Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation

Condeixa, L, Leiras, A, Pinheiro De Oliveira, F and de Brito Jr, I 2017, 'Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation', International Journal of Disaster Risk Reduction, vol. 25, pp. 238-247.


Document type: Journal Article
Collection: Journal Articles

Title Disaster relief supply pre-positioning optimization: A risk analysis via shortage mitigation
Author(s) Condeixa, L
Leiras, A
Pinheiro De Oliveira, F
de Brito Jr, I
Year 2017
Journal name International Journal of Disaster Risk Reduction
Volume number 25
Start page 238
End page 247
Total pages 10
Publisher Elsevier
Abstract Problems related to disaster management are strongly influenced by random outcomes, which indicates the importance of using a mathematical tool that can coherently take into account the stochasticity associated with humanitarian logistics problems. However, stochastic models can lead to decisions that consider solely expected values and thus neglect the eventual damage associated with the worst-case scenarios of a disaster. Two-stage stochastic optimization can lead to solutions that present relief supply shortages that could be often prevented. Bearing this in mind, models with risk aversion have been proposed to make decisions that are better suited to humanitarian operations. In this paper, we propose a model for pre-positioning, location and distribution that uses the measure of Conditional Value at Risk (CVaR) to better attend to affected people in a disaster. A post-optimization analysis is conducted to evaluate the quality of the solution. The results indicate that a risk-aversion profile can lead to a 100% reduction of shortages that can be avoided.
Subject Environmental Science and Management not elsewhere classified
Public Health and Health Services not elsewhere classified
Human Geography not elsewhere classified
Keyword(s) Conditional Value at Risk
Humanitarian logistics
Risk analysis
Stochastic programming
DOI - identifier 10.1016/j.ijdrr.2017.09.007
Copyright notice © 2017 Elsevier Ltd
ISSN 2212-4209
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