Hierarchical and two-stage framework for the paced mixed-model assembly line balancing and sequencing problem considering ergonomic risk

Abstract

Paced mixed-model assembly lines are popular with various manufacturing enterprises. However, they face the weakness that they are at risk of line stoppages owing to the occurrence of workstation work overload situations. Moreover, the consideration of workers' health and ergonomic risk on manual assembly lines is a necessity required by legislation. Therefore, this article addresses mixed-model assembly line balancing and sequencing taking the problem of ergonomic risk into consideration and manages work overloads using a side-by-side policy with utility workers. To solve these problems, this article proposes an hierarchical and two-stage framework. The optimization goals are to minimize the number of workstations and utility workers. Furthermore, this article integrates an iterated greedy algorithm into a genetics algorithm to obtain global exploration and local exploitation ability. Finally, a divide-and-conquer strategy is proposed to meet the challenge of solving a large-scale problem. Experimental results show the effectiveness of the mechanism proposed in this article.

Keywords:

• Paced mixed-model assembly lines
• balancing and sequencing
• ergonomic risk
• hierarchical framework
• divide-and-conquer strategy

Disclosure statement

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

Data availability statement

Data sharing is not applicable to this article as no new data were created or analysed in this study.

Additional information

Funding

This work was supported by the Science Fund for Distinguished Young Scholars of Guangdong Province [grant number 2022B1515020076]; the National Natural Science Foundation of China [grant numbers U20A6004, 72271062, 52075107]; the State Administration of Science, Technology and Industry for National Defense, PRC [grant number JCKY2020209B005]; the Science and Technology Planning Project of Guangdong Province [grant number 2022A1515010991]; National Key Research and Development Program of China[2019YFB1703800].