2026-09-19T00:00:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/900582022-01-31T17:17:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jarrod Goentzel and Erica Gralla.
Moline, Julia N. (Julia Nessa)
Massachusetts Institute of Technology. Technology and Policy Program.
Massachusetts Institute of Technology. Engineering Systems Division
Technology and Policy Program
n-us---
2014-09-19T21:37:14Z
2014-09-19T21:37:14Z
2014
2014
http://hdl.handle.net/1721.1/90058
890140884
Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Engineering Systems Division, Technology and Policy Program, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 100-102).
Resource allocation decisions in post-disaster operations are challenging because of situational dynamics, insufficient information, organizational culture, political context, and urgency. We propose a methodology to create a data-driven decision process for post-disaster resource allocation that enables timely, transparent and consistent decision-making during crisis. Our methodology defines the decisions that must be made, identifies relevant historical, initial, and trending data sources, and develops numerical thresholds, quantitative relationships, and optimization models to support decision making. The general process also offers flexibility to consider non-quantitative factors and spans multiple review periods. We apply this methodology to the Federal Emergency Management Agency's (FEMA) program for establishing and managing Disaster Recovery Centers (DRCs) after a disaster. A detailed case study of one disaster response and relevant historical data provide the basis for DRC decision making thresholds, relationships, and optimization models. We then apply the newly developed process to several recent disaster response scenarios and find that FEMA could have reduced cost by 60-80% while providing sufficient capacity for survivors. Finally, we discuss the generalizability of the methodology to other post-disaster programs along with limitations and potential future work.
by Julia N. Moline.
S.M. in Technology and Policy
123 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Engineering Systems Division.
Technology and Policy Program.
Data-driven resource allocation decisions : FEMA's disaster recovery centers
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>Resource allocation decisions in post-disaster operations are challenging because of situational dynamics, insufficient information, organizational culture, political context, and urgency. We propose a methodology to create a data-driven decision process for post-disaster resource allocation that enables timely, transparent and consistent decision-making during crisis. Our methodology defines the decisions that must be made, identifies relevant historical, initial, and trending data sources, and develops numerical thresholds, quantitative relationships, and optimization models to support decision making. The general process also offers flexibility to consider non-quantitative factors and spans multiple review periods. We apply this methodology to the Federal Emergency Management Agency's (FEMA) program for establishing and managing Disaster Recovery Centers (DRCs) after a disaster. A detailed case study of one disaster response and relevant historical data provide the basis for DRC decision making thresholds, relationships, and optimization models. We then apply the newly developed process to several recent disaster response scenarios and find that FEMA could have reduced cost by 60-80% while providing sufficient capacity for survivors. Finally, we discuss the generalizability of the methodology to other post-disaster programs along with limitations and potential future work.</Abstract>
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