Highlights Joint optimisation of supply chain capacities, repair capabilities, supply chain operations and repair resource logistics. Scenario tree generation method considering the risk of failure propagation. Multi-stage stochastic programming model for initial network capacities and dynamic network flows. Case study quantifying the costs and benefits of pooling repair resource procurement.
Abstract The joint optimisation of investments in capacity and repair capability of production and logistics systems at risk of being damaged is an important aspect of supply chain resilience that is not sufficiently addressed by state-of-the-art modelling approaches. Furthermore, logistical issues of procuring repair resources impact speed of recovery but are not considered in most existing models. This paper presents a novel multi-stage stochastic programming model that optimizes pre-disruption investment decisions, as well as post-disruption dynamic adjustment of supply chain operations and allocation of repair resources. A case study demonstrates how the method can quantify the effects of pooling repair resources.
Optimal supply chain resilience with consideration of failure propagation and repair logistics
2019-12-20
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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