Robustifying resilient supply chain against ambiguous facility disruptions under new risk-averse criteria

Supply chain (SC) disruptions often result in significant economic losses and eroded consumer trust. This paper investigates the design of resilient SCs that effectively respond to facility disruptions while considering customer loyalty. To address disruption impacts, we deploy multiple resilience strategies for network reconstruction during disruption events. Given the unpredictability of facility disruptions, we construct a novel moment-based ambiguity set via statistical methods to quantify capacity failure fractions. We develop a two-stage adaptive distributionally robust optimization (ADRO) model that minimizes SC costs under this ambiguity set. While traditional risk measures like Conditional Value at Risk (CVaR) focuses on extreme tail risks, we extend our risk-neutral model to a risk-averse formulation using the mean absolute deviation from the median (MADM) criterion, which provides a new perspective by minimizing cost variability around the median. In terms of solution method, tailored Benders decomposition (BD) algorithms with multi-subproblem are designed for our ADRO model reformulations. The effectiveness of our ADRO methods and BD algorithm is demonstrated via a practical case study. The results indicate that our model can accommodate the risk preferences of decision-makers while simultaneously offering resilient and economic SC design schemes for decision-makers.