Authors: Lingpeng Meng; Qin Ma; Chuanfeng Han; Qidi Wu
Addresses: School of Electronics and Information, Tongji University, No. 4800, Cao'an Road, Shanghai, 201804, China ' Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, University of Georgia, Davison Life Sciences Bldg. A110, 120 E Green St., Athens, GA 30602-7229, USA ' Institute of Urban Construction and Emergency Management, Tongji University, No. 1239, Siping Road, Shanghai, 200092, China ' School of Electronics and Information, Tongji University, No. 4800, Cao'an Road, Shanghai, 201804, China
Abstract: Classical facility location models like the maximum covering location model implicitly assume that the number of facilities is given exogenously. However, the tremendous magnitude and low frequency of large-scale emergencies make it difficult to apply the above assumption simply. This paper developed a greedy clustering method aiming to identify suitable number of large-scale emergency facilities, taking into account of the distinct attributes of each demand area and the required response quality. An illustrative example is given to show the effectiveness and efficiency of the proposed method.
Keywords: facility numbers; greedy clustering; covering models; large-scale emergencies; facility location; emergency facilities; emergency response; disaster response; emergency management.
International Journal of Computing Science and Mathematics, 2013 Vol.4 No.3, pp.242 - 251
Received: 14 May 2013
Accepted: 20 Jun 2013
Published online: 10 May 2014 *