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

Sergey Barykin (), Wenye Zhang, Daria Dinets, Andrey Nechesov, Nikolay Didenko, Djamilia Skripnuk, Olga Kalinina, Tatiana Kharlamova, Andrey Kharlamov, Anna Teslya, Gumar Batov and Evgenii Makarenko
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Sergey Barykin: Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Wenye Zhang: Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Daria Dinets: Department of Finance, Accounting, and Auditing, Peoples’ Friendship University of Russia Named After Patrice Lumumba, 117198 Moscow, Russia
Andrey Nechesov: International AI Committee IAIC, Hong Kong, China
Nikolay Didenko: Graduate School of Business Engineering, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Djamilia Skripnuk: Graduate School of Business Engineering, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Olga Kalinina: Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Tatiana Kharlamova: Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Andrey Kharlamov: Department of General Economic Theory and the History of Economic Thought, St. Petersburg State University of Economics, 191023 St. Petersburg, Russia
Anna Teslya: Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia
Gumar Batov: Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 360002 Nalchik, Russia
Evgenii Makarenko: Department of Business Informatics and Management, St. Petersburg State University of Aerospace Instrumentation, 190000 St. Petersburg, Russia

Sustainability, 2025, vol. 17, issue 17, 1-26

Abstract: 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.

Keywords: bioethanol supply chain; CB logistics; omnichannel logistics network; AI-based route optimization; RL; sustainability assessment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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