ML-driven agent-based simulation of agri-food supply chain
Keywords:
digital technologies, agent-based modeling, transformer-based neural operator, deep time- index models, forecasting, machine learning, software and analytical complex, supply chains, food securityAbstract
The paper presents the joint use of machine learning methods and agent-based modeling for analysis and scenario forecasts to reduce the impact of trade flow destabilization on food security in Russia in increasing sanctions pressure. The authors propose a conceptual scheme of a software and analytical complex for forecasting indicators of agri-food supply chains. The obtained results can form the basis of a socio-economic multi-agent model for ensuring food security. Using the proposed approach in the situation centers can help counter external threats and ensure Russia's national security.
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Copyright (c) 2024 Юлия Сергеевна Отмахова, Дмитрий Алексеевич Девяткин
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