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An Approximate Dynamic Programming Approach to Food Security of Communities Following Hazards

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dc.contributor.authorNozhati, Saeed-
dc.contributor.authorSarkale, Yugandhar-
dc.contributor.authorEllingwood, Bruce R.-
dc.contributor.authorChong, Edwin K.P.-
dc.contributor.authorMahmoud, Hussam-
dc.date.accessioned2019-06-24T23:45:47Z-
dc.date.available2019-06-24T23:45:47Z-
dc.date.issued2019-05-26-
dc.identifier.citation13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019ko_KR
dc.identifier.isbn979-11-967125-0-1-
dc.identifier.otherICASP13-096-
dc.identifier.urihttp://hdl.handle.net/10371/153966-
dc.description.abstractFood security can be threatened by extreme natural hazard events for households of all social classes within a community. To address food security issues following a natural disaster, the recovery of several elements of the built environment within a community, including its building portfolio, must be considered. Building portfolio restoration is one of the most challenging elements of recovery owing to the complexity and dimensionality of the problem. This study introduces a stochastic scheduling algorithm for the identification of optimal building portfolio recovery strategies. The proposed approach provides a computationally tractable formulation to manage multi-state, large-scale infrastructure systems. A testbed community modeled after Gilroy, California, is used to illustrate how the proposed approach can be implemented efficiently and accurately to find the near-optimal decisions related to building recovery following a severe earthquake.ko_KR
dc.language.isoenko_KR
dc.titleAn Approximate Dynamic Programming Approach to Food Security of Communities Following Hazardsko_KR
dc.typeConference Paperko_KR
dc.identifier.doi10.22725/ICASP13.096-
dc.citation.pages461-466-
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Civil & Environmental Engineering (건설환경공학부)ICASP13
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