Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions
Mathematical Programming Approach to Optimize Tactical and Operational Supply Chain Decisions under Disruptions
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DOI：
10.1021/acs.iecr.2c01641
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发表时间：
2022-11-03
影响因子：
4.2
通讯作者：
Ierapetritou, Marianthi
中科院分区：
文献类型：
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作者：
Badejo, Oluwadare;Ierapetritou, Marianthi
供应链（SC）网络变得更加突出，复杂，管理更具挑战性，特别是考虑到可能出现的众多风险和不确定性。研究表明，有两种基本方法来对冲供应链中断的负面影响：主动和被动。虽然前一种方法提出了不同的方法来产生强大的和有弹性的结构，后一种方法确保SC有效地恢复。现有工作的一个普遍缺点是没有考虑供应链动态。因此，中断被认为是静态事件，不包括持续时间和恢复策略。在这项工作中，我们开发了一个供应链模型，帮助决策，考虑主动和被动的战略，在解决中断。我们采用了离散时间扩展模型来解决供应链问题，并考虑使用滚动时域框架的中断动力学。在所提出的供应链模型中，一个图形网络表示的供应链，其中的节点组成的供应商，制造基地，仓库和客户使用弧进行交互。圆弧决定了节点之间的材料流。独立的中断可以发生在节点和/或弧，和中断的时间被量化使用的几何分布。在出现中断时，我们采取了调整路由计划、库存水平、容量灵活性以及其他战术和运营决策来对冲中断。为了说明所提出的方法，我们用一个小问题来说明弧和节点中断的影响，在决策和一个现实的案例研究，以证明所提出的框架的计算复杂性。结果表明，由于初始网络配置限制了节点的灵活性，节点中断的影响更为显著。此外，结果表明，供应链有效地运作，因为该解决方案提供了服务水平和供应链运营总成本之间的平衡。
Supply chain (SC) networks have become more prominent, complex, and challenging to manage, especially considering the multitude of risks and uncertainty that may manifest. Studies have shown two basic approaches to hedge against the negative impact of SC disruptions: proactive and reactive. While the former methods suggest different approaches to generating robust and resilient structures, the latter approach ensures that the SC recovers effectively. A general shortcoming of existing work is not considering SC dynamics. Consequently, disruptions are considered static events without including the durations and recovery policies. In this work, we develop a SC model that aids decision-making in addressing disruptions by considering proactive and reactive strategies. We adopted a discrete time-expanded model to solve the SC problem and consider the disruption dynamics using the rolling horizon framework. In the proposed SC model, a graph network represents the SC, where the nodes consisting of suppliers, manufacturing sites, warehouses, and customers interact using the arcs. The arcs determine the flow of materials between nodes. Independent disruptions can occur at the nodes and/or arcs, and the time of disruption is quantified using the geometric distribution. In the advent of disruption, we have adopted adjusting routing plans, inventory levels, capacity flexibility, and other tactical and operational decisions to hedge against disruption. To illustrate the proposed approach, we used a small problem to illustrate the effect of arcs and node disruption in decision-making and a realistic case study to demonstrate the proposed framework's computational complexity. The results suggested that the effect of node disruption is more predominant because the initial network configuration limits the flexibility at the nodes. Furthermore, it was shown that the SC operated efficiently, as the solution offers a balance between the service level and the total cost of operating the SC.