OPTIMIZATION OF LOGISTICS PROCESSES IN THE ECONOMY: THE ROLE OF ANALYTICAL MODELING AND FUZZY LOGIC

Authors

  • Andrii Papinko cand.sc.(econ.), senior lecturer at the department of transport and logistics, West Ukrainian National University, Ternopil
  • Ihor Loik candidate for the third (educational and scientific) level of higher education in the specialty “Management”, West Ukrainian National University, Ternopil
  • Serhii Myshko candidate for the third (educational and scientific) level of higher education in the specialty “Management”, West Ukrainian National University, Ternopil
  • Yurii Koval candidate for the third (educational and scientific) level of higher education in the specialty “Management”, West Ukrainian National University, Ternopil

DOI:

https://doi.org/10.37332/

Keywords:

logistics systems, analytical modeling, multifactor uncertainty, supply chains, fuzzy logic

Abstract

Papinko A.I., Loik I.O., Myshko S.A., Koval Yu.B. OPTIMIZATION OF LOGISTICS PROCESSES IN THE ECONOMY: THE ROLE OF ANALYTICAL MODELING AND FUZZY LOGIC

Purpose. The aim of the study is to substantiate the possibilities of optimizing logistics processes based on the application of analytical modeling methods and fuzzy logic tools to improve the efficiency of managing material, information, and financial flows in conditions of uncertainty and risk.

Methodology of research. The methodological basis of the study is a systematic and comprehensive approach to the analysis of logistics systems in conditions of multifactorial uncertainty. In the course of the study, methods of theoretical generalization, scientific abstraction, analysis, and synthesis were used to identify the limitations of traditional statistical approaches to assessing the economic efficiency of logistics processes. A comparative analysis of classical methods of mathematical statistics and modern tools for modeling fuzzy data was applied. The theoretical and methodological justification for the use of the apparatus of fuzzy set theory and fuzzy logic was carried out on the basis of formalization of expert-linguistic, interval, and contradictory information characteristic of logistics systems. The study is conceptual and analytical in nature and aims to develop approaches to building adaptive models for forecasting the economic efficiency of logistics projects.

Findings. The feasibility of using analytical models based on fuzzy logic to optimize logistics processes in an unstable economic environment has been substantiated. Key limitations of traditional statistical methods when working with incomplete, contradictory, and non-statistical information have been identified. An approach to integrating multiple logistics functions within a single adaptive model is proposed, which improves the accuracy of economic efficiency forecasting and reduces the risks of management decisions. The need for a unified description of material flows, taking into account their multidimensionality and dynamism, has been proven.

Originality. Theoretical and methodological foundations of analytical modeling of logistics systems based on fuzzy logic have been developed, taking into account the multifactorial uncertainty of the Ukrainian economic environment. The approach to assessing the economic efficiency of logistics processes has been improved by integrating qualitative and quantitative parameters within a single model. The provisions on the use of expert-linguistic information in forecasting and managing complex dynamic systems have been further developed.

Practical value. The practical significance of the results obtained lies in the possibility of their use for the development of software and mathematical support for decision-making systems in the field of logistics. The proposed approaches can be implemented in the activities of enterprises to improve the accuracy of forecasting, optimize material flows, minimize costs, and reduce the level of risk in conditions of uncertainty. The results obtained can also be used in the educational process in the training of specialists in logistics systems management and economic and mathematical modeling.

Key words: logistics systems, analytical modeling, multifactor uncertainty, supply chains, fuzzy logic.

 

References

1. Merkt, O.R. and Merkt, O.V. (2024), “Theory of modeling of transport and logistics systems. International experience”, Proiektnyi ta lohistychnyi menedzhment. Novi znannia na bazi dvokh metodolohii : zb. materialiv konf. IV-yi Mizhnar. nauk.-prakt. konf. [Project and logistics management. New knowledge based on two methodologies: collection of materials of the 4th International Scientific and Practical Conference], (Odesa, November 7-8, 2024) ONMU, Odesa, Ukraine.

