Details
| Translated title of the contribution | Layout Optimization for Small-scale Modular Conveyor Systems |
|---|---|
| Original language | German |
| Pages (from-to) | 232-236 |
| Number of pages | 5 |
| Journal | ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb |
| Volume | 116 |
| Issue number | 4 |
| Early online date | 21 Apr 2021 |
| Publication status | Published - 30 Apr 2021 |
| Externally published | Yes |
Abstract
2021 The machine learning based method for layout optimization of smallscale modular conveyor systems, which is developed within a research project at IPH - Institut für Integrierte Produktion Hannover gGmbH, provides SMEs a decision support, which enables them to execute complex layout planning independently. In addition, the machine learning method is intended to reduce the cost and time required for planning and to improve the quality of the solution compared to manual layout design.
ASJC Scopus subject areas
- Engineering(all)
- Business, Management and Accounting(all)
- Strategy and Management
- Decision Sciences(all)
- Management Science and Operations Research
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In: ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb, Vol. 116, No. 4, 30.04.2021, p. 232-236.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Layoutoptimierung für kleinskalige modulare Förderanlagen
AU - Aurich, Paul
AU - Stonis, Malte
AU - Overmeyer, Ludger
PY - 2021/4/30
Y1 - 2021/4/30
N2 - 2021 The machine learning based method for layout optimization of smallscale modular conveyor systems, which is developed within a research project at IPH - Institut für Integrierte Produktion Hannover gGmbH, provides SMEs a decision support, which enables them to execute complex layout planning independently. In addition, the machine learning method is intended to reduce the cost and time required for planning and to improve the quality of the solution compared to manual layout design.
AB - 2021 The machine learning based method for layout optimization of smallscale modular conveyor systems, which is developed within a research project at IPH - Institut für Integrierte Produktion Hannover gGmbH, provides SMEs a decision support, which enables them to execute complex layout planning independently. In addition, the machine learning method is intended to reduce the cost and time required for planning and to improve the quality of the solution compared to manual layout design.
KW - Fördersysteme
KW - Kleinskalige modulare Förderer
KW - Künstliche Intelligenz
KW - Maschinelles Lernen
KW - Materialfluss und Logistik
KW - Operations Research
UR - http://www.scopus.com/inward/record.url?scp=85105141002&partnerID=8YFLogxK
U2 - 10.1515/zwf-2021-0048
DO - 10.1515/zwf-2021-0048
M3 - Artikel
AN - SCOPUS:85105141002
VL - 116
SP - 232
EP - 236
JO - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
JF - ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb
SN - 0947-0085
IS - 4
ER -