Supplier selection under disruption risks using Stochastic Mixed Linear Programming techniques

Abstract

Supplier selection is an important key of supply chain management and mainly with the presence of disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. Many approaches have been developed in the literature based on various formal modeling techniques. In this paper, Stochastic Mixed Linear Program (MILP) techniques are used for the selection of suppliers under risk disruption. Two set of disruption scenarios are considered: (1) scenario with independent local disruption of each supplier, and (2) scenario with local and global disruption that may result in all suppliers simultaneously. The two percentiles: Value at risk (VaR) and conditional value at risk (CVaR) are used to model the risk of supply chain disruption. It be concluded that these percentiles are capable to optimizing the supply portfolio by minimizing expects worst-case per part via calculating the value at risk of expected cost part. The extension of this study seems very interesting for the risk analysis in complex supply chains.