2. Yanush, M.V. (2017), “Forecasting the economic efficiency of logistics systems”, Aktualni zadachi suchasnykh tekhnolohii : Zbirnyk tez dopovidei Ⅵ Mizhnarodnoi naukovo-tekhnichnoi konferentsii molodykh uchenykh ta studentiv [Actual problems of modern technologies : Collection of abstracts of the Ⅵ International scientific and technical conference of young scientists and students] (Ternopil, November 16-17 2017), TNTU, Ternopil, Ukraine, P. 86-87.

3. Potapova, N., Volontyr, L. and Zelinska, O. (2022), “Mathematical and computer modeling of the functioning of logistics processes and systems”, Visnyk Khmelnytskoho natsionalnoho universytetu. Technicni nauky, no. 307, Volume 2, DOI: doi.org/10.31891/2307-5732-2022-307-2-73-79.

4. Mohanty Mahamaya, Shankar Ravi. (2019), “A hierarchical analytical model for performance management of integrated logistics”, Journal of Management Analytics, Vol. 6:2. DOI.org/10.1080/23270012.2019.1608326.

5. Nesterenko, O.V., Savenko, O.I., and Falovskyi, O.O. (2016), Intelektualni systemy pidtrymky pryiniattia rishen: Navch. posibn. [Intelligent decision support systems: Study guide] (P.I. Bidiukov, Ed.). Natsionalna akademiia upravlinnia, Kyiv, 188 p.

6. Bezuha, L.S., Yurchenko, N.I., Ilchenko, T.V., Palchyk, I.M., and Volovyk, D.V. (2021), Lohistyka: navch. posib. [Logistics: Study guide]. Porohy, Dnipro 252 p.

7. Bilichenko, V.V., Buriennikov, Yu.Yu. and Romaniuk, S.O. (2017), Osnovy lohistyky: navchalny posibnyk [Fundamentals of logistics: Study guide]. VNTU, Vinnytsia, Ukraine, 129 p.

8. Marchenko, V.M., and Shutiuk, V.V. (2018), Lohistyka: Pidruchnyk [Logistics: Textbook]. Artek, Kyiv, Ukraine, 312 p.

9. Chorna, O.V., Popovych, P.V., Maiak, M.M., et al. (2023). “Increasing the efficiency of supply chains”. Tsentralnoukrainskyi naukovyi visnyk. Tekhnichni nauky, no. 7(38) DOI: https://doi.org/10.32515/2664-262X.2023.7(38).1.258-265.

10. Zinovieva, O.H. (2025), “Using fuzzy logic in decision-making systems”. Visnyk Khersonskoho natsionalnoho tekhnichnoho universytetu, no. 2(1). DOI: https://doi.org/10.35546/kntu2078-4481.2025.1.2.10.

11. Falovich, N.M., Popovich, P.V. and Shevchuk, O.S. (2023), “Structure and potential of the logistics sector of Ukraine”. Tsentralnoukrainskyi naukovyi visnyk. Tekhnichni nauky, no. 7(38), pp. 280-286, DOI: https://doi.org/10.32515/2664-262X.2023.7(38).1.280-286

12. Popovych, P.V., Shevchuk, O.S., Dziura, V.O. and Babii, M.V. (2017), “Analysis of the Ukrainian road transport market”, Visnyk mashynobuduvannia ta transport, no. 2, pp. 124-130.

13. Dziadykevych, Yu.V., Falovych, N.M. and Popovych, P.V. (2022), “Foreign practice of regulating road transport”, Tsentralnoukrainskyi naukovyi visnyk. Tekhnichni nauky, no. 5(36), pp. 240-245, DOI: https://doi.org/10.32515/2664-262X.2022.5(36).2.240-245

14. Dombrovskyi, V., Dombrovskyi, M., Komar, M., Semaniuk, V. and Liakhovych, G., (2023), “Emerging Digital Technologies Driven Approach to Increase the Supply Chains Competitivity”, IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Dortmund, Germany, 2023, pp. 1241-1246, DOI: 10.1109/IDAACS58523.2023.10348902.

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Published

2025-06-30

Issue

No. 2 (2025): INNOVATIVE ECONOMY

Section

Статті

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How to Cite

“OPTIMIZATION OF LOGISTICS PROCESSES IN THE ECONOMY: THE ROLE OF ANALYTICAL MODELING AND FUZZY LOGIC”. INNOVATIVE ECONOMY, no. 2, June 2025, pp. 317-22, https://doi.org/10.37332/.