12 Nisan 2023 Dergi makalesi Açık Erişim

Öksüz, Mehmet Kürşat; Büyüközkan, Kadir; Bal, Alperen; Satoğlu, Şule Itır

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  <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">&lt;p&gt;The capacitated p-median problem is a well-known location-allocation problem that is NP-hard. We proposed an advanced&lt;br&gt;
Genetic Algorithm (GA) integrated with an Initial Solution Procedure for this problem to solve the medium and large-size&lt;br&gt;
instances. A 3&lt;sup&gt;3&lt;/sup&gt; Full Factorial Design was performed where three levels were selected for the probability of mutation,&lt;br&gt;
population size, and the number of iterations. Parameter tuning was performed to reach better performance at each&lt;br&gt;
instance. MANOVA and Post-Hoc tests were performed to identify significant parameter levels, considering both computational&lt;br&gt;
time and optimality gap percentage. Real data of Lorena and Senne (2003) and the data set presented by&lt;br&gt;
Stefanello et al. (2015) were used to test the proposed algorithm, and the results were compared with those of the other&lt;br&gt;
heuristics existing in the literature. The proposed GA was able to reach the optimal solution for some of the instances in&lt;br&gt;
contrast to other metaheuristics and the Mat-heuristic, and it reached a solution better than the best known for the largest&lt;br&gt;
instance and found near-optimal solutions for the other cases. The results show that the proposed GA has the potential to&lt;br&gt;
enhance the solutions for large-scale instances. Besides, it was also shown that the parameter tuning process might improve&lt;br&gt;
the solution quality in terms of the objective function and the CPU time of the proposed GA, but the magnitude of&lt;br&gt;
improvement may vary among different instances.&lt;/p&gt;</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>
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