Designing a Russian–Chinese Omnichannel Logistics Network for the Supply of Bioethanol

This research considers an Artificial Intelligence (AI)-driven omnichannel logistics network for bioethanol supply from Russia to China. As a renewable, low-carbon transport fuel, bioethanol plays a critical role in energy diversification and decarbonization strategies for both Russia and China. However, its flammability and temperature sensitivity impose stringent requirements on transport infrastructure and supply chain management, making it a typical application scenario for exploring intelligent logistics models. The proposed model integrates information, transportation, and financial flows into a unified simulation framework designed to support flexible and sustainable cross-border (CB) logistics. Using a combination of machine learning, multi-objective evaluation, and reinforcement learning (RL), the system models and ranks alternative transportation routes under varying operational conditions. Results indicate that the mixed corridor through Kazakhstan and Kyrgyzstan achieves the best overall balance of cost, time, emissions, and customs reliability, outperforming single-country routes. The findings highlight the potential of AI-enhanced logistics systems in supporting low-carbon energy trade and CB infrastructure coordination.