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Increasing supply chain resilience through efficient redundancy allocation: a risk-averse mathematical model

机译：通过高效的冗余分配增加供应链弹性：风险厌恶的数学模型

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摘要

The COVID-19 pandemic has created significant uncertainty in all areas of life, including supply chains (SCs). This paper presents a new risk-averse mixed-integer nonlinear problem mathematical model for the design and planning of a two-echelon resilient SC network. Disruption events, which can partially or completely reduce the available capacity, are included in the model. The model’s objective is to minimise the total costs by determining the optimal facility location and capacity, allocation flows and resilience actions for hedging against disruption risk. A solution procedure is tested through computational experiments, and managerial insights were formed based on a numerical example for several disruption configurations, with a specific case of long-term crises similar to the COVID-19 pandemic. The results showed that recovery activities are the most efficient actions to take for a short-term disruption event. Besides, proactive resilience investment in a protection system and flexibility enhancement allows the SC to handle the disruption period with a limited increase in network building costs and overcapacity.
机译：Covid-19大流行在所有生命领域都产生了重大的不确定性，包括供应链（SCS）。本文提出了一种新的风险厌恶混合整数非线性问题，用于双梯队弹性SC网络的设计和规划的设计和规划。可以在模型中包含部分或完全降低可用容量的中断事件。该模型的目的是通过确定对冲破坏风险的最佳设施位置和容量，分配流动和恢复动作来最大限度地减少总成本。通过计算实验测试解决方法，基于几个破坏配置的数值示例形成的管理见解，具有与Covid-19大流行类似的长期危机的具体情况。结果表明，恢复活动是短期中断事件采取最有效的行动。此外，在保护系统和灵活性增强的主动弹性投资允许SC处理中断期间，网络建筑成本和产能过剩增加。

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