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Dynamic relief-demand management for emergency logistics operations under large-scale disasters

  • Sheu, Jiuh-Biing
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    This paper presents a dynamic relief-demand management model for emergency logistics operations under imperfect information conditions in large-scale natural disasters. The proposed methodology consists of three steps: (1) data fusion to forecast relief demand in multiple areas, (2) fuzzy clustering to classify affected area into groups, and (3) multi-criteria decision making to rank the order of priority of groups. The results of tests accounting for different experimental scenarios indicate that the overall forecast errors are lower than 10% inferring the proposed method's capability of dynamic relief-demand forecasting and allocation with imperfect information to facilitate emergency logistics operations.

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    File URL: http://www.sciencedirect.com/science/article/pii/S1366554509001161
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    Article provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.

    Volume (Year): 46 (2010)
    Issue (Month): 1 (January)
    Pages: 1-17

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    Handle: RePEc:eee:transe:v:46:y:2010:i:1:p:1-17
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