Advances and Emerging Trends in Evolutionary Computation for Complex Optimization under Uncertainty and Systemic Risks: Insights from the Transport and Logistics

Maxim Krasnyuk

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Published: 16.09.2026

Maxim Krasnyuk

Kyiv National Economic University named after Vadym Hetman

Abstract

The article explores innovative approaches, current trends and prospects for the application of evolutionary computing to solve complex and complex problems of the transport and logistics industry in conditions of turbulence, uncertainty and systemic risks. The need to transition from static optimization to dynamic, multi-criteria, adaptive and risk-oriented models is substantiated. The prospects for hybridization of evolutionary algorithms with artificial intelligence, machine learning, Big Data and digital twins are considered. It is shown that the further development of evolutionary computing contributes to the formation of adaptive, intelligent and sustainable logistics systems capable of quickly responding to environmental changes and systemic risks.

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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. 309 (2026): 14th ISPC «Global and Regional Aspects of Sustainable Development» (September 16-18, 2026; Copenhagen, Denmark)

Section

Modeling and Nanotechnology

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

Author Biography

Maxim Krasnyuk, Kyiv National Economic University named after Vadym Hetman

Associate Professor, Department of Computer Science

How to Cite

Krasnyuk, M. (2026). Advances and Emerging Trends in Evolutionary Computation for Complex Optimization under Uncertainty and Systemic Risks: Insights from the Transport and Logistics. Scientific Collection «InterConf», 309, 106–114. https://interconf.openpubarchive.com/index.php/proceeding/article/view/99

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