Evolutionary Optimization Technologies in Innovation Management: Evidence From the Transport and Logistics Industry under Modern Dynamic Systemic Risks and Uncertainty

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Published: 18.07.2026

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Krasnyuk Maxim
Kyiv National Economic University named after Vadym Hetman image/svg+xml

Abstract

The article considers the possibilities of using evolutionary optimization technologies in innovative management of the transport and logistics industry under conditions of uncertainty, turbulence and systemic risks. The advantages of evolutionary computing in solving multi-criteria management problems are substantiated and the prospects for their integration with artificial intelligence and digital transformation technologies are determined.

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Data Availability Statement

All data supporting the findings of this study are presented in the text of the scientific work.

Issue

No. 301 (2026): 10th ISPC «Experimental and Theoretical Research in Modern Science» (July 16-18, 2026; Toronto, Canada).

Section

Information and Web technologies
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Author Biography

Krasnyuk Maxim, Kyiv National Economic University named after Vadym Hetman

Associate Professor; Department of Computer Science; Kyiv National Economic University named after Vadym Hetman; Ukraine

How to Cite

Krasnyuk, M. (2026). Evolutionary Optimization Technologies in Innovation Management: Evidence From the Transport and Logistics Industry under Modern Dynamic Systemic Risks and Uncertainty. Scientific Collection «InterConf», 301, 132-140. https://interconf.openpubarchive.com/index.php/proceeding/article/view/31

References

Maksym Naumenko (2024). Modern concepts of innovation management at enterprises. Scientific innovations and advanced technologies No. 6(34) (2024). DOI: https://doi.org/10.52058/2786-5274-2024- 6(34)-435-449

Ostapenko T., Onopriienko D., Hrashchenko I., Palyvoda O., Krasniuk S., Danilova E. (2022) Research of impact of nanoeconomics on the national economic system development. Innovative development of national economies: collective monograph. – Kharkiv: PC TECHNOLOGY CENTER, 2022. – pp. 46 -70. URL: https://er.knutd.edu.ua/handle/123456789/23169

Hrashchenko, I. S. (2011). Problemy ta osoblyvosti prohnozuvannia na pidpryiemstvakh sfery posluh [Problems and features of forecasting at service enterprises]. Ekonomichnyi visnyk Donbasu - Economic Bulletin of Donbass, (3(25)), 175 -179. https://www.evd- journal.org/download/2011/2011-3/Ek_visnyk_3_2011.pdf [In Ukrainian].

Hrashchenko, I. S., & Pryshchepa, N. P. (2016). Formuvannia konkurentnoho potentsialu pidpryiemstv za umov zrostannia mizhnarodnoi konkurentsii [Formation of the competitive potential of enterprises in conditions of growing international competition]. Skhidna Yevropa: ekonomika, biznes ta upravlinnia - Eastern Europe: economics, business and management, (4), 118–122.

Naumenko, М. (2024). Methodology of determining factors of activity efficiency and competitive position of the enterprise on the market in crisis conditions. Scientific innovations and advanced technologies, № 7(35) (2024). DOI: https://doi.org/10.52058/2786- 5274-2024-7(35)-648-665 [in Ukrainian].

Nevmerzhytska S. M. (2018). Formation of a strategy for the innovative development of enterprises in conditions of uncertainty. Scientific Bulletin of the Kherson State University. Series: Economic Sciences. 2018. Vol. 32. pp. 99 -103. URL: https://ej.journal.kspu.edu/index.php/ej/article/view/422/418.

Krasnyuk M.T., Naumenko M.A. (2024) Efektyvne zastosuvannia henetychnykh alhorytmiv u vyrishenni bahatoekstremumnykh optymizatsiinykh zadach v menedzhmenti konkurentnoho pidpryiemstva [Effective application of genetic algorithms in solving multi - extremum optimization problems in the management of a competitive enterprise]. Grail of Science, № 41, June 2024, 65 -73. URL: https://archive.journal-grail.science/index.php/2710- 3056/issue/view/05.07.2024/29 [in Ukrainian].

Krasnyuk, M., & Kustarovskiy, O. (2017). The development of the concept and set of practical measures of anticrisis logistics management in the current Ukraine conditions. Zarządzanie. Teoria i Praktyka, Wyższa Szkoła Menedżerska w Warszawie, (1 (19)), pp. 31–37.

Оksana Karpenko, Olena Palyvoda, & Olena Bondarenko (2018). Simulation modelling of strategic development of transport and logistics clusters in Ukraine. Baltic Journal of Economic Studies, 4 (2), 93-98. doi: 10.30525/2256-0742/2018-4-2-93-98

Kulynych Y., Krasnyuk M., Krasniuk S. (2022). Efficiency of evolutionary algorithms in solving optimization problems on the example of the fintech industry. Grail of Science, № 14-15, May 2022, (pp. 63 -70). DOI: https://doi.org/10.36074/grail -of- science.27.05.2022

Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. John Wiley & Sons.

Michalewicz, Z., & Fogel, D. B. (2004). How to solve it: Modern heuristics (2nd ed.). Springer.

Storn, R., & Price, K. (1997). Differential evolution -A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11 (4), 341 –359. https://doi.org/10.1023/A:1008202821328

Simchi-Levi, D., Kaminsky, P., & Simchi -Levi, E. (2021). Designing and managing the supply chain: Concepts, strategies, and case studies (4th ed.). McGraw-Hill Education.

Pourhejazy, P., & Kwon, O. K. (2016). The new generation of operations research methods in supply chain optimization: A review. Sustainability, 8 (10), Article 1033. https://doi.org/10.3390/su8101033

Potvin, J. -Y. (2009). State -of-the-art survey -Evolutionary algorithms for vehicle routing. INFORMS Journal on Computing, 21(4), 518–548. https://doi.org/10.1287/ijoc.1080.0312

Skitsko, V. I., & Voinikov, M. Y. (2024). Supply chain management using evolutionary algorithms. Problemy Ekonomiky, 3, 240 –248. https://doi.org/10.32983/2222-0712-2024-3-240-248