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  <doc>
    <id>10067</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>226</volume>
    <type>bookpart</type>
    <publisherName>Springer</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Flexible Pooling Pattern Design with Integer Programming</title>
    <abstract language="eng">Sample pooling has the potential to significantly enhance large-scale screening procedures, especially in scenarios like the COVID-19 pandemic, where rapid and widespread PCR testing has been crucial. Efficient strategies are essential to increase the testing capacity, i.e., the number of tests that can be processed within a given timeframe. Non-adaptive pooling strategies can further streamline the testing process by reducing the required testing rounds. In contrast to adaptive strategies, where subsequent tests depend on prior results, non-adaptive pooling processes all samples in a single round, eliminating the need for sequential retesting and reducing delays. This paper presents a highly flexible method based on integer programming to design optimized pooling patterns suitable for various applications, including&#13;
medical diagnostics and quality control in industrial production. Using coronavirus&#13;
testing as a case study, we formulate and solve optimization and satisfiability models&#13;
that compute efficient pool designs. Our optimized pooling does not only increase&#13;
testing capacity, but also accelerates the testing process and reduces overall costs.&#13;
The proposed method is adaptable and can be seamlessly integrated into automated&#13;
testing systems.</abstract>
    <parentTitle language="eng">Theory, Algorithms and Experiments in Applied Optimization. In Honor of the 70th Birthday of Panos Pardalos</parentTitle>
    <enrichment key="Series">Springer Optimization and Its Applications</enrichment>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="AcceptedDate">2025-03-20</enrichment>
    <author>Uwe Gotzes</author>
    <submitter>Janina Zittel</submitter>
    <author>Annika Buchholz</author>
    <author>Josef Kallrath</author>
    <author>Niels Lindner</author>
    <author>Thorsten Koch</author>
    <collection role="persons" number="koch">Koch, Thorsten</collection>
    <collection role="projects" number="MODAL-Gesamt">MODAL-Gesamt</collection>
    <collection role="persons" number="lindner">Lindner, Niels</collection>
    <collection role="institutes" number="aim">Applied Algorithmic Intelligence Methods</collection>
    <collection role="projects" number="MODAL-EnergyLab">MODAL-EnergyLab</collection>
    <collection role="persons" number="buchholz">Buchholz, Annika</collection>
    <collection role="institutes" number="aopt">Applied Optimization</collection>
  </doc>
</export-example>
