TY - JOUR
T1 - Reinforcement learning-based selective disassembly sequence planning for the end-of-life products with structure uncertainty
AU - Zhao, Xikun
AU - Li, Congbo
AU - Tang, Ying
AU - Cui, Jiabin
N1 - Funding Information:
Manuscript received February 25, 2021; accepted June 20, 2021. Date of publication July 20, 2021; date of current version August 20, 2021. This letter was recommended for publication by Associate Editor N. Kong and Editor J. Yi upon evaluation of the reviewers’ comments. This work was supported in part by the National Natural Science Foundation of China under Grant 51975075 and in part by the National Key R&D Program of China under Grant 2019YFB1706103. (Corresponding author: Congbo Li.) Xikun Zhao, Congbo Li, and Jiabin Cui are with the State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China (e-mail: xikunzhao@163.com; congboli@cqu.edu.cn; cuijiabin@cqu.edu.cn).
Publisher Copyright:
© 2016 IEEE.
PY - 2021/10
Y1 - 2021/10
N2 - Selective disassembly sequence planning (SDSP) is regarded as an efficient strategy to determine optimal disassembly sequences for extracting target parts (TP) from complex end-of-life (EOL) products. Previous research assumes that all EOL products have the same structure and the optimal selective disassembly sequences are given before the EOL products are removed. However, the products have different operation states during their use stage, which results in high structure uncertainty of EOL products. The structure uncertainty of EOL products often makes the predetermined selective disassembly sequences impractical for minimizing disassembly time and maximizing disassembly profit. This letter undertakes this challenge by integrated reinforcement learning (RL) to determine the optimal disassembly sequences adaptive to the structure uncertainty of the EOL products. Firstly, a multi-level selective disassembly hybrid graph model (MSDHGM) is developed to illustrate the contact, precedence, and level relationships among parts. Then, the SDSP is formulated as a finite Markov Decision Process and a deep Q-network based selective disassembly sequence planning (DQN-SDSP) is proposed. Finally, extensive comparative experiments are conducted to verify the proposed method compared with NSGA-II and ABC algorithms.
AB - Selective disassembly sequence planning (SDSP) is regarded as an efficient strategy to determine optimal disassembly sequences for extracting target parts (TP) from complex end-of-life (EOL) products. Previous research assumes that all EOL products have the same structure and the optimal selective disassembly sequences are given before the EOL products are removed. However, the products have different operation states during their use stage, which results in high structure uncertainty of EOL products. The structure uncertainty of EOL products often makes the predetermined selective disassembly sequences impractical for minimizing disassembly time and maximizing disassembly profit. This letter undertakes this challenge by integrated reinforcement learning (RL) to determine the optimal disassembly sequences adaptive to the structure uncertainty of the EOL products. Firstly, a multi-level selective disassembly hybrid graph model (MSDHGM) is developed to illustrate the contact, precedence, and level relationships among parts. Then, the SDSP is formulated as a finite Markov Decision Process and a deep Q-network based selective disassembly sequence planning (DQN-SDSP) is proposed. Finally, extensive comparative experiments are conducted to verify the proposed method compared with NSGA-II and ABC algorithms.
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U2 - 10.1109/LRA.2021.3098248
DO - 10.1109/LRA.2021.3098248
M3 - Article
AN - SCOPUS:85111002171
VL - 6
SP - 7807
EP - 7814
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
SN - 2377-3766
IS - 4
M1 - 9492060
ER -