Abstract
A multi-portfolio approach and a scenario-based stochastic mixed integer program are developed for risk-averse selection of resilient supply and demand portfolios in a geographically dispersed multi-tier supply chain network under disruption risks. The resilience of the supply chain is improved by selection of primary supply portfolio and by pre-positioning of risk mitigation inventory of parts at different tiers that will hedge against all disruption scenarios. Simultaneously for each disruption scenario, recovery and transshipment portfolios are determined and decisions on usage the pre-positioned inventory are made to minimize conditional cost-at-risk or maximize conditional service-at-risk. Some properties of optimal solutions, derived from the proposed model provide additional managerial insights. In particular, the impact of unit penalty for unfulfilled demand for products on resilience of the risk-averse supply portfolio is investigated. The findings also indicate that the developed multi-portfolio approach forms an embedded network flow structure that leads to computationally efficient stochastic mixed integer program with a very strong LP relaxation.
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