AI-Driven Computational Simulation for Enhancing Resilience in Agri-Fresh Food Supply Chains

Agri-Fresh Food Supply Chains (AFFSCs) are increasingly vulnerable to disruptions caused by climate events, transportation issues, and regulatory shifts. These disruptions challenge the operational robustness and food security of global and local food systems. This study explores the potential of AI-driven computational simulation to enhance resilience within AFFSCs by integrating machine learning with agent-based and discrete-event simulation techniques. The proposed methodology evaluates various network configurations under multiple disruption scenarios to assess key resilience performance indicators. By simulating the effects of different structural and operational strategies, the model provides insights into the adaptability and recovery potential of supply chains. Preliminary results indicate that intelligent network design and digital decision-making tools can significantly mitigate disruption impacts, improving logistics responsiveness and system reliability. The contribution of this research lies in its integration of AI and simulation for supply chain resilience analysis, offering valuable recommendations for both policymakers and practitioners. This work intersects the fields of Artificial Intelligence and Data Science, Business Management and Operations Management, Simulation, Optimization and Productivity Improvement, and Supply Chain and Logistics.