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

Jiuh-Biing Sheu

Transportation Research Part E: Logistics and Transportation Review, 2010, vol. 46, issue 1, 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.

Keywords: Emergency; logistics; operations; Relief-demand; management; Multi-source; data; fusion; Fuzzy; clustering; Entropy; TOPSIS (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (83)

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