Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62370
Title: A mean-shift algorithm for large-scale planar maximal covering location problems
Authors: He, Z
Fan, B
Cheng, TCE 
Wang, SY
Tan, CH
Keywords: Location
Large scale optimization
Planar maximal covering location problem
Mean shift
Issue Date: 2016
Publisher: Elsevier
Source: European journal of operational research, 2016, v. 250, no. 1, p. 65-76 How to cite?
Journal: European journal of operational research 
Abstract: The planar maximal covering location problem (PMCLP) concerns the placement of a given number of facilities anywhere on a plane to maximize coverage. Solving PMCLP requires identifying a candidate locations set (CLS) on the plane before reducing it to the relatively simple maximal covering location problem (MCLP). The techniques for identifying the CLS have been mostly dominated by the well-known circle intersect points set (CIPS) method. In this paper we first review PMCLP, and then discuss the advantages and weaknesses of the CIPS approach. We then present a mean-shift based algorithm for treating large-scale PMCLPs, i.e., MSMC. We test the performance of MSMC against the CIPS approach on randomly generated data sets that vary in size and distribution pattern. The experimental results illustrate MSMC's outstanding performance in tackling large-scale PMCLPs.
URI: http://hdl.handle.net/10397/62370
ISSN: 0377-2217
EISSN: 1872-6860
DOI: 10.1016/j.ejor.2015.09.006
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