Abstract
It is commonly observed in the food industry, battery production, automotive paint shop, and semiconductor manufacturing that an intermediate product’s residence time in the buffer within a production line is controlled by a time window to guarantee product quality. There is typically a minimum time limit reflected by a part’s travel time or process requirement. Meanwhile, these intermediate parts are prevented from staying in the buffer for too long by an upper time limit, exceeding which a part will be scrapped or need additional treatment. To increase production throughput and reduce scrap, one needs to control machines’ working mode according to real-time system information in the stochastic production environment, which is a difficult problem to solve, due to the system’s complexity. In this article, we propose a novel decomposition-based control approach by decomposing a production system into small-scale subsystems based on domain knowledge and their structural relationship. An iterative aggregation procedure is then used to generate a production control policy with convergence guarantee. Numerical studies suggest that the decomposition-based control approach outperforms general-purpose reinforcement learning method by delivering significant system performance improvement and substantial reduction on computation overhead.
Original language | English (US) |
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Pages (from-to) | 943-959 |
Number of pages | 17 |
Journal | IISE Transactions |
Volume | 53 |
Issue number | 9 |
DOIs | |
State | Published - 2021 |
Keywords
- Residence time
- decomposition-based control
- multi-stage transfer line
- real-time control
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering