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Title: Designing a Disaster Recovery Strategy.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the National Laboratories Information Technology Summit held May 10-13, 2008 in Albuquerque, NM.
Country of Publication:
United States

Citation Formats

Bragg, Donald J.,. Designing a Disaster Recovery Strategy.. United States: N. p., 2007. Web.
Bragg, Donald J.,. Designing a Disaster Recovery Strategy.. United States.
Bragg, Donald J.,. Tue . "Designing a Disaster Recovery Strategy.". United States. doi:.
title = {Designing a Disaster Recovery Strategy.},
author = {Bragg, Donald J.,},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue May 01 00:00:00 EDT 2007},
month = {Tue May 01 00:00:00 EDT 2007}

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  • The transportation network of a modern city is an essential lifeline system. Severe economic and social impacts would arise if part of this network becomes unusable after a natural or human-made disaster. This research identifies the major traffic congestion problems that would occur in a post-disaster situation and generate appropriate traffic control response strategies. Two major problems that arise on a post-disaster (broken) transportation network are Seattle's and Braess' Paradoxes, and Redundant Traffic (users who cannot be served because of link capacity problems). Computer simulation and knowledge-based expert system techniques are developed and applied to these problems to determine appropriatemore » post-disaster traffic control strategies. A post-disaster trip simulation model called MOVER, and expert system shell called RUNNER, and a traffic control expert system for the test city called HERCULES are developed and used to solve this problem. MOVER is a new type of traffic assignment model developed exclusively for simulating travel behavior on a damaged transportation network. MOVER also computes measures of the effectiveness of alternative recovery strategies. Four response principles are formulated based on the results obtained from using MOVER, and they are then used to develop a knowledge-based expert system. Five different network damage scenarios are simulated for the test city network (150 links, 40 nodes).« less
  • No abstract prepared.
  • No abstract prepared.
  • This paper considers the single commodity allocation problem (SCAP) for disaster recovery, a fundamental problem faced by all populated areas. SCAPs are complex stochastic optimization problems that combine resource allocation, warehouse routing, and parallel fleet routing. Moreover, these problems must be solved under tight runtime constraints to be practical in real-world disaster situations. This paper formalizes the specification of SCAPs and introduces a novel multi-stage hybrid-optimization algorithm that utilizes the strengths of mixed integer programming, constraint programming, and large neighborhood search. The algorithm was validated on hurricane disaster scenarios generated by Los Alamos National Laboratory using state-of-the-art disaster simulation toolsmore » and is deployed to aid federal organizations in the US.« less
  • This paper studies the Power System Stochastic Storage Problem (PSSSP), a novel application in power restoration which consists of deciding how to store power system components throughout a populated area to maximize the amount of power served after disaster restoration. The paper proposes an exact mixed-integer formulation for the linearized DC power flow model and a general column-generation approach. Both formulations were evaluated experimentally on benchmarks using the electrical power infrastructure of the United States and disaster scenarios generated by state-of-the-art hurricane simulation tools similar to those used by the National Hurricane Center. The results show that the column-generation algorithmmore » produces near-optimal solutions quickly and produces orders of magnitude speedups over the exact formulation for large benchmarks. Moreover, both the exact and the column-generation formulations produce significant improvements over greedy approach and hence should yield significant benefits in practice.« less