Decomposition-based real-time control of multi-stage transfer lines with residence time constraints

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.

Keywords:

• Residence time
• real-time control
• multi-stage transfer line
• decomposition-based control

Additional information

Funding

This work is supported by the U.S. National Science Foundation under Grant CMMI-1922739.

Notes on contributors

Feifan Wang

Feifan Wang received a bachelor’s degree from the Department of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China, in 2013, and a master’s degree from the Department of Industrial and Systems Engineering, Zhejiang University, Hangzhou, China, in 2016. He is currently pursuing a PhD degree with the School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA. His research interests include the modeling, analysis, and control of production systems. He won the best student paper award in IEEE CASE 2019.

Feng Ju

Feng Ju is an assistant professor with the School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA. He received a BS degree from Shanghai Jiao Tong University, Shanghai, China, in 2010, and an MS degree in electrical and computer engineering and PhD degree in industrial and systems engineering from the University of Wisconsin, Madison, WI, USA, in 2011 and 2015, respectively. His current research interests include modeling, analysis, continuous improvement, and optimization of manufacturing systems. Dr. Ju is also a member of the Institute for Operations Research and the Management Sciences, Institute of Industrial and Systems Engineers, and Institute of Electrical and Electronics Engineers. He was a recipient of multiple awards, including the best paper award in IFAC MIM, best student paper award in IEEE CASE, and best student paper finalist in IEEE CASE and IFAC INCOM.