Evaluating supply chain resilience during pandemic using agent-based simulation

Teddy Lazebnik

Physica A: Statistical Mechanics and its Applications, 2025, vol. 674, issue C

Abstract: Recent pandemics have highlighted vulnerabilities in our global economic systems, especially supply chains. A possible future pandemic raises a dilemma for business owners between short-term profitability and long-term supply chain resilience preparedness. In this study, we propose a novel agent-based simulation model that integrates epidemiological dynamics using a Susceptible–Exposed–Infected–Recovered–Dead (SEIRD) model, a supply and demand economic model, and a spatial representation of supply chain networks to evaluate supply chain resilience preparedness strategies during pandemics. Using this model, we explore a range of supply chain resilience preparedness strategies under pandemic scenarios using in silico experiments. Our analysis shows that the exact supply chain resilience preparedness strategy is hard to obtain for each firm and is relatively sensitive to the exact profile of the pandemic and economic state at the beginning of the pandemic. As such, we used a machine learning model that uses agent-based simulation to estimate a near-optimal supply chain resilience preparedness strategy for a firm.

Keywords: Pandemic spread; Supply chain; Supply-and-demand model; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:674:y:2025:i:c:s0378437125004327

DOI: 10.1016/j.physa.2025.130780

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