Details
| Translated title of the contribution | Machine learning potentials in adaptive manufacturing control |
|---|---|
| Original language | German |
| Pages (from-to) | 17-20 |
| Number of pages | 4 |
| Journal | Fabriksoftware |
| Volume | 24 |
| Issue number | 3 |
| Publication status | Published - 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.
ASJC Scopus subject areas
- Business, Management and Accounting(all)
- Management of Technology and Innovation
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
- Engineering(all)
- Industrial and Manufacturing Engineering
Sustainable Development Goals
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In: Fabriksoftware, Vol. 24, No. 3, 2019, p. 17-20.
Research output: Contribution to journal › Article › Research › 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 -