Natural and man-made disasters, such as earthquakes, floods, plane crashes, high-rise building collapses, or major nuclear facility malfunctions, pose an ever-present challenge to public emergency services. Disasters may result in a large volume of responders arriving on-scene to provide assistance to victims. Coordination of responding resources is a major problem in disasters. The main motivation for the work is that disaster response and recovery efforts require timely interaction and coordination of public emergency services in order to save lives and property.
In the present research effort, we are primarily concerned with assisting the Emergency medical agencies that deal with emergency situations by developing a decision-support system that can help them respond quickly and efficiently to a given situation. The overall goal of this project develop a practical solution for the resource allocation problem which can be integrated with the DIORAMA system that we have developed in our lab. The DIORAMA system collects information like victim’s location and condition in disaster site.
Based on the information collected by the DIORAMA system, we developed an algorithm that can find the nearest resources from the disaster site to mitigate the risk. This problem can be solved in two phases, allocation and dispatching. The Emergency manager will provide the system Priority ratings of the cluster with respect to the emergency response resources and also the demands at each cluster. In the first phase allocation, we determine the number of emergency resources that can be allocated at each cluster which minimizes the overall risk. We define risk as the fraction of the unsatisfied demand. The output of this phase is the optimal resource allocation table. In the second phase, we find the nearest resource warehouse that can cater the demands of the cluster and dispatch the resources accordingly to the disaster site. This is also an integer programming problem. The final output of this phase is the dispatch table from which we can determine from where should the resources has to be sent to the clusters for an efficient and timely response. This is also rendered on Google Maps.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:theses-1499 |
| Date | 01 January 2010 |
| Creators | Kondaveti, Russell |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses 1911 - February 2014 |
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