ARTIFICIAL INTELLIGENCE-DRIVEN OPTIMIZATION OF TRANSPORT OPERATIONS FOR REDUCING LOGISTICS COSTS AND CARBON FOOTPRINT

Abstract

Purpose. The purpose of this paper is to investigate ways to enhance the efficiency of transport and logistics systems in the context of global digital transformation and economic decarbonization. The study focuses on the optimization of transport routes using artificial intelligence technologies as a tool for reducing logistics costs and the carbon footprint of enterprises. Methodology. The research is based on a comprehensive methodological framework that integrates systemic, analytical, logical-structural, and comparative approaches. The study employs mathematical modeling, machine learning algorithms, big data analytics, and multi-criteria optimization techniques. To evaluate the model’s performance, a digital twin of the transport network was developed, allowing the simulation of various transportation scenarios in real time. The optimization model combines three key parameters-logistics costs, CO₂ emissions, and delivery time-using adjustable weighting coefficients according to enterprise-specific priorities. Findings. The results of simulation modeling demonstrate that the implementation of AI-driven routing technologies enables a reduction in fuel consumption by 18.7%, a decrease in average delivery time by 12.3%, a reduction in CO₂ emissions by 20.1%, and an overall decrease in logistics costs by 22.4%. These outcomes are consistent with global trends in the digital transformation of logistics and confirm the effectiveness of intelligent transport systems in achieving sustainable development objectives. The application of machine learning, genetic algorithms, and particle swarm optimization provided superior stability of solutions and adaptability to changing operational conditions. Practical implications. The developed model can be applied by enterprises of different sizes to increase competitiveness, reduce operational costs, and meet climate targets. The findings may also support the design of public policies aimed at promoting sustainable transport, digitalization, and decarbonization across the economy. Value / originality. The study contributes to the advancement of green logistics and the concept of sustainable digital supply chains by integrating intelligent optimization algorithms into transport management. It presents a conceptual and methodological framework for AI-based optimization of transport operations, which bridges economic efficiency and environmental sustainability. Future research should focus on developing industry standards for AI integration in logistics systems, creating hybrid optimization algorithms, and exploring the socio-economic impacts of digital transformation in the transport sector.