Optimizing the recovery of disrupted single-sourced multi-echelon assembly supply chain networks

We consider optimization problems related to the scheduling of Multi-Echelon Assembly Supply Chain (MEASC) networks that find application in the recovery from large-scale disruptive events. Each manufacturer within this network assembles a component from a series of sub-components received from other manufacturers and, due to high qualification standards, each sub-component of the manufacturer is single-sourced. Our motivating industries for this problem are defense aircraft and biopharmaceutical manufacturing. We develop scheduling decision rules that are applied locally at each manufacturer and are proven to optimize two industry-relevant global recovery metrics: (i) minimizing the maximum tardiness of any order of the final product of the MEASC network, and (ii) and minimizing the time to recover from the disruptive event. Our approaches are applied to a data set based on an industrial partner's supply chain to show their applicability as well as their advantages over Integer Programming (IP) models. The developed decision rules are proven to be optimal, faster, and more robust than the equivalent IP formulations. In addition, they provide conditions under which local manufacturer decisions will lead to globally optimal recovery efforts. These decision rules can help managers to make better production and shipping decisions to optimize the recovery after disruptions and quantitatively test the impact of different pre-event mitigation strategies against potential disruptions. They can be further useful in MEASCs with or expecting a large amount of backorders.