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 journalArticle

156 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 Jan 1

Fingerprint

Supply chains
Logistics
logistics
supply
Planning
planning
uncertainty
traffic
Stochastic programming
demand
programming
Sampling
simulation
experiment
methodology
Experiments
Uncertainty

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Transportation

Cite this

@article{cf9ad4c845ce4469935135126038bd7b,
title = "Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains",
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.",
author = "Aharon Ben-Tal and Chung, {Byung Do} and Mandala, {Supreet Reddy} and Tao Yao",
year = "2011",
month = "1",
day = "1",
doi = "10.1016/j.trb.2010.09.002",
language = "English",
volume = "45",
pages = "1177--1189",
journal = "Transportation Research, Series B: Methodological",
issn = "0191-2615",
publisher = "Elsevier Limited",
number = "8",

}

Robust optimization for emergency logistics planning : Risk mitigation in humanitarian relief supply chains. / Ben-Tal, Aharon; Chung, Byung Do; Mandala, Supreet Reddy; Yao, Tao.

In: Transportation Research Part B: Methodological, Vol. 45, No. 8, 01.01.2011, p. 1177-1189.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Robust optimization for emergency logistics planning

T2 - Risk mitigation in humanitarian relief supply chains

AU - Ben-Tal, Aharon

AU - Chung, Byung Do

AU - Mandala, Supreet Reddy

AU - Yao, Tao

PY - 2011/1/1

Y1 - 2011/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80051945761&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051945761&partnerID=8YFLogxK

U2 - 10.1016/j.trb.2010.09.002

DO - 10.1016/j.trb.2010.09.002

M3 - Article

VL - 45

SP - 1177

EP - 1189

JO - Transportation Research, Series B: Methodological

JF - Transportation Research, Series B: Methodological

SN - 0191-2615

IS - 8

ER -