  4. Demchenko, E.M., & Dorosh, O.E. (2023). Transport systems and transportation technologies. Collection of Scientific Works of DNUZT, 25, 70-83. doi: 10.15802/TSTT2023/284496.
  5. Dong, Z., & Tian, M. (2025). Fuzzy logic-based risk assessment model for cross-border e-commerce logistics supply chain. Journal of Circuits, Systems and Computers. doi: 10.1142/S0218126626500453.
  6. In 2024, customs authorities of the State Customs Service detected violations of customs regulations amounting to UAH 18 billion. (2025). Retrieved from https://customs.gov.ua/news/zagalne-20/post/za-2024-rik-mitnitsi-derzhmitsluzhbi-viiavili-porushen-mitnikh-pravil-na-18-mlrd-grn-1967.
  7. Ishchuk, M., Ishchuk, O., & Khrypko, S. (2024). Sustainable digital evolution: Transforming the Ukrainian customs service. European Journal of Sustainable Development, 13(1), 217-228. doi: 10.14207/ejsd.2024.v13n1p217.
  8. AnchorIvashova, L.M., & Kyda, L.I. (2020). Digitalization of customs procedures: The current state and prospects of customs development. Public Administration and Customs Administration, 3(22), 218-230. doi: 10.32836/2310-9653-2019-3-218-230.
  9. Kim, S.-B., & Kim, D. (2020). ICT implementation and its effect on public organizations: The case of digital customs and risk management in Korea. Sustainability, 12(8), article number 3421. doi: 10.3390/su12083421.
  10. Kostiana, O.V., Karpova, V.V., & Ostapenko, V.M. (2021). Risk management in customs affairs as a factor in ensuring customs security in a period of economic downturn. Business Inform, 11, 404-410. doi: 10.32983/2222-4459-2021-11-404-410.
  11. Kovtunov, Yu.O., Makohon, O.A., Isakov, O.V., Babkin, Yu.V., Kalinin I.V., & Lazuta, R.R. (2020). Use of the mathematical apparatus of fuzzy logic for fuzzification and algorithmization of the interactive monitoring system of transport communications. Control, Navigation and Communication Systems, 3(61), 64-68. doi: 10.26906/SUNZ.2020.3.064.
  12. Kveliashvili, I.M., & Totska, T.S. (2021). Implementation of modern customs management concepts of European countries into national practice. Public Administration in the Sphere of State Security and Customs Affairs, 2(29), 74-80. doi: 10.32836/2310-9653-2021-2.12.
  13. Law of Ukraine No. 2697-IX “About the Customs Tariff of Ukraine”. (2023, January). Retrieved from https://zakon.rada.gov.ua/laws/show/2697-20?lang=en#Text.
  14. Lebid, V., Anufrieva, T., & Savenko, G. (2020). Studies of the efficiency of simplification of customs formalities on the basis of digitalization. Technological Audit and Production Reserves, 1(4(57)), 49-53. doi: 10.15587/2706-5448.2021.225627.
  15. AnchorLemeha, R. (2020). Automation and informatization of customs procedures as priority directions of improvement of administration of customs regimes in Ukraine. Entrepreneurship, Economy and Law, 11, 126-133.
  16. Luo, N., Yu, H., You, Z., Li, Y., Zhou, T., Jiao, Y., Han, N., Liu, C., Jiang, Z., & Qiao, S. (2023). Fuzzy logic and neural network-based risk assessment model for import and export enterprises: A review. Journal of Data Science and Intelligent Systems, 1(1), 2-11. doi: 10.47852/bonviewJDSIS32021078.
  17. Mishchenko, A.V., Kurylo, O.V., & Zolotukhina, O.A. (2020). A fuzzy model for assessing information security risks and maintaining the protection level of ERP systems. Telecommunication and Information Technologies, 1(66), 142-150. doi: 10.31673/2412-4338.2020.011451.
  18. Moreno-Palacio, D.P., González-Calderón, C.A., López-Ospina, H., Gil-Marin, J.K., & Posada-Henao, J.J. (2023). Freight tour synthesis based on entropy maximization with fuzzy logic constraints. Transportation, 51, 2323-2357. doi: 10.1007/s11116-023-10407-y.
  19. Muzylyov, D., Medvediev, I., & Pavlenko, O. (2024). Risk factor assessment in agricultural supply chain by fuzzy logic. IOP Conference Series: Earth and Environmental Science, 1376(1), article number 012038. doi: 10.1088/1755-1315/1376/1/012038.
  20. Osipchuk, Yu.O. (2024). Risk management in customs affairs under martial law conditions. Economics. Management. Current Issues of Science and Practice, 2(108), 16-21. doi: 10.26642/ema-2024-2(108)-16-21.
  21. Pálková, A., & Mašek, J. (2024). Fuzzy logic as a decision-making tool for transport request selection. Transportation Research Procedia, 77, 116-122. doi: 10.1016/j.trpro.2024.01.015.
  22. Tyshchenko, V.F., Ostapenko, V.M., & Karpova, V.V. (2022). Digitization in the customs risk management. Economics and Society, 37, 60-65. doi: 10.32782/2524-0072/2022-37-35.
  23. Vijayakumar, S. (2025). Technology-centric and data-driven customs risk management for supply chain security. World Customs Journal, 19(1), 38-63. doi: 10.55596/001c.131745.
  24. World Customs Organization. (n.d.). Overview. Retrieved from https://www.wcoomd.org/en/topics/nomenclature/overview.aspx.
  25. Zheng, C., Cao, J., & Zhou, H. (2025). Optimization of emergency supplies customs clearance strategy based on K-means clustering. Frontiers in Public Health, 13, article number 1481328. doi: 10.3389/fpubh.2025.1481328.

https://doi.org/10.33744/2308-6645-2025-2-29-94-104