A fuzzy control system for assembly line balancing with a three-state degradation process in the era of Industry 4.0

ABSTRACT

The assembly line balancing problem is always explored using the assumption that the processing ability of each workstation is constant. However, the initial workload balance can be easily broken by the changing processing condition of the machines, due to degradation. In the context of Industry 4.0, real-time information related to the machine health state is available. The aim is to improve the performance of the assembly process by making full use of the obtained real-time information. This research is the first exploration of real-time assembly line balancing with the changing health states of machines and the trigger point of adjustments to the assembly line. In this study, a fuzzy control system is developed to determine when to re-balance the assembly line and how to adjust the production rates to smooth the workloads of the workstations. The numerical results show that the assembly line with the proposed fuzzy control system satisfies the demand for most cases, and achieves higher utilisation of machines and lower buffer levels. Therefore, the real-time information brought by Industry 4.0 can be used to improve the performance of an assembly line.

KEYWORDS:

• Industry 4.0
• assembly line balancing
• fuzzy control system
• machine degradation
• real-time information

Acknowledgements

The work described in this paper was supported by grants from The Natural Science Foundation of China (grant numbers 71571111, 71971143); the Innovation Method Fund of China (Grant No. 2018IM020200); National Key Research and Development Project (Grant No. 2018YFC0807506); the Fundamental Research Funds for Central Universities [Grant No. 2018JC055]; and The Research Committee of Hong Kong Polytechnic University (Project Numbers G-UADM; G-UAFS). The authors would also like to thank the Qilu Young Scholars and Tang Scholars of Shandong University for financial and technical support. The authors also would like to thank The Hong Kong Polytechnic University Research Committee for financial and technical support, and a grant under student account code RURR.

Disclosure statement

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

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Numbers 71571111, 71971143]; the Innovation Method Fund of China: [Grant Number 2018IM020200]; National Key Research and Development Project (Grant No. 2018YFC0807506); the Fundamental Research Funds for Central Universities [Grant No. 2018JC055]; and The Research Committee of Hong Kong Polytechnic University (Project Numbers G-UADM; G-UAFS).