Dergi makalesi Açık Erişim
Öksüz, Mehmet Kürşat;
Büyüközkan, Kadir;
Bal, Alperen;
Satoğlu, Şule Itır
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.48623/aperta.263031</identifier> <creators> <creator> <creatorName>Öksüz, Mehmet Kürşat</creatorName> <givenName>Mehmet Kürşat</givenName> <familyName>Öksüz</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5791-3845</nameIdentifier> <affiliation>Erzincan Binali Yıldırım Üniversitesi</affiliation> </creator> <creator> <creatorName>Büyüközkan, Kadir</creatorName> <givenName>Kadir</givenName> <familyName>Büyüközkan</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6321-0302</nameIdentifier> <affiliation>Karadeniz Teknik Üniversitesi</affiliation> </creator> <creator> <creatorName>Bal, Alperen</creatorName> <givenName>Alperen</givenName> <familyName>Bal</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0675-0796</nameIdentifier> <affiliation>American University of the Middle East</affiliation> </creator> <creator> <creatorName>Satoğlu, Şule Itır</creatorName> <givenName>Şule Itır</givenName> <familyName>Satoğlu</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-2768-4038</nameIdentifier> <affiliation>İstanbul Teknik Üniversitesi</affiliation> </creator> </creators> <titles> <title>A Genetic Algorithm Integrated With The Initial Solution Procedure And Parameter Tuning For Capacitated P-Median Problem</title> </titles> <publisher>Aperta</publisher> <publicationYear>2023</publicationYear> <subjects> <subject>Location-Allocation</subject> <subject>Capacitated p-median problem</subject> <subject>Facility location</subject> <subject>Genetic algorithm</subject> <subject>Initial solution algorithm</subject> <subject>Parameter tuning</subject> </subjects> <dates> <date dateType="Issued">2023-04-12</date> </dates> <resourceType resourceTypeGeneral="Text">Journal article</resourceType> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/263031</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.48623/aperta.263030</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>The capacitated p-median problem is a well-known location-allocation problem that is NP-hard. We proposed an advanced<br> Genetic Algorithm (GA) integrated with an Initial Solution Procedure for this problem to solve the medium and large-size<br> instances. A 3<sup>3</sup> Full Factorial Design was performed where three levels were selected for the probability of mutation,<br> population size, and the number of iterations. Parameter tuning was performed to reach better performance at each<br> instance. MANOVA and Post-Hoc tests were performed to identify significant parameter levels, considering both computational<br> time and optimality gap percentage. Real data of Lorena and Senne (2003) and the data set presented by<br> Stefanello et al. (2015) were used to test the proposed algorithm, and the results were compared with those of the other<br> heuristics existing in the literature. The proposed GA was able to reach the optimal solution for some of the instances in<br> contrast to other metaheuristics and the Mat-heuristic, and it reached a solution better than the best known for the largest<br> instance and found near-optimal solutions for the other cases. The results show that the proposed GA has the potential to<br> enhance the solutions for large-scale instances. Besides, it was also shown that the parameter tuning process might improve<br> the solution quality in terms of the objective function and the CPU time of the proposed GA, but the magnitude of<br> improvement may vary among different instances.</p></description> </descriptions> <fundingReferences> <fundingReference> <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName> <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier> <awardNumber>215M143</awardNumber> </fundingReference> </fundingReferences> </resource>
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Görüntülenme | 54 | 54 |
İndirme | 62 | 62 |
Veri hacmi | 58.7 MB | 58.7 MB |
Tekil görüntülenme | 50 | 50 |
Tekil indirme | 61 | 61 |