Increasing supply chain resilience through efficient redundancy allocation: a risk-averse mathematical model

The COVID-19 pandemic has created significant uncertainty in all areas of life, including supply chains (SCs). This paper presents a new risk-averse mixed-integer nonlinear problem mathematical model for the design and planning of a two-echelon resilient SC network. Disruption events, which can partially or completely reduce the available capacity, are included in the model. The model’s objective is to minimise the total costs by determining the optimal facility location and capacity, allocation flows and resilience actions for hedging against disruption risk. A solution procedure is tested through computational experiments, and managerial insights were formed based on a numerical example for several disruption configurations, with a specific case of long-term crises similar to the COVID-19 pandemic. The results showed that recovery activities are the most efficient actions to take for a short-term disruption event. Besides, proactive resilience investment in a protection system and flexibility enhancement allows the SC to handle the disruption period with a limited increase in network building costs and overcapacity.