EconPapers    
Economics at your fingertips  
 

Task allocation of human-robot collaborative assembly line considering assembly complexity and workload balance

Min Cai, Gen Wang, Xinggang Luo and Xueqi Xu

International Journal of Production Research, 2025, vol. 63, issue 13, 4749-4775

Abstract: Human-robot collaboration is increasingly utilised in assembly lines, where task allocation is critical. To address the task allocation problem, this paper first evaluates each assembly task using the indicator of automation potential to determine if a collaborative robot can complete it. The method for evaluating assembly complexity and workload is then introduced, which determines the assembly complexity of each task for both robots and workers, as well as the workload for workers. Based on the above indicators, a new task allocation optimisation model for the human-robot collaborative assembly line is established with the objectives of minimising the cycle time, minimising the workload variance between different workstations, and the assembly complexity per unit product. An improved multi-objective migratory bird optimisation algorithm with fast non-dominated sorting is developed to solve the mathematical model of this task allocation. Finally, the proposed method is applied to an assembly line in a real enterprise. The results of algorithm comparisons show that the proposed algorithm is effective, and some managerial insights are also derived from the experimental tests. The result shows that the study effectively reduces product assembly complexity and balances workers’ workload across stations while maintaining assembly efficiency.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2024.2442546 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tprsxx:v:63:y:2025:i:13:p:4749-4775

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/00207543.2024.2442546

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-08-05
Handle: RePEc:taf:tprsxx:v:63:y:2025:i:13:p:4749-4775