Dynamic Balancing of U-Shaped Robotic Disassembly Lines Using an Effective Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Dynamic Balancing of U-Shaped Robotic Disassembly Lines Using an Effective Deep Reinforcement Learning Approach

Published:2024-04 Issue:4 Volume:20 Page:6855-6865
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Kaipu¹^ORCID,Li Yibing¹^ORCID,Guo Jun¹^ORCID,Gao Liang²^ORCID,Li Xinyu²^ORCID

Affiliation:

1. Hubei Digital Manufacturing Key Laboratory, School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China

2. State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Funder

National Natural Science Foundation of China

Hubei Provincial Natural Science Foundation of China

Knowledge Innovation Program of Wuhan-Shuguang Project

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10492621/10414258.pdf?arnumber=10414258

Reference39 articles.

1. EMOGA: A Hybrid Genetic Algorithm With Extremal Optimization Core for Multiobjective Disassembly Line Balancing

2. Flexible Process Planning and End-of-Life Decision-Making for Product Recovery Optimization Based on Hybrid Disassembly

3. Model review and algorithm comparison on multi-objective disassembly line balancing

4. Human-robot collaborative disassembly line balancing considering the safe strategy in remanufacturing

5. An improved multi-objective discrete bees algorithm for robotic disassembly line balancing problem in remanufacturing

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