Task allocation strategies considering task matching and ergonomics in the human-robot collaborative hybrid assembly cell

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.

KEYWORDS:

• Human-robot collaboration
• task allocation
• task matching
• physiological fatigue
• psychological fatigue

Acknowledgments

The authors would like to thank the two anonymous reviewers and editor for their insightful comments and suggestions.

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This study was financially supported by the National Natural Science Foundation of China [grant Nos.71831006, 71771070 and 72171064].

Notes on contributors

Min Cai

Min Cai received her Ph.D. degree in mechanical engineering from Zhejiang University, China. She is currently an associate professor in the Institute of Industrial Engineering and Management, Hangzhou Dianzi University, China. Her current research interests include human factors and ergonomics, digital engineering and management.

Rensheng Liang

Rensheng Liang is a graduate student at the School of Management, Hangzhou Dianzi University, China. He is majoring in Management Science and Engineering. His current research interests include scheduling and human-robot collaboration.

Xinggang Luo

Xinggang Luo is a professor at the School of Management, Hangzhou Dianzi University, China. He has authored more than 70 papers on peer-reviewed journals including DSS, IEEE SMC, IEEE EM, EJOR, IJPR, IJPE, KBS, ASC, C&IE. His current research interests include service science and quality management.

Chunlai Liu

Chunlai Liu received his Ph.D. degree in Management Science and Engineering from Dalian University of Technology, China. He is currently a lecturer in the Institute of Industrial Engineering and Management, Hangzhou Dianzi University, China. His current research interests include advanced manufacturing system, healthcare delivery and scheduling. And he has published over 10 academic publications.