Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains

Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains

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Article ID: iaor20118647
Volume: 45
Issue: 8
Start Page Number: 1177
End Page Number: 1189
Publication Date: Sep 2011
Journal: Transportation Research Part B
Authors: , , ,
Keywords: simulation: applications, programming: probabilistic
Abstract:

This paper proposes a methodology to generate a robust logistics plan that can mitigate demand uncertainty in humanitarian relief supply chains. More specifically, we apply robust optimization (RO) for dynamically assigning emergency response and evacuation traffic flow problems with time dependent demand uncertainty. This paper studies a Cell Transmission Model (CTM) based system optimum dynamic traffic assignment model. We adopt a min–max criterion and apply an extension of the RO method adjusted to dynamic optimization problems, an affinely adjustable robust counterpart (AARC) approach. Simulation experiments show that the AARC solution provides excellent results when compared to deterministic solution and sampling based stochastic programming solution. General insights of RO and transportation that may have wider applicability in humanitarian relief supply chains are provided.

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