Back to Search
Start Over
A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines
- Source :
- Journal of Computing and Information Science in Engineering. 20
- Publication Year :
- 2020
- Publisher :
- ASME International, 2020.
-
Abstract
- Reentrant flow plays an important role for the allocation of limited resources in semiconductor manufacturing. In particular, over- or under-loading of workstations may deteriorate performances of the whole production line. Therefore, load balancing is usually accomplished with dispatching rules to balance the workload to enhance production performance. Focus on the realistic needs, a novel prediction-based dynamic scheduling method with a multi-layer perceptron (MLP) is proposed for load balancing. This study proposed MLP based on the simulation dataset of empirical industrial fabrication facilities as the prediction model. The prediction outputs incorporated into the dynamic dispatching rule (DDR) for optimal load balancing based on the queue length at each workstation, named as a dynamic scheduling method considering load balancing (DSMLB). Based on the validation, DSMLB compared with the state-of-the-art dispatching rules shows that DSMLB has improved the daily movement, equipment utilization (EU), throughput rate, and cycle time (CT).
- Subjects :
- 0209 industrial biotechnology
010504 meteorology & atmospheric sciences
Computer science
Scheduling (production processes)
02 engineering and technology
01 natural sciences
Computer Graphics and Computer-Aided Design
Industrial and Manufacturing Engineering
Computer Science Applications
Wafer fabrication
020901 industrial engineering & automation
Multilayer perceptron
Electronic engineering
Integrated circuit fabrication
Software
0105 earth and related environmental sciences
Subjects
Details
- ISSN :
- 19447078 and 15309827
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- Journal of Computing and Information Science in Engineering
- Accession number :
- edsair.doi...........638de83c12cb77f3d3b0479242e3ea8e
- Full Text :
- https://doi.org/10.1115/1.4045742