Drawing upon economic and environmental sustainability, this study explores how developing the operational resilience of the medical supply chain (MSC) contributes to maintaining healthcare in the face of disruption risks, such as the COVID-19 pandemic. To this end, an optimization-based roadmap is proposed by employing lean tools to achieve and realize MSC resilience. A novel two-stage stochastic optimization model and robust counterpart are developed with the objective of overall cost minimization to cope with the unknowable demand uncertainty represented by scenarios. The reason behind proposing a scenario-based stochastic model is to implement preparedness strategies during the (re)design phase by making strategic and operational level decisions. That being the case, seven cases are generated based on the demand uncertainty intervals along with seven different reliability levels for sensitivity analysis. Computational experiments are conducted through a real case study to compare the centralized and decentralized distribution models in terms of efficiency and responsiveness. The results obtained by the stochastic model and robust counterpart are compared to demonstrate how strong the proposed model is. On top of that, lean tools are used to visualize and analyze the improvement opportunities to contribute to the methodology. By doing so, this paper presents novel theoretical and empirical insights regarding MSC resilience. The computational results emphasize the importance of employing a pre-disruption strategy via the proposed methodology to design a resilient MSC to be prepared for pandemic-related risk. The findings from the sensitivity analysis also verify that regardless of the disruption degree, the developed roadmap with the centralized distribution model leads to up to 40% improvements in terms of the overall cost, order lead time, emission amount, and inventory shortage metrics.

利用经济和环境的可持续性，本研究探讨了发展医疗供应链 (MSC) 的运营弹性如何有助于在面临中断风险（例如 COVID-19 大流行）时维持医疗保健。为此，通过采用精益工具来实现和实现 MSC 弹性，提出了基于优化的路线图。以总体成本最小化为目标，开发了一种新颖的两阶段随机优化模型和鲁棒对应模型，以应对场景所代表的不可知的需求不确定性。提出基于情景的随机模型背后的原因是在（重新）设计阶段通过制定战略和运营层面的决策来实施准备策略。既然如此，根据需求不确定性区间以及用于敏感性分析的七个不同可靠性级别生成七个案例。通过真实案例研究进行计算实验，以比较集中式和分散式分布模型的效率和响应能力。将随机模型和鲁棒对应模型获得的结果进行比较，以证明所提出的模型有多强大。最重要的是，精益工具用于可视化和分析改进机会，为该方法做出贡献。通过这样做，本文提出了关于 MSC 弹性的新颖理论和实证见解。计算结果强调了通过所提出的方法采用预中断策略来设计弹性 MSC 以应对大流行相关风险的重要性。