Multi-objective optimisation for energy-aware flexible job-shop scheduling problem with assembly operations

Abstract

There is a lack of studies on joint optimisation of flexible job-shop scheduling problem (FJSP) considering energy consumption and production efficiency in the machining-assembly system. Thus, in this paper, we propose a methodology for multi-objective optimisation of energy-aware flexible job-shop scheduling during machining and assembly operations. First, a mixed integrated mathematical model is developed to improve production efficiency and minimise energy consumption. Then, a novel heuristic algorithm integrated particle swarm optimisation (PSO) and genetic algorithm (GA) is developed to address the established multi-objective problem. Moreover, numerical examples are carried out to verify the validity and performance of the solving methods in achieving energy awareness in the manufacturing system. Computational results are presented to demonstrate the advantage of solving the problem compared with the exact method and common heuristic algorithms, and the trade-off between production efficiency and energy efficiency is analysed to make the final decision for managers.

KEYWORDS:

• Flexible job-shop scheduling system
• machining and assembly operations
• multi-objective optimisation
• energy efficiency

Acknowledgements

We express our sincere thanks to Lovol Heavy Industry Co., Ltd. for the case verification.

Disclosure statement

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

Additional information

Funding

The authors would like to thank the National Key Research and Development Program of China [Project No. 2019YFB1704001] and the National Natural Science Foundation of China [Project No. 51675051].

Notes on contributors

Weibo Ren

Weibo Ren, is currently a DE (Doctor of Engineering) student in School of Mechanical Engineering at Beijing Institute of Technology in China. His primary research focused on Operation Research in Industrial Production and Service System.

Jingqian Wen

Jingqian Wen, is associated professor in School of Mechanical Engineering at Beijing Institute of Technology in China, and he received his Ph.D. from Beihang University. His research interests intelligent manufacturing for personalised production.

Yan Yan

Yan Yan, received her Ph.D. in Beijing Institute of Technology, and is currently professor in the School of Mechanical Engineering at Beijing Institute of Technology in China. Her research interests includes Knowledge Engineering and Operation Research in Manufacturing system.

Yaoguang Hu

Yaoguang Hu, received his Ph.D. in Beihang University, and is currently professor in the School of Mechanical Engineering at Beijing Institute of Technology in China. His primary research focuses on smart manufacturing system and Industrial Product-Service Systems.

Yu Guan

Yu Guan, is a Ph.D. student in School of Mechanical Engineering at Beijing Institute of Technology in China. He works on smart manufacturing system and industrial engineering.

Jinliang Li

Jinliang Li, is General Manager of Lovol Arbos Heavy Industry. His primary resea-rch focused on information technology and maintenance service in agriculturalmachinery.