Accelerated hyperbolic smoothing method for solving the multisource Fermat–Weber and k-Median problems
This article deals with the Multisource Fermat–Weber and continuous k-Median problems. The first problem is the continuous location–allocation problem, defined in a planar region, an important problem in facility location subject. The continuous k-Median problem, defined in a multidimensional space,...
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Veröffentlicht in: | Knowledge-based systems 2020-03, Vol.191, p.105226, Article 105226 |
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description | This article deals with the Multisource Fermat–Weber and continuous k-Median problems. The first problem is the continuous location–allocation problem, defined in a planar region, an important problem in facility location subject. The continuous k-Median problem, defined in a multidimensional space, is also known as the minimum sum-of-distances clustering problem. Their mathematical modellings lead to a min-sum-min formulation which is a global optimization problem with a bi-level nature, nondifferentiable and with many minimizers. To overcome these severe difficulties, the Hyperbolic Smoothing methodology is proposed, in connection with a partition of locations in two groups: location in the frontier and location in gravitational regions, which drastically simplify the computational tasks. For the purpose of illustrating both the reliability and the efficiency of the method, we perform a set of computational experiments making use of the traditional instances described in the literature. Apart from consistently presenting similar or even better results when compared to related approaches, the novel technique was able to deal with instances never tackled before, with up to 1243088 cities. |
doi_str_mv | 10.1016/j.knosys.2019.105226 |
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The first problem is the continuous location–allocation problem, defined in a planar region, an important problem in facility location subject. The continuous k-Median problem, defined in a multidimensional space, is also known as the minimum sum-of-distances clustering problem. Their mathematical modellings lead to a min-sum-min formulation which is a global optimization problem with a bi-level nature, nondifferentiable and with many minimizers. To overcome these severe difficulties, the Hyperbolic Smoothing methodology is proposed, in connection with a partition of locations in two groups: location in the frontier and location in gravitational regions, which drastically simplify the computational tasks. For the purpose of illustrating both the reliability and the efficiency of the method, we perform a set of computational experiments making use of the traditional instances described in the literature. Apart from consistently presenting similar or even better results when compared to related approaches, the novel technique was able to deal with instances never tackled before, with up to 1243088 cities.</description><identifier>ISSN: 0950-7051</identifier><identifier>EISSN: 1872-7409</identifier><identifier>DOI: 10.1016/j.knosys.2019.105226</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Clustering ; Facility location ; Global optimization ; k-Median ; Mathematical models ; Min-sum-distances clustering ; Multisource Fermat–Weber ; Smoothing</subject><ispartof>Knowledge-based systems, 2020-03, Vol.191, p.105226, Article 105226</ispartof><rights>2019 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. 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Apart from consistently presenting similar or even better results when compared to related approaches, the novel technique was able to deal with instances never tackled before, with up to 1243088 cities.</description><subject>Clustering</subject><subject>Facility location</subject><subject>Global optimization</subject><subject>k-Median</subject><subject>Mathematical models</subject><subject>Min-sum-distances clustering</subject><subject>Multisource Fermat–Weber</subject><subject>Smoothing</subject><issn>0950-7051</issn><issn>1872-7409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UMtKw0AUHUTBWv0DFwOuU-9MMkm6EUrxBRU3isthHjcmbZKpM1OhO__BP_RLTIlrVxcO53HPIeSSwYwBy6_Xs03vwj7MOLD5AAnO8yMyYWXBkyKD-TGZwFxAUoBgp-QshDUAcM7KCakXxmCLXkW0tN5v0WvXNoaGzrlYN_077TDWztLKeRpc-3mAYo2027WxCW7nDdI79J2KP1_fb6jRU9Vbukme0Daqp1vvdItdOCcnlWoDXvzdKXm9u31ZPiSr5_vH5WKVmDTNYqItsjyHDCzPkKMRWDKWQskZL5gVleYMWam5yLQSViODuRK6wjTLq1JBkU7J1eg7BH_sMES5Hp7sh0jJ00KUPIM8H1jZyDLeheCxklvfdMrvJQN52FSu5bipPGwqx00H2c0ow6HBZ4NeBtNgb4aqHk2U1jX_G_wCvO-EEg</recordid><startdate>20200305</startdate><enddate>20200305</enddate><creator>Xavier, Vinicius Layter</creator><creator>Xavier, Adilson Elias</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>E3H</scope><scope>F2A</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20200305</creationdate><title>Accelerated hyperbolic smoothing method for solving the multisource Fermat–Weber and k-Median problems</title><author>Xavier, Vinicius Layter ; Xavier, Adilson Elias</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-bde166040d24e2ec5e81130821271d5fb21e18b254ba5dbe109a5bfe346f8a073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Clustering</topic><topic>Facility location</topic><topic>Global optimization</topic><topic>k-Median</topic><topic>Mathematical models</topic><topic>Min-sum-distances clustering</topic><topic>Multisource Fermat–Weber</topic><topic>Smoothing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xavier, Vinicius Layter</creatorcontrib><creatorcontrib>Xavier, Adilson Elias</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Knowledge-based systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xavier, Vinicius Layter</au><au>Xavier, Adilson Elias</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Accelerated hyperbolic smoothing method for solving the multisource Fermat–Weber and k-Median problems</atitle><jtitle>Knowledge-based systems</jtitle><date>2020-03-05</date><risdate>2020</risdate><volume>191</volume><spage>105226</spage><pages>105226-</pages><artnum>105226</artnum><issn>0950-7051</issn><eissn>1872-7409</eissn><abstract>This article deals with the Multisource Fermat–Weber and continuous k-Median problems. 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subjects | Clustering Facility location Global optimization k-Median Mathematical models Min-sum-distances clustering Multisource Fermat–Weber Smoothing |
title | Accelerated hyperbolic smoothing method for solving the multisource Fermat–Weber and k-Median problems |
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