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INDUSTRIAL ENGINEER – VOLUME 47: NUMBER 03
Natural disasters lead to increasingly higher death tolls and severe material losses. When a disaster occurs, delivering relief supplies such as food, water and shelter to people in the affected areas as early as possible is of critical importance for effective relief operations. The way is by positioning the resources strategically in advance based on the most likely disaster scenarios. Considering the complex structure of humanitarian relief systems and the need to allocate scarce resources in a way that improves the effectiveness of the relief operations, humanitarian logistics could benefit greatly from industrial engineering methods.
There is significant uncertainty in factors such as location and severity of disaster, demand quantities and transportation infrastructure that affects how effective a response is. It is crucial to develop models incorporating the inherent uncertainty to make sound decisions. Such a stochastic pre-disaster relief network design problem is addressed via a new risk-averse stochastic programming approach in “Stochastic Network Design for Disaster Preparedness.” In this paper, the authors determine the size and location of the response facilities and inventory levels of relief supplies at each facility while guaranteeing a certain level of network reliability.
Their optimization models feature a chance constraint to ensure that demand for relief supplies across the network is satisfied with a high probability. The authors develop a computationally efficient solution algorithm using methods from network flows, Boolean programming and integer programming. Computational results for the case study on the Southeastern U.S. region facing hurricane risk demonstrate the effectiveness of the proposed methods and underline the importance of long-term pre-disaster planning.
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