Dynamic task allocation of hybrid flow shop for machines in parallel with different speeds based on an MLD prediction model

ABSTRACT

The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method.

GRAPHICAL ABSTRACT

KEYWORDS:

• Hybrid flow shop
• task allocation
• mixed logical dynamical model
• model predictive control

Acknowledgments

This research is supported in part by the National Natural Science Foundation of China under Grant 62,173,311 and 72,201,252, and in part by College Youth Backbone Teacher Project of Henan Province under Grant 2021GGJS001.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [62173311 and 72201252].

Notes on contributors

Jianbin Xin

Jianbin Xin (Member, IEEE) received the B.Sc. degree in electrical engineering from Xidian University, China, in 2007, the M.Sc. degree in control science and engineering from Xi’an Jiaotong University, China, in 2010, and the Ph.D. degree from the Department of Maritime and Transport Technology, Delft University of Technology, The Netherlands, in 2015.Currently, he is an Associate Professor with the Department of Automation, Zhengzhou University, China. His research interests include planning and control of smart logistics systems and cooperative robots.

Sixuan Li

Sixuan Li received a bachelor’s degree in Automation from Zhengzhou University in 2021, where she is currently pursuing a master’s degree at the School of Electrical and and Information Engineering. Her research interests include task allocation and vehicle allocation for manufacturing.

Yanjie Zhou

Yanjie Zhou received Ph.D. degree from the Department of Industrial Engineering at Pusan National University in 2020 and received B.S. Degree and M.S. Degree in Computer Science and Computer Applied Technology from Zhengzhou University in 2012 and 2015, respectively. He is currently an associate professor with the School of Management at Zhengzhou University. He focuses on solving real-world optimization problems by using artificial intelligence techniques.

Andrea D’Ariano

Andrea D’Ariano received the B.S. and M.S. degrees in computer science, automation, and management engineering from Roma Tre University and the Ph.D. degree from the Department of Transport and Planning, Delft University of Technology, in April 2008, under the supervision of Prof. I. A. Hansen.Currently, he is a Full Professor at the Department of Engineering, Roma Tre University. His research interests include the study of scheduling problems with application to public transportation and logistics. He is the Associate Editor of well-known international journals, such as Transportation Research—B: Methodological, Transportation Research—C: Emerging Technologies, and Transportation Research—E: Logistics and Transportation Review, and conferences, such as IEEE ITSC.