- Titel
- Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:l189-qucosa2-897000
- Konferenz
- 20. AALE-Konferenz. Bielefeld, 06.03.-08.03.2024
- Quellenangabe
- Tagungsband AALE 2024
Herausgeber: Hochschule für Technik, Wirtschaft und Kultur Leipzig
Erscheinungsort: Leipzig
Erscheinungsjahr: 2024
ISBN: 978-3-910103-02-3 - Erstveröffentlichung
- 2024
- DOI
- https://doi.org/10.33968/2024.52
- Abstract (EN)
- The global value creation networks have experienced increased volatility and dynamic behavior in recent years, resulting in an acceleration of a trend already evident in the shortening of product and technology cycles. In addition, the manufacturing industry is demonstrating a trend of allowing customers to make specific adjustments to their products at the time of ordering. Not only do these changes require a high level of flexibility and adaptability from the cyber-physical systems, but also from the employees and the supervisory production planning. As a result, the development of control and monitoring mechanisms becomes more complex. It is also necessary to adjust the production process dynamically if there are unforeseen events (disrupted supply chains, machine breakdowns, or absences of staff) in order to make the most effective and efficient use of the available production resources. In recent years, reinforcement learning (RL) research has gained increasing popularity in strategic planning as a result of its ability to handle uncertainty in dynamic environments in real time. RL has been extended to include multiple agents cooperating on complex tasks as a solution to complex problems. Despite its potential, the real-world application of multi-agent reinforcement learning (MARL) to manufacturing problems, such as flexible job-shop scheduling, has been less frequently approached. The main reason for this is most of the applications in this field are frequently subject to specific requirements as well as confidentiality obligations. Due to this, it is difficult for the research community to obtain access to them, which presents substantial challenges for the implementation of these tools. ...
- Freie Schlagwörter (EN)
- Multi-Agent Reinforcement Learning, Dynamic Resource Allocation, Manufacturing Systems, Smart Production Systems, Industrial IoT Test Bed
- Herausgeber (Institution)
- Hochschule für Technik, Wirtschaft und Kultur Leipzig
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:l189-qucosa2-897000
- Veröffentlichungsdatum Qucosa
- 13.02.2024
- Dokumenttyp
- Konferenzbeitrag
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0