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Pre-positioning and dynamic delivery planning for short-term response following a natural disaster

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  • Rawls, Carmen G.
  • Turnquist, Mark A.
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    Abstract

    Natural disasters often result in large numbers of evacuees being temporarily housed in schools, churches, and other shelters. The sudden influx of people seeking shelter creates demands for emergency supplies, which must be delivered quickly. A dynamic allocation model is constructed to optimize pre-event planning for meeting short-term demands (over approximately the first 72h) for emergency supplies under uncertainty about what demands will have to be met and where those demands will occur. The model also includes requirements for reliability in the solutions – i.e., the solution must ensure that all demands are met in scenarios comprising at least 100α% of all outcomes. A case study application using shelter locations in North Carolina and a set of hurricane threat scenarios is used to illustrate the model and how it supports an emergency relief strategy.

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    Bibliographic Info

    Article provided by Elsevier in its journal Socio-Economic Planning Sciences.

    Volume (Year): 46 (2012)
    Issue (Month): 1 ()
    Pages: 46-54

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    Handle: RePEc:eee:soceps:v:46:y:2012:i:1:p:46-54

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    Web page: http://www.elsevier.com/locate/seps

    Related research

    Keywords: Pre-positioning strategies; Emergency response; Stochastic programming; Facility location; Resource allocation; Dynamic models;

    References

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    1. Sheu, Jiuh-Biing, 2007. "An emergency logistics distribution approach for quick response to urgent relief demand in disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 687-709, November.
    2. Rawls, Carmen G. & Turnquist, Mark A., 2010. "Pre-positioning of emergency supplies for disaster response," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 521-534, May.
    3. Stepanov, Alexander & Smith, James MacGregor, 2009. "Multi-objective evacuation routing in transportation networks," European Journal of Operational Research, Elsevier, vol. 198(2), pages 435-446, October.
    4. Haghani, Ali & Oh, Sei-Chang, 1996. "Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(3), pages 231-250, May.
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    Cited by:
    1. Wilson, Duncan T. & Hawe, Glenn I. & Coates, Graham & Crouch, Roger S., 2013. "A multi-objective combinatorial model of casualty processing in major incident response," European Journal of Operational Research, Elsevier, vol. 230(3), pages 643-655.
    2. Rennemo, Sigrid Johansen & Rø, Kristina Fougner & Hvattum, Lars Magnus & Tirado, Gregorio, 2014. "A three-stage stochastic facility routing model for disaster response planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 116-135.
    3. Davis, Lauren B. & Samanlioglu, Funda & Qu, Xiuli & Root, Sarah, 2013. "Inventory planning and coordination in disaster relief efforts," International Journal of Production Economics, Elsevier, vol. 141(2), pages 561-573.
    4. Galindo, Gina & Batta, Rajan, 2013. "Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 20-37.

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