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Transportation in disaster response operations

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  • Berkoune, Djamel
  • Renaud, Jacques
  • Rekik, Monia
  • Ruiz, Angel
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    Abstract

    Disasters are extraordinary situations that require significant logistical deployment to transport equipment and humanitarian goods in order to help and provide relief to victims. An efficient response helps to reduce the social, economic and environmental impacts. In this paper, we define and formulate a practical transportation problem often encountered by crisis managers in emergency situations. Since optimal solutions to such a formulation may be achieved only for very small-size instances, we developed an efficient genetic algorithm to deal with realistic situations. This algorithm produces near optimal solutions in relatively short computation times and is fast enough to be used interactively in a decision-support system, providing high-quality transportation plans to emergency managers.

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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: 23-32

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    Handle: RePEc:eee:soceps:v:46:y:2012:i:1:p:23-32

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

    Related research

    Keywords: Transportation; Emergency logistics; Heuristics; Genetic algorithms;

    References

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    1. Balcik, Burcu & Beamon, Benita M. & Krejci, Caroline C. & Muramatsu, Kyle M. & Ramirez, Magaly, 2010. "Coordination in humanitarian relief chains: Practices, challenges and opportunities," International Journal of Production Economics, Elsevier, vol. 126(1), pages 22-34, July.
    2. Yi, Wei & Kumar, Arun, 2007. "Ant colony optimization for disaster relief operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 660-672, November.
    3. Tzeng, Gwo-Hshiung & Cheng, Hsin-Jung & Huang, Tsung Dow, 2007. "Multi-objective optimal planning for designing relief delivery systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(6), pages 673-686, November.
    4. Laporte, Gilbert, 1992. "The vehicle routing problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(3), pages 345-358, June.
    5. 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.
    6. Sheu, Jiuh-Biing, 2010. "Dynamic relief-demand management for emergency logistics operations under large-scale disasters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(1), pages 1-17, January.
    7. 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.
    8. Jotshi, Arun & Gong, Qiang & Batta, Rajan, 2009. "Dispatching and routing of emergency vehicles in disaster mitigation using data fusion," Socio-Economic Planning Sciences, Elsevier, vol. 43(1), pages 1-24, March.
    9. Yi, Wei & Ozdamar, Linet, 2007. "A dynamic logistics coordination model for evacuation and support in disaster response activities," European Journal of Operational Research, Elsevier, vol. 179(3), pages 1177-1193, June.
    10. Laporte, Gilbert, 1992. "The traveling salesman problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(2), pages 231-247, June.
    11. Altay, Nezih & Green III, Walter G., 2006. "OR/MS research in disaster operations management," European Journal of Operational Research, Elsevier, vol. 175(1), pages 475-493, November.
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