<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>5832</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Reinforcement Learning for Adaptive Job Shop Scheduling in Robotic Cells: Handling Disruptions Effectively</title>
    <parentTitle language="eng">2025 11th International Conference on Mechatronics and Robotics Engineering (ICMRE)</parentTitle>
    <identifier type="url">10.1109/ICMRE64970.2025.10976238</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <author>Eddi Miller</author>
    <author>Anna-Maria Schmitt</author>
    <author>Tobias Kaupp</author>
    <author>Andreas Schiffler</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
  </doc>
</export-example>
