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.
...
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89700 |
| Date | 13 February 2024 |
| Creators | Heik, David, Bahrpeyma, Fouad, Reichelt, Dirk |
| Contributors | Hochschule für Technik, Wirtschaft und Kultur Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 978-3-910103-02-3, urn:nbn:de:bsz:l189-qucosa2-896465, qucosa:89646 |
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