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  <doc>
    <id>6110</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
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    <completedDate>--</completedDate>
    <publishedDate>2016-11-24</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Tackling Industrial-Scale Supply Chain Problems by Mixed-Integer Programming</title>
    <abstract language="eng">SAP's decision support systems for optimized supply network planning rely on mixed-integer programming as the core engine to compute optimal or near-optimal solutions. The modeling flexibility and the optimality guarantees provided by mixed-integer programming greatly aid the design of a robust and future-proof decision support system for a large and diverse customer base. In this paper we describe our coordinated efforts to ensure that the performance of the underlying solution algorithms matches the complexity of the large supply chain problems and tight time limits encountered in practice.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-61107</identifier>
    <identifier type="doi">10.4208/jcm.1905-m2019-0055</identifier>
    <enrichment key="SourceTitle">Journal of Computational Mathematics</enrichment>
    <author>Gerald Gamrath</author>
    <submitter>Ambros Gleixner</submitter>
    <author>Ambros Gleixner</author>
    <author>Thorsten Koch</author>
    <author>Matthias Miltenberger</author>
    <author>Dimitri Kniasew</author>
    <author>Dominik Schlögel</author>
    <author>Alexander Martin</author>
    <author>Dieter Weninger</author>
    <series>
      <title>ZIB-Report</title>
      <number>16-45</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>supply chain management, supply network optimization, mixed-integer linear programming, primal heuristics, numerical stability, large-scale optimization</value>
    </subject>
    <collection role="msc" number="90B06">Transportation, logistics</collection>
    <collection role="msc" number="90C11">Mixed integer programming</collection>
    <collection role="institutes" number="optimization">Mathematical Optimization</collection>
    <collection role="institutes" number="mip">Mathematical Optimization Methods</collection>
    <collection role="persons" number="gamrath">Gamrath, Gerald</collection>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="persons" number="miltenberger">Miltenberger, Matthias</collection>
    <collection role="projects" number="ASTfSCM">ASTfSCM</collection>
    <collection role="projects" number="MIP-ZIBOPT">MIP-ZIBOPT</collection>
    <collection role="projects" number="MODAL-SynLab">MODAL-SynLab</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/6110/ZR-16-45.pdf</file>
    <file>https://opus4.kobv.de/opus4-zib/files/6110/ZR-16-45-rev1.pdf</file>
  </doc>
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