An improved event-triggered predictive control for capacity adjustment in reconfigurable job-shops

Abstract

In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed ‘Double-layer event-triggering conditions’. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method.

Keywords:

• Reconfiguration machine tool (RMT)
• capacity adjustment
• model predictive control (MPC)
• genetic algorithm (GA)
• production control
• work in process (WIP)

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This work is partially supported by the National Key Research and Development Program of China [grant numbers 2020YFB1712403, 2020YFB1712404, 2020YFB1712405], and the Key Scientific and Technological Project of Henan Province [grant numbers 212102210380, 212102210080].

Notes on contributors

Qiang Zhang

Qiang Zhang received his M.S. degree in Control Theory and Control Engineering from Zhongyuan University of Technology, and Ph.D. degree in Production Engineering from the University of Bremen. His research interests include predictive control in production and logistics, evolutionary computing and optimisation, and reconfigurable manufacturing system. He has published over 10 academic journal and conference papers, such as International Journal of Production Research, Logistics Research, International Conference on Manufacturing Modelling Management and Control (MIM), Vienna International Conference on Mathematical Modelling (MATHMOD), and International Conference on Dynamics in Logistics (LDIC).

Ping Liu

Ping Liu received her B.Sc. degree in Measurement and Control Technology and Instrument and M.S. degree in Control Theory and Control Engineering from Zhongyuan University of Technology, China, in 2012 and 2015, respectively, and the Ph.D. degree in Production Engineering from the University of Bremen, Germany, in 2019. Her research interests include intelligent control, robust control and manufacturing control.

Yu Chen

Yu Chen serves as a professor in the School of Intelligent Engineering, Zhengzhou University of Aeronautics. He received the M.S. degree in circuit and system from Northwestern Polytechnical University, Xi'an, China, in 2009, and the Ph.D. degree in electronic science and technology from Northwestern Polytechnical University, Xi'an, China, in 2017. His research interests include data acquisition and signal processing, network information security, navigation guidance and control, and network collaborative manufacturing.

Quan Deng

Quan Deng works as a research scientist in the department of Intelligent ICT for Co-operative Production at BIBA since early 2016. He graduated in Software Engineering from Harbin Institute of Technology in China and obtained his Master of Engineering degree in Software Engineering from Harbin Institute of Technology in China and Master of Science in Global Production Engineering from the Technical University of Berlin in Germany. From 2013 to 2016, he worked as a software engineer in PLM & Process Management in Zuken Contact GmbH & Co KG, Bremen, Germany. Since he joined BIBA, he has been involved in several industrial, national and European projects, such as KIPro, FALCON, NIMBLE, LEVEL-UP, i4Q, and TRICK.

Jürgen Pannek

Jürgen Pannek received his Ph.D. in mathematics from University of Bayreuth, Germany, in 2010. Thereafter, he held postdoc position in economics at Curtin University and The University of Western Australia, Australia, in 2010 and 2011. He joined the University of Federal Armed Forces Munich in 2012 as research assistant and substitute professor in aerospace engineering. In 2014, he became a professor in production engineering at the University of Bremen, Germany. From 2019 to 2021, he joint industry and became senior technical consultant responsible for building up a new robotic branch. In 2021, he joined TU Braunschweig, Germany, as head of Institute for Intermodal Transport and Logistic Systems. His research is driven by development and application of control methods with a particular focus on system theory.