A novel robust optimization approach for supply chain resilience: The role of flexibility and collaboration

Abstract

Supply chains are increasingly vulnerable to disruptive events that impair performance and stability. Despite research on supply chain resilience, a significant gap remains in risk management approaches on how to simultaneously minimize expected costs and control extreme cost variations. Existing methods neglect to narrow the gap between worst-case and best-case outcomes and to quantify the economic benefit of enhanced information in decision-making under uncertainty. This gap underscores the need for an integrated robust optimization framework that balances expected cost and risk while incorporating the assessment of novel digital technologies. In response, this study proposes a two-stage stochastic mixed-integer nonlinear programming (MINLP) model that introduces two new metrics: Evolutionary Modified Conditional Value at Risk (EMCVaR) and the Information Impact Metric (IIM). EMCVaR unifies tail risk, solution variance, and model infeasibility into a single measure, yielding a controllable and predictable cost range, while IIM quantifies the economic benefit derived from the data-driven decision support system. Moreover, our model incorporates digital technologies, such as advanced screening, predictive analytics, and real-time digital monitoring, to enhance supply chain flexibility and collaboration. Our computational analysis demonstrates that diversified resilience strategies reduce both expected and worst-case costs, decrease cost variability by up to 15 %, and narrow the gap between extreme outcomes by over 30 %. From a managerial perspective, the study recommends adopting EMCVaR as a risk-budget to bound cost volatility, employing IIM to prioritize digital-technology investments that accelerate recovery, and formalizing backup-capacity and spot-market clauses with key suppliers, an integrated strategy shown to reduce worst-case disruption costs by more than 30 % while limiting routine cost variability to roughly 7 %.