Resilience Analysis of Multi-modal Logistics Service Network Through Robust Optimization with Budget-of-Uncertainty

TL;DR

This paper uses robust optimization with a budget-of-uncertainty approach to analyze the resilience of multi-modal logistics networks under time uncertainties, considering factors like network size and disruption severity.

Contribution

It introduces a novel application of robust optimization to supply chain resilience analysis, focusing on multi-modal logistics networks under uncertainty.

Findings

Robust optimization effectively identifies critical vulnerabilities.

Network size and disruption scale significantly impact resilience.

Managerial insights for improving supply chain robustness.

Abstract

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.