1 Ocak 2009 Dergi makalesi Açık Erişim

Solyali, O.; Ozpeynirci, O.

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/97013</identifier>
  <creators>
    <creator>
      <creatorName>Solyali, O.</creatorName>
      <givenName>O.</givenName>
      <familyName>Solyali</familyName>
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    <creator>
      <creatorName>Ozpeynirci, O.</creatorName>
      <givenName>O.</givenName>
      <familyName>Ozpeynirci</familyName>
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  <titles>
    <title>Operational Fixed Job Scheduling Problem Under Spread Time Constraints: A Branch-And-Price Algorithm</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2009</publicationYear>
  <dates>
    <date dateType="Issued">2009-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/97013</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1080/00207540701666204</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">This study addresses the operational fixed job scheduling problem under spread time constraints. The problem is to select a subset of jobs having fixed ready times and deadlines for processing on identical parallel machines such that total weight of the selected jobs is maximised. We first give a mathematical formulation of the problem and then reformulate it using Dantzig-Wolfe decomposition. We propose a branch-and-price algorithm that works on the reformulation of the problem. Computational results show that our algorithm is far superior to its competitor in the literature. It solves instances that could not be solved in one hour CPU time in less than a second and is able to solve large-scale instances in reasonable times which make it a computationally viable tool for decision-making.</description>
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