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Local order scheduling for mixed-model assembly lines in the aircraft manufacturing industry
- Source :
- Production Engineering, 12 (6), 759–767
- Publication Year :
- 2018
- Publisher :
- Springer, 2018.
-
Abstract
- Multi-variant products to be assembled on mixed-model assembly lines at locations within a production network need to be scheduled locally. Scheduling is a highly complex task especially if it simultaneously covers the assignment of orders, which are product variants to be assembled within a production period, to assembly lines as well as their sequencing on the lines. However, this is required if workers can flexibly fulfill tasks across stations of several lines and, thus, capacity of workers is shared among the lines. As this is the case for final assembly of the Airbus A320 Family, this paper introduces an optimization model for local order scheduling for mixed-model assembly lines covering both assignment to lines as well as sequencing. The model integrates the planning approaches mixed-model sequencing and level scheduling in order to minimize work overload in final assembly and to level material demand with regard to suppliers. The presented model is validated in the industrial application of the final assembly of the Airbus A320 Family. The results demonstrate significant improvement in terms of less work overload and a more even material demand compared to current planning.
- Subjects :
- Mixed model
HD
0209 industrial biotechnology
021103 operations research
Work overload
Computer science
TL
Mechanical Engineering
0211 other engineering and technologies
Scheduling (production processes)
Order scheduling
02 engineering and technology
Industrial engineering
Industrial and Manufacturing Engineering
Product variant
020901 industrial engineering & automation
Aircraft manufacturing
HD28
ddc:620
Engineering & allied operations
Subjects
Details
- Language :
- English
- ISSN :
- 09446524 and 18637353
- Database :
- OpenAIRE
- Journal :
- Production Engineering, 12 (6), 759–767
- Accession number :
- edsair.doi.dedup.....dd38dde17141f47d11bacfaf3faeff5d