  Supply chain resilience analysis aims to identify the critical elements in
the supply chain, measure its reliability, and analyze solutions for improving
vulnerabilities. While extensive methods like stochastic approaches have been
dominant, robust optimization-widely applied in robust planning under
uncertainties without specific probability distributions-remains relatively
underexplored for this research problem. This paper employs robust optimization
with budget-of-uncertainty as a tool to analyze the resilience of multi-modal
logistics service networks under time uncertainty. We examine the interactive
effects of three critical factors: network size, disruption scale, disruption
degree. The computational experiments offer valuable managerial insights for
practitioners and researchers.
