TY - GEN
T1 - Discrete Fruit Fly Optimization Algorithm for Disassembly Line Balancing Problems by Considering Human Worker's Learning Effect
AU - Wang, Jianping
AU - Guo, Xiwang
AU - Zhou, Mengchu
AU - Wang, Jiacun
AU - Qin, Shujin
AU - Qi, Liang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The recycling of discarded products is an integral part of resource utilization. A disassembly line balancing problem (DLBP) concerns the recycling and remanufacturing process of end-of-life (EOL) products. Disassembly time is affected by many factors, e.g., the precedence relationships among disassembly tasks, and the skills and learning abilities of disassembly workers. In this paper, we use Petri nets to specify a disassembly process and establish a mixed integer programming model to describe DLBP that considers the learning effect of human workers. We then propose a discrete fruit fly optimization algorithm to solve the proposed problem. By comparing its experimental results with other intelligent optimization algorithms', its efficiency is verified.
AB - The recycling of discarded products is an integral part of resource utilization. A disassembly line balancing problem (DLBP) concerns the recycling and remanufacturing process of end-of-life (EOL) products. Disassembly time is affected by many factors, e.g., the precedence relationships among disassembly tasks, and the skills and learning abilities of disassembly workers. In this paper, we use Petri nets to specify a disassembly process and establish a mixed integer programming model to describe DLBP that considers the learning effect of human workers. We then propose a discrete fruit fly optimization algorithm to solve the proposed problem. By comparing its experimental results with other intelligent optimization algorithms', its efficiency is verified.
KW - Petri nets
KW - disassembly line balancing
KW - discrete fruit fly optimization algorithm
KW - learning effect
UR - http://www.scopus.com/inward/record.url?scp=85144634945&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144634945&partnerID=8YFLogxK
U2 - 10.1109/ANZCC56036.2022.9966961
DO - 10.1109/ANZCC56036.2022.9966961
M3 - Conference contribution
AN - SCOPUS:85144634945
T3 - 2022 Australian and New Zealand Control Conference, ANZCC 2022
SP - 201
EP - 206
BT - 2022 Australian and New Zealand Control Conference, ANZCC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Australian and New Zealand Control Conference, ANZCC 2022
Y2 - 24 November 2022 through 25 November 2022
ER -