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

Aharon Ben-Tal, Byung Do Chung, Supreet Reddy Mandala, Tao Yao

Research output: Contribution to journalArticlepeer-review

181 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1177-1189
Number of pages13
JournalTransportation Research Part B: Methodological
Volume45
Issue number8
DOIs
Publication statusPublished - 2011 Sep

Bibliographical note

Funding Information:
This work was partially supported by the grant awards CMMI-0824640 and CMMI-0900040 from the National Science Foundation and the Marcus – Technion/PSU Partnership Program.

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation

Fingerprint Dive into the research topics of 'Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains'. Together they form a unique fingerprint.

Cite this