@article{ref1,
title="Dynamic relief-demand management for emergency logistics operations under large-scale disasters",
journal="Transportation research part E: logistics and transportation review",
year="2010",
author="Sheu, Jiuh-Biing",
volume="46",
number="1",
pages="1-17",
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.<p />",
language="en",
issn="1366-5545",
doi="10.1016/j.tre.2009.07.005",
url="http://dx.doi.org/10.1016/j.tre.2009.07.005"
}