Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (2): 297-317.doi: 10.23919/JSEE.2021.000026

• INTELLIGENT OPTIMIZATION AND SCHEDULING • Previous Articles     Next Articles

Multi-objective reconfigurable production line scheduling for smart home appliances

Shiyun LI(), Sheng ZHONG(), Zhi PEI*(), Wenchao YI(), Yong CHEN(), Cheng WANG(), Wenzhu ZHANG()   

  1. 1 College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
  • Received:2020-10-28 Online:2021-04-29 Published:2021-04-29
  • Contact: Zhi PEI E-mail:lishiyun@zjut.edu.cn;zsheng2811@qq.com;peizhi@zjut.edu.cn;yiwenchao@zjut.edu.cn;cy@zjut.edu.cn;cwang@zjut.edu.cn;wzzhang@zjut.edu.cn
  • About author:|LI Shiyun was born in 1978. He received his B.E. degree in mechanical manufacturing and automation from Beijing Institute of Technology, China, in 2001, and his Ph.D. degree in management information technology in digital design and manufacture from Beijing Institute of Technology, China, in 2006. He is currently working as a lecturer at the College of Mechanical Engineering, Zhejiang University of Technology, China. His research interests include mathematical modeling and optimization as well as their applications in design and manufacturing management. E-mail: lishiyun@zjut.edu.cn||ZHONG Sheng was born in 1995. He is currently working toward his master degree in the College of Mechanical Engineering, Zhejiang University of Technology. His research interests are intelligent algorithm design and application. E-mail: zsheng2811@qq.com||PEI Zhi was born in 1982. He received his B.S. and Ph.D. degrees in industrial engineering from Tsinghua University, Beijing, China, in 2005 and 2011, respectively. He was a visiting professor with North Carolina State University, Raleigh, USA, in 2015. He is currently an associate professor with the College of Mechanical Engineering, Zhejiang University of Technology, China. His current research interests include manufacturing system modeling, machine scheduling, nonlinear optimization, and queuing theory. E-mail: peizhi@zjut.edu.cn||YI Wenchao was born in 1989. She received her B.S. and Ph.D. degrees in industrial engineering from Huazhong University of Science and Technology, China, in 2011 and 2016, respectively. She is currently an associate professor with the College of Mechanical Engineering, Zhejiang University of Technology, China. Her current research interests include evolutionary algorithms and scheduling. E-mail: yiwenchao@zjut.edu.cn||CHEN Yong was born in 1973. He received his B.S., M.S. and Ph.D. degrees in mechanical engineering from Zhejiang University, in 1995, 1997 and 2000 respectively. Since 2009, he has been a professor with the College of Mechanical Engineering, Zhejiang University of Technology. He is the author of more than 50 academic arti-cles and holds five patents and 40 software copyrights. His research interests include intelligent system planning and intelligent algorithms. E-mail: cy@zjut.edu.cn||WANG Cheng was born in 1982. He received his B.S. degree in industrial engineering from Zhejiang University of Technology in 2005 and Ph.D. degree in systems engineering from Tianjin University, China in 2010. Since 2016, he has been an associate professor with the College of Mechanical Engineering, Zhejiang University of Technology. His research interests include data driven decision making, complex systems, game theory and mechanism design. E-mail: cwang@zjut.edu.cn||ZHANG Wenzhu was born in 1990. She received her B.S. degree in management science and engineering from Huazhong University of Science and Technology in 2013, and Ph.D. degree in management science and engineering from Fudan University in 2020. She was a visiting scholar with Washington University in Saint Louis, USA, in 2016. She is currently an assistant professor with the College of Mechanical Engineering, Zhejiang University of Technology, China. Her current research interests include game theory, operations management, and optimization. E-mail: wzzhang@zjut.edu.cn
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71871203; 52005447; L1924063), and Zhejiang Provincial Natural Science Foundation of China (LY18G010017; LQ21E050014);This work was supported by the National Natural Science Foundation of China (71871203; 52005447; L1924063), and Zhejiang Provincial Natural Science Foundation of China (LY18G010017; LQ21E050014)

Abstract:

In a typical discrete manufacturing process, a new type of reconfigurable production line is introduced, which aims to help small- and mid-size enterprises to improve machine utilization and reduce production cost. In order to effectively handle the production scheduling problem for the manufacturing system, an improved multi-objective particle swarm optimization algorithm based on Brownian motion (MOPSO-BM) is proposed. Since the existing MOPSO algorithms are easily stuck in the lo-cal optimum, the global search ability of the proposed method is enhanced based on the random motion mechanism of the BM. To further strengthen the global search capacity, a strategy of fitting the inertia weight with the piecewise Gaussian cumulative distribution function (GCDF) is included, which helps to maintain an excellent convergence rate of the algorithm. Based on the commonly used indicators generational distance (GD) and hypervolume (HV), we compare the MOPSO-BM with several other latest algorithms on the benchmark functions, and it shows a better overall performance. Furthermore, for a real reconfigurable production line of smart home appliances, three algorithms, namely non-dominated sorting genetic algorithm-II (NSGA-II), decomposition-based MOPSO (dMOPSO) and MOPSO-BM, are applied to tackle the scheduling problem. It is demonstrated that MOPSO-BM outperforms the others in terms of convergence rate and quality of solutions.

Key words: reconfigurable production line, improved particle swarm optimization (PSO), multi-objective optimization, flexible flowshop scheduling, smart home appliances