Food supply chain network design under uncertainty and pandemic disruption

Today, supply chains are affected by various uncertainties and disruptions. Among the unexpected disruptions, the widespread of COVID-19 pandemic has adversely influenced supply chains (SC) worldwide and is a reminder of the importance of resilience in supply chain networks. In this research, the goal is to present a mathematical programming model for designing a resilient food supply chain that can withstand disruptions caused by pandemics and their ripple effects. A hybrid robust-stochastic optimization approach is proposed to handle random as well as deep uncertainties, and three resilience strategies are applied to make the model resilient. Several numerical examples are generated to validate the presented model and derive practical insights. The method consistently demonstrated a reduced optimality gap, showing an average improvement of 37 \% compared to the nominal approach. Resilient strategies, particularly outsourcing, consistently resulted in a substantial average cost reduction of 25 \% . The combined use of all three strategies indicated a remarkable average cost reduction of up to 52 \% .