We consider optimization problems related to the scheduling of multi-echelon assembly supply chain (MEASC) networks that have applications 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. 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 (ii) and minimizing the time to recover from the disruptive event. Our approaches are applied to a data set based upon an industrial partner’s supply chain to show their applicability as well as their advantages over integer programming models. The developed decision rules were 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.
Citation
Technical Report, Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute
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