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Determining the number of facilities for large-scale emergency

Authors:

Qidi WuAuthors Info & Claims

International Journal of Computing Science and Mathematics, Volume 4, Issue 3

Pages 242 - 251

https://doi.org/10.1504/IJCSM.2013.057253

Published: 01 October 2013 Publication History

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.

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Cited By

Alghanmi NAlotaibi RAlshammari SMahmood A(2023)Population affinity propagation approach for points of dispensing location allocationApplied Intelligence10.1007/s10489-023-04809-953:22(27392-27412)Online publication date: 7-Sep-2023
https://dl.acm.org/doi/10.1007/s10489-023-04809-9
Meng LKang QHan CZhou M(2018)Determining the Optimal Location of Terror Response Facilities Under the Risk of DisruptionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2017.269796519:2(476-486)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1109/TITS.2017.2697965
Zeng XLi GZou HChen Q(2015)An empirical comparison of incremental linear feature extraction methodsInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2015.0693878:3(249-255)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1504/IJWMC.2015.069387
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Published In

cover image International Journal of Computing Science and Mathematics

International Journal of Computing Science and Mathematics Volume 4, Issue 3

October 2013

111 pages

ISSN:1752-5055

EISSN:1752-5063

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Inderscience Publishers

Geneva 15, Switzerland

Publication History

Published: 01 October 2013

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Cited By

Alghanmi NAlotaibi RAlshammari SMahmood A(2023)Population affinity propagation approach for points of dispensing location allocationApplied Intelligence10.1007/s10489-023-04809-953:22(27392-27412)Online publication date: 7-Sep-2023
https://dl.acm.org/doi/10.1007/s10489-023-04809-9
Meng LKang QHan CZhou M(2018)Determining the Optimal Location of Terror Response Facilities Under the Risk of DisruptionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2017.269796519:2(476-486)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1109/TITS.2017.2697965
Zeng XLi GZou HChen Q(2015)An empirical comparison of incremental linear feature extraction methodsInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2015.0693878:3(249-255)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1504/IJWMC.2015.069387
He RMa CLi Y(2015)Site selection of public parking lot in the new districtInternational Journal of Wireless and Mobile Computing10.1504/IJWMC.2015.0686258:2(147-152)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1504/IJWMC.2015.068625
Changat MLekha DSubhamathi A(2015)Algorithms for the remoteness function, and the median and antimedian sets in ℓ1-graphsInternational Journal of Computing Science and Mathematics10.1504/IJCSM.2015.0729626:5(480-491)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1504/IJCSM.2015.072962
Liu DChen JChai RWang W(2014)Evolutionary analysis of important public health event-based multi-agent simulation modelInternational Journal of Innovative Computing and Applications10.1504/IJICA.2014.0642186:1(33-43)Online publication date: 1-Aug-2014
https://dl.acm.org/doi/10.1504/IJICA.2014.064218

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