Abstract Food supply chains are nowadays perturbed by an increased supply and demand uncertainty, and more and more suffering from unexpected disruptions. In the specific context of food supply chains (FSC) for perishable products, these could be linked to natural hazards, industrial accidents or epidemics and their impact could lead to huge economic losses. The case of epidemic events has been little studied in the existing literature, although there are numerous cases reported in practice. At the strategic level, this requires a novel risk modeling approach to tackle the correlation and propagation features and advanced stochastic multi-period models to design the FSC network. Our interest in this research is to propose a comprehensive two-stage scenario-based mathematical model to design a resilient food supply chain under demand uncertainty and epidemic disruptions. In order to adequately characterize epidemic disruptions, they are modeled as a compound stochastic process and a Monte Carlo procedure is developed to generate plausible scenarios. The modeling approach covers the special characteristics of FSC, such as products perishability in time and discount prices based on product's age. In addition, a number of resiliency strategies are incorporated into the core model to enhance the resilience level of the FSC network design. The developed models are solved through an efficient solution approach relying on scenario reduction technique and Benders decomposition. Numerous problem instances are used to validate the modeling approach and to derive managerial insights.

中文翻译：

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易受流行病干扰的弹性食品供应链网络的设计

摘要 如今，食品供应链因供需不确定性增加而受到干扰，并且越来越多地遭受意外中断。在易腐烂产品的食品供应链 (FSC) 的特定背景下，这些可能与自然灾害、工业事故或流行病有关，其影响可能导致巨大的经济损失。尽管在实践中报告了许多案例，但现有文献中对流行事件的案例研究很少。在战略层面，这需要一种新颖的风险建模方法来解决相关性和传播特征，并需要先进的随机多周期模型来设计 FSC 网络。我们对这项研究的兴趣是提出一个基于两阶段情景的综合数学模型，以在需求不确定性和流行病中断的情况下设计一个有弹性的食品供应链。为了充分描述流行病中断的特征，它们被建模为复合随机过程，并开发了蒙特卡罗程序来生成合理的场景。建模方法涵盖了 FSC 的特殊特征，例如产品的及时腐烂性和基于产品年龄的折扣价格。此外，核心模型中加入了多项弹性策略，以提升FSC网络设计的弹性水平。开发的模型通过依赖场景缩减技术和 Benders 分解的有效解决方法来解决。