(625d) A Novel Framework for Supply Chain Optimization Under Major Disruptions

Oluwadare Badejo^a, Marianthi Ierapetritou

(a) Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St, Newark, DE 19716, United States.

Supply chain networks have become more prominent, complex, and challenging to manage, especially considering the multitude of risks and uncertainty that may manifest^1–4. Generally, the uncertainties are either operational or disruptive. Operational uncertainties - such as demands, prices, raw material availabilities and delay times due to transportations issues are well addressed in the literature^5,6. Disruption uncertainties which results from man-made and natural disasters, pandemic, or strikes have been at the forefront of research. Studies have shown two basic approaches to hedge against the negative impact of different disruptions: proactive and reactive. While the former methods suggest different approaches to generating robust and resilient structures, the latter approach ensures that the supply chain recovers from inherent disruptions⁷. A general shortcoming of existing work is that the supply chain dynamics are not considered i.e., the disruptions are considered static events without considering durations and recovery policies^7,8.

In this work, we develop a supply chain model that aids decision-making addressing disruptions by considering both proactive and reactive strategies. In solving the supply chain problem, the decision dynamics are considered by using a time expanded problem and adopting the rolling horizon framework. In the proposed supply chain model, the supply chain is represented as a graph network, where the nodes consisting of suppliers, manufacturing sites, warehouses, and retailers interact using the arcs. The arcs determine the flow of material between nodes. Independent disruptions can occur at the nodes and/or arcs and the time of disruption is quantified using geometric probability distribution. In the event of disruption, we have adopted strategies such as adjusting routing plans with multimodal transportation option, inventory levels, facility capacity flexibility, and using customer location as a warehouse in case of main warehouse is disruption.

To illustrate the decision making strategies resulted from the proposed approach as well as the model computational efficiency, we have utilized two case studies. The first and smaller case study was used to illustrate the effect of arcs and node disruptions in the decision making while the second case study is presented to demonstrate the computational requirements of the proposed framework. The results suggested that the effect of nodes disruption is more predominant. This is because the flexibility at the nodes is limited by the initial network configuration which makes adopted strategies less robust. Furthermore, on the larger case study, the supply chain model scales well and is solved in reasonable time. The solution balances the tradeoff between customer service level and total cost of the entire supply chain network.

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