Dynamic Relief-Demand Management for Emergency Logistics Operations Under Large-Scale Disasters

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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  • English

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  • Accession Number: 01144734
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Nov 5 2009 9:39AM