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    <completedDate>2022-02-24</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Standardized framework for evaluating centralized and decentralized control systems in modular assembly systems</title>
    <parentTitle language="eng">2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)</parentTitle>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Arnet</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>virtual commissioning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cyber-physical production system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital manufacturing</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>scheduling</value>
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      <language>eng</language>
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      <value>production control</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-agent system</value>
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    <subject>
      <language>eng</language>
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      <value>modular assembly system</value>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
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  <doc>
    <id>1470</id>
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    <title language="eng">Adaptive production control with negotiating agents in modular assembly systems</title>
    <parentTitle language="eng">2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)</parentTitle>
    <identifier type="isbn">978-1-7281-4569-3</identifier>
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    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Arnet</last_name>
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    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>multi-agent system</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>negotiation-based coordination</value>
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      <language>eng</language>
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      <value>intelligent manufacturing</value>
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      <language>eng</language>
      <type>uncontrolled</type>
      <value>production scheduling</value>
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      <language>eng</language>
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      <value>production control</value>
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      <language>eng</language>
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      <value>modular assembly system</value>
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    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
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  <doc>
    <id>1309</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2335</pageFirst>
    <pageLast>2351</pageLast>
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    <edition/>
    <issue>8</issue>
    <volume>32</volume>
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    <type>article</type>
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    <publisherPlace>Cham</publisherPlace>
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    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Modular production control using deep reinforcement learning: proximal policy optimization</title>
    <abstract language="eng">EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design.</abstract>
    <parentTitle language="eng">Journal of Intelligent Manufacturing</parentTitle>
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    <identifier type="urn">urn:nbn:de:bvb:573-13092</identifier>
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      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
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      <first_name>Tobias</first_name>
      <last_name>Classen</last_name>
    </author>
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      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>modular production</value>
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      <value>production control</value>
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      <value>production scheduling</value>
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      <value>deep reinforcement learning</value>
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      <language>eng</language>
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      <value>proximal policy optimization</value>
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    <subject>
      <language>eng</language>
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      <value>automotive industry</value>
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    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1309/Mayer2021_ModularProductionControlUsingD.pdf</file>
  </doc>
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    <id>6028</id>
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    <publishedYear>2021</publishedYear>
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    <completedDate>2025-06-26</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Modular Production Control with Multi-Agent Deep Q-Learning</title>
    <parentTitle language="eng">Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</parentTitle>
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    <author>
      <first_name>Dennis</first_name>
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