Selection of Supply and Demand Portfolios and Production and Inventory Scheduling

Abstract

This chapter presents a multi-portfolio approach for the spatial and temporal integrated decision-making in a supply chain under disruption risks. A disruptive event is assumed to impact both primary suppliers of parts and the manufacturer primary assembly plant. Then the manufacturer selects recovery suppliers, recovery plants along with transshipment of parts from disabled primary plant to recovery plants and production and inventory scheduling in recovery plants. The mitigation and recovery decisions are integrated over time and space: the primary portfolios to be implemented before a disruptive event are optimized simultaneously with recovery portfolios for the aftermath period as well as the portfolios for both part suppliers and product manufacturers in different geographic regions are determined simultaneously. Using conditional cost-at-risk and conditional service-at-risk as risk measures, the risk-averse solutions are obtained. The solution results are compared for different demand patterns. The findings indicate that when the objective is to optimize service level with no regard to costs, both supply and demand portfolios are more diversified. The findings also demonstrate that the developed multi-portfolio approach leads to computationally efficient stochastic MIP models with a very strong LP relaxation. The proposed multi-portfolio approach may help to better mitigate the impact of disruption propagation on supply chain performance, i.e., the ripple effect. The major managerial insights are provided at the end of this chapter.