TY - GEN
T1 - Grey Wolf Algorithm for Human-Robot Collaborative Disassembly Line Balancing Problem Subject to Dangerous Components
AU - Li, Chong
AU - Guo, Xi Wang
AU - Wang, Jiacun
AU - Qin, Shujin
AU - Qi, Liang
AU - Tang, Ying
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Disassembly is a critical remanufacturing process to obtain reusable components from discarded products. Due to the limitation of disassembly by humans or robots alone, the human-robot collaborative disassembly method is used to obtain components. Three types of components are considered in this paper: dangerous, delicate and normal. Robots disassemble dangerous components, humans disassemble delicate components, and both humans and robots can disassemble normal components. A mathematical model that maximizes disassembly profit is established. An improved gray wolf optimizer algorithm to solve the single-product disassembly line balancing problem is proposed. The algorithm is compared with the migratory bird optimization algorithm and the brain storming optimization algorithm to test its performance. Experimental results show that the proposed algorithm has a faster convergence speed.
AB - Disassembly is a critical remanufacturing process to obtain reusable components from discarded products. Due to the limitation of disassembly by humans or robots alone, the human-robot collaborative disassembly method is used to obtain components. Three types of components are considered in this paper: dangerous, delicate and normal. Robots disassemble dangerous components, humans disassemble delicate components, and both humans and robots can disassemble normal components. A mathematical model that maximizes disassembly profit is established. An improved gray wolf optimizer algorithm to solve the single-product disassembly line balancing problem is proposed. The algorithm is compared with the migratory bird optimization algorithm and the brain storming optimization algorithm to test its performance. Experimental results show that the proposed algorithm has a faster convergence speed.
UR - http://www.scopus.com/inward/record.url?scp=85146926158&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146926158&partnerID=8YFLogxK
U2 - 10.1109/ICNSC55942.2022.10004166
DO - 10.1109/ICNSC55942.2022.10004166
M3 - Conference contribution
AN - SCOPUS:85146926158
T3 - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control: Autonomous Intelligent Systems
BT - ICNSC 2022 - Proceedings of 2022 IEEE International Conference on Networking, Sensing and Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022
Y2 - 15 December 2022 through 18 December 2022
ER -