A multi-objective approach for supply chain design considering disruptions impacting supply availability and quality

We develop a multi-objective stochastic programming model to explore tradeoffs between costs and risk in the supply chain in the event a disruption occurs. We explicitly consider network configuration and operating cost under normal conditions, cost of unsatisfied demand, cost of shipping tainted products to a customer, and quality inspection cost as conflicting goals to be minimized simultaneously. We analyze different disruption scenarios to determine the best supplier selection and inspection strategies to mitigate the effect of disruptions on supply availability and quality. Even the single-objective version of this problem is NP-hard; thus, we propose a genetic algorithm-based search method to identify Pareto-optimal supply chain configurations. We use data envelopment analysis for calculating the fitness value of various supply chain configurations. The proposed approach efficiently yields high-quality supply chain designs, allowing the decision maker to determine an appropriate tradeoff between various costs.