Details
| Titel in Übersetzung | Machine learning potentials in adaptive manufacturing control |
|---|---|
| Originalsprache | Deutsch |
| Seiten (von - bis) | 17-20 |
| Seitenumfang | 4 |
| Fachzeitschrift | Fabriksoftware |
| Jahrgang | 24 |
| Ausgabenummer | 3 |
| Publikationsstatus | Veröffentlicht - 2019 |
Abstract
In work planning, static conditions are currently assumed and supposedly optimal production sequences are defined before start of production. Dynamic influences during production lead to unsystematic rescheduling and an inefficient planning result. Therefore, a machine learning approach for adaptive production control using genetic algorithms is presented.
Schlagwörter
- Genetic Algorithm, Machine Learning, Production Control
ASJC Scopus Sachgebiete
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Technologie- und Innovationsmanagement
- Informatik (insg.)
- Software
- Informatik (insg.)
- Angewandte Informatik
- Informatik (insg.)
- Information systems
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
Ziele für nachhaltige Entwicklung
Zitieren
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- BibTex
- RIS
in: Fabriksoftware, Jahrgang 24, Nr. 3, 2019, S. 17-20.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Machine Learning in der adaptiven Fertigungssteuerung
T2 - Genetischer Algorithmus zur Bewertung alternativer Arbeitspläne
AU - Denkena, Berend
AU - Wilmsmeier, Sören
AU - Winter, Florian
PY - 2019
Y1 - 2019
N2 - In work planning, static conditions are currently assumed and supposedly optimal production sequences are defined before start of production. Dynamic influences during production lead to unsystematic rescheduling and an inefficient planning result. Therefore, a machine learning approach for adaptive production control using genetic algorithms is presented.
AB - In work planning, static conditions are currently assumed and supposedly optimal production sequences are defined before start of production. Dynamic influences during production lead to unsystematic rescheduling and an inefficient planning result. Therefore, a machine learning approach for adaptive production control using genetic algorithms is presented.
KW - Genetic Algorithm
KW - Machine Learning
KW - Production Control
UR - http://www.scopus.com/inward/record.url?scp=85093943783&partnerID=8YFLogxK
M3 - Artikel
AN - SCOPUS:85093943783
VL - 24
SP - 17
EP - 20
JO - Fabriksoftware
JF - Fabriksoftware
SN - 2569-7692
IS - 3
ER -