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Stochastic network models for logistics planning in disaster relief

Alem, Douglas; Clark, Alistair; Moreno, Alfredo


Douglas Alem

Alistair Clark

Alfredo Moreno


Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty
about the exact nature and magnitude of the disaster, a lack of reliable information about the location
and needs of victims, possible random supplies and donations, precarious transport links, scarcity of
resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide
how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes
into account practical characteristics that have been neglected by the literature so far, such as budget
allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic
multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of
the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple
two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested.
Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model
can help plan and organise relief to provide good service levels in most scenarios, and how this depends
on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real
and random instances.


Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187-206.

Journal Article Type Article
Acceptance Date Apr 21, 2016
Online Publication Date May 4, 2016
Publication Date Nov 16, 2016
Journal European Journal of Operational Research
Print ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 255
Issue 1
Pages 187-206
Keywords OR in disaster relief, humanitarian logistics, emergency logistics planning, two-stage stochastic programming, risk-aversion
Public URL
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