Dynamic relief-demand management for emergency logistics operations under large-scale disasters
AbstractThis 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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Bibliographic InfoArticle provided by Elsevier in its journal Transportation Research Part E: Logistics and Transportation Review.
Volume (Year): 46 (2010)
Issue (Month): 1 (January)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description
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- Falasca, Mauro & Zobel, Christopher, 2012. "An optimization model for volunteer assignments in humanitarian organizations," Socio-Economic Planning Sciences, Elsevier, vol. 46(4), pages 250-260.
- Berkoune, Djamel & Renaud, Jacques & Rekik, Monia & Ruiz, Angel, 2012. "Transportation in disaster response operations," Socio-Economic Planning Sciences, Elsevier, vol. 46(1), pages 23-32.
- Lin, Yen-Hung & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2012. "Location of temporary depots to facilitate relief operations after an earthquake," Socio-Economic Planning Sciences, Elsevier, vol. 46(2), pages 112-123.
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