Epidemic outbreaks pose a significant risk to supply chain and logistics, particularly in the global automotive industry. These risks are characterized by long-term disruptions that can have ripple effects, making them challenging to predict. Contingency plans must be developed to address these risks effectively, with simulation tools being a crucial component. These tools should be used as data integration tools from multiple sources to improve risk analysis quality, and preemptive measures should be taken when possible instead of only reacting to disruptions as they occur. This paper focuses on epidemic breakouts as a distinct category of disruption risk in automotive supply chains (ASCs) and logistics. It formulates a multi-objective nonlinear model to determine the optimal selection strategy for functional supply chains and logistics providers under the impact of epidemic outbreaks. The model uses a realistic nonlinear investment return model for supplier development and order allocation strategy, considering COVID-19′s impact. Nonlinear return models for both single and multiple manufacturers and suppliers are developed. A series of sensitivity tests for various scenarios illustrates the behavior of the model and its decision-making value. Numerical experiments and results show that supplier selection and order allocation strategies are mutually influential. Facility closures and reopenings' timing can have more significant impacts on the ASC performance than upstream disruptions or epidemic propagation speed. Lead-time, epidemic spread speed, and interruption durations are also vital factors to consider in optimizing overall performance and allocating investment funds across multiple suppliers to avoid risks and maximize returns. Decision-makers should consider all relevant factors simultaneously when designing such plans to ensure optimal performance and minimize risk.

疫情的爆发给供应链和物流带来了重大风险，特别是在全球汽车行业。这些风险的特点是长期破坏，可能产生连锁反应，因此难以预测。必须制定应急计划来有效应对这些风险，其中模拟工具是关键组成部分。这些工具应用作来自多个来源的数据集成工具，以提高风险分析质量，并且应在可能的情况下采取先发制人的措施，而不是仅在发生中断时才做出反应。本文重点关注流行病爆发作为汽车供应链（ASC）和物流中断风险的一个独特类别。建立多目标非线性模型，确定疫情影响下功能性供应链和物流提供商的最优选择策略。该模型采用现实的非线性投资回报模型来进行供应商开发和订单分配策略，并考虑了 COVID-19 的影响。为单个和多个制造商和供应商开发了非线性回报模型。针对各种场景的一系列敏感性测试说明了模型的行为及其决策价值。数值实验和结果表明，供应商选择和订单分配策略是相互影响的。设施关闭和重新开放的时间对 ASC 性能的影响比上游中断或流行病传播速度更显着。交货时间、疫情传播速度和中断持续时间也是优化整体绩效和在多个供应商之间分配投资资金以避免风险和最大化回报时需要考虑的重要因素。决策者在设计此类计划时应同时考虑所有相关因素，以确保最佳绩效并最大限度地降低风险。