Stochastic optimization of medical supply location and distribution in disaster management

We propose a stochastic optimization approach for the storage and distribution problem of medical supplies to be used for disaster management under a wide variety of possible disaster types and magnitudes. In preparation for disasters, we develop a stochastic programming model to select the storage...

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Bibliographic Details
Published in:International journal of production economics 2010-07, Vol.126 (1), p.76-84
Main Authors: Mete, Huseyin Onur, Zabinsky, Zelda B.
Format: Article
Language:English
Subjects:
Disaster management
Disaster management Stochastic programming Medical supply storage
Disasters
Distribution planning
Emergency preparedness
Inventory planning
Location analysis
location and distribution Inventory planning Vehicle routing
Mathematical programming
Medical
Medical supplies
Medical supply storage, location and distribution
Optimization
Optimization techniques
Position (location)
Programming
Risk
Stochastic models
Stochastic programming
Stochasticity
Studies
Vehicle routing
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Summary:We propose a stochastic optimization approach for the storage and distribution problem of medical supplies to be used for disaster management under a wide variety of possible disaster types and magnitudes. In preparation for disasters, we develop a stochastic programming model to select the storage locations of medical supplies and required inventory levels for each type of medical supply. Our model captures the disaster specific information and possible effects of disasters through the use of disaster scenarios. Thus, we balance the preparedness and risk despite the uncertainties of disaster events. A benefit of this approach is that the subproblem can be used to suggest loading and routing of vehicles to transport medical supplies for disaster response, given the evaluation of up-to-date disaster field information. We present a case study of our stochastic optimization approach for disaster planning for earthquake scenarios in the Seattle area. Our modeling approach can aid interdisciplinary agencies to both prepare and respond to disasters by considering the risk in an efficient manner.
ISSN:0925-5273
1873-7579
DOI:10.1016/j.ijpe.2009.10.004