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Task allocation strategies considering task matching and ergonomics in the human-robot collaborative hybrid assembly cell.
- Source :
- International Journal of Production Research; Nov2023, Vol. 61 Issue 21, p7213-7232, 20p, 1 Black and White Photograph, 2 Diagrams, 5 Charts, 6 Graphs
- Publication Year :
- 2023
-
Abstract
- With the increased use of collaborative robots, a new production model of the human-robot collaborative hybrid assembly cell (HRCHAC) is becoming a new trend in customised production. Collaborative assembly between workers and robots in assembly cells can significantly increase productivity and improve the well-being of workers once the distribution of tasks and resources is optimised. This paper proposes a new integrated task allocation model to better utilise human-robot collaboration to increase productivity and improve worker well-being. The developed model enables the skills of both workers and robots to be fully utilised while ensuring economic efficiency and the effective protection of workers' physiological and psychological health. First, the product assembly process is decomposed into several assembly tasks, and the characteristics of each task are analysed. Second, a bi-objective mixed-integer planning model is developed with the objectives of minimising unit product assembly time and maximising total task matching. The ergonomics-related objectives are considered in terms of both the physiological and psychological fatigue of the worker, and relevant constraints are established. An improved NSGA-II algorithm is developed to determine the final task allocation scheme. Finally, the proposed method is applied to a real industrial case to verify the effectiveness of the approach. [ABSTRACT FROM AUTHOR]
- Subjects :
- ERGONOMICS
INDUSTRIAL robots
ECONOMIC efficiency
FATIGUE (Physiology)
WELL-being
Subjects
Details
- Language :
- English
- ISSN :
- 00207543
- Volume :
- 61
- Issue :
- 21
- Database :
- Complementary Index
- Journal :
- International Journal of Production Research
- Publication Type :
- Academic Journal
- Accession number :
- 171952285
- Full Text :
- https://doi.org/10.1080/00207543.2022.2147234