Citation

Sheu JB. Transp. Res. E Logist. Transp. Rev. 2010; 46(1): 1-17.

Copyright

(Copyright © 2010, Elsevier Publishing)

DOI

10.1016/j.tre.2009.07.005

PMID

unavailable

Abstract

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.