Hochschulschrift
Adaptive order dispatching based on reinforcement learning : application in a complex job shop in the semiconductor industry
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISBN
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9783844077629
3844077626
- Dimensions
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21 cm, 326 g
- Extent
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176, XXX Seiten
- Language
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Englisch
- Notes
-
Illustrationen
Karlsruher Institut für Technologie (KIT), Dissertation, 2020
- Bibliographic citation
-
Forschungsberichte aus dem wbk, Institut für Produktionstechnik, Karlsruher Institut für Technologie (KIT) ; Band 241
- Keyword
-
Auftragsabwicklung
Bestärkendes Lernen
Prozessoptimierung
Order
- Event
-
Veröffentlichung
- (where)
-
Düren
- (who)
-
Shaker Verlag GmbH
- (when)
-
[2020]
- Creator
- Contributor
- Table of contents
- Rights
-
Bei diesem Objekt liegt nur das Inhaltsverzeichnis digital vor. Der Zugriff darauf ist unbeschränkt möglich.
- Last update
-
11.03.2025, 12:34 PM CET
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Hochschulschrift
Associated
- Kuhnle, Andreas
- Shaker Verlag
- Shaker Verlag GmbH
Time of origin
- [2020]
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Adaptive representations for reinforcement learning
Complex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturing
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
Energy-Flexible Job-Shop Scheduling Using Deep Reinforcement Learning
Reinforcement Learning for Robust Adaptive Quantum-Enhanced Metrology
Generic multi-agent reinforcement learning approach for flexible job-shop scheduling
Multi-agent reinforcement learning approaches for distributed job shop scheduling problems
Designing an adaptive production control system using reinforcement learning
Complex adaptive systems : an introduction
Reinforcement Learning Strategies for a Context-Aware Adaptive Cruise Control
Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning
Adaptive Order Dispatching based on Reinforcement Learning: Application in a Complex Job Shop in the Semiconductor Industry
Adaptive representations for reinforcement learning
Complex job shop simulation “CoJoSim” : a reference model for simulating semiconductor manufacturing
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
Energy-Flexible Job-Shop Scheduling Using Deep Reinforcement Learning
Reinforcement Learning for Robust Adaptive Quantum-Enhanced Metrology
Generic multi-agent reinforcement learning approach for flexible job-shop scheduling
Multi-agent reinforcement learning approaches for distributed job shop scheduling problems
Designing an adaptive production control system using reinforcement learning
Complex adaptive systems : an introduction
Reinforcement Learning Strategies for a Context-Aware Adaptive Cruise Control