TY - GEN
T1 - Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Problems
AU - Zhong, Zhaokai
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 - As more and more end-of-life products are accumulated over time, there is an urgent need for their recycling. Disassembly is a key step to do so. In order to improve the operational efficiency of disassembly lines, a disassembly line balance problem (DLBP) has drawn many researchers' attention. There are multiple factors that affect disassembly quality and efficiency, e.g., workstation allocation and disassembly revenue. This work addresses a multi-objective DLBP. We consider three objectives: maximizing the net profit of disassembly, minimizing the maximal gap of working time among workstations, and minimizing the risk of performing dangerous disassembly tasks. An improved proximal policy optimization is proposed for the multi-objective DLBP. Five real-world products are used to test its effectiveness and feasibility. Experimental results verify the strength of the algorithm by comparing it with an Actor-Critic algorithm.
AB - As more and more end-of-life products are accumulated over time, there is an urgent need for their recycling. Disassembly is a key step to do so. In order to improve the operational efficiency of disassembly lines, a disassembly line balance problem (DLBP) has drawn many researchers' attention. There are multiple factors that affect disassembly quality and efficiency, e.g., workstation allocation and disassembly revenue. This work addresses a multi-objective DLBP. We consider three objectives: maximizing the net profit of disassembly, minimizing the maximal gap of working time among workstations, and minimizing the risk of performing dangerous disassembly tasks. An improved proximal policy optimization is proposed for the multi-objective DLBP. Five real-world products are used to test its effectiveness and feasibility. Experimental results verify the strength of the algorithm by comparing it with an Actor-Critic algorithm.
KW - Multi-object disassembly line balancing
KW - disassembly
KW - proximal policy optimization
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85144627336&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144627336&partnerID=8YFLogxK
U2 - 10.1109/ANZCC56036.2022.9966864
DO - 10.1109/ANZCC56036.2022.9966864
M3 - Conference contribution
AN - SCOPUS:85144627336
T3 - 2022 Australian and New Zealand Control Conference, ANZCC 2022
SP - 207
EP - 212
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 -