Publikation: Adaptive Artificial Intelligence Schedu...
Stammdaten
Titel: | Adaptive Artificial Intelligence Scheduling Methods for Large-Scale, Stochastic, Industrial Applications |
Untertitel: | |
Kurzfassung: | Traditional scheduling techniques suffer from a lack of flexibility. The problem's instances need to be deterministic, and results on datasets with small benchmark instances do usually not transfer to large-scale instances. We propose to develop adaptive algorithms that can leverage the similarities between instances of industrial scheduling problems. In particular, we focus on applications of modern machine learning techniques to combinatorial optimization problems, an emerging and promising research area. Traditional scheduling techniques such as constraint, mixed-integer, or answer set programming are highly generic, domain-independent, and, therefore, do not explicitly exploit the specificities of a problem domain. However, in a production facility, the settings between two consecutive schedules are often very similar. The machines, workers, production capacity, etc., usually stay the same or do not change significantly. Traditional scheduling techniques do not take advantage of such similarities, while machine learning, especially deep learning, can discover and exploit relationships in the data. Therefore, our research aims to incorporate machine learning into combinatorial optimization. |
Schlagworte: | Machine Learning; Planning, Routing, and Scheduling; Constraint Satisfaction and Optimization |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 23.07.2022 (Online) |
Erschienen in: |
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22)
(
International Joint Conferences on Artificial Intelligence;
L. de Raedt
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 5877 - 5878 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 23.07.2022 |
ISBN (e-book): |
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eISSN: | - |
DOI: | http://dx.doi.org/10.24963/ijcai.2022/841 |
Homepage: | - |
Open Access |
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AutorInnen
Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Artificial Intelligence und Cybersecurity
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AT - A-9020 Klagenfurt |
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