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

Author

Listed:
  • Yaxin Pang

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Shenle Pan

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

  • Eric Ballot

    (CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique)

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.

Suggested Citation

  • Yaxin Pang & Shenle Pan & Eric Ballot, 2024. "Resilience Analysis of Multi-modal Logistics Service Network Through Robust Optimization with Budget-of-Uncertainty," Working Papers hal-04579868, HAL.
    • Handle: RePEc:hal:wpaper:hal-04579868
    Note: View the original document on HAL open archive server: https://hal.science/hal-04579868v1
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