| Title |
Deep reinforcement learning-based approach for dynamic disassembly scheduling of end-of-life products with stimuli-activated self-disassembly |
| ID_Doc |
10367 |
| Authors |
Han, MY; Yun, LX; Li, L |
| Title |
Deep reinforcement learning-based approach for dynamic disassembly scheduling of end-of-life products with stimuli-activated self-disassembly |
| Year |
2023 |
| Published |
|
| DOI |
10.1016/j.jclepro.2023.138758 |
| Abstract |
Remanufacturing is one of the most critical strategies for end-of-life product management to promote a circular economy; however, it has been seen very limited implementation due to the labor-intensive and time-consuming disassembly processes for component retrieval. The newly emerged 4D printing technology enables the fabrication of stimuli-responsive reconfigurable structures, outlining new ways to achieve non-destructive and simultaneous self-disassembly of components with different geometry. However, large uncertainties and increased process dynamics have also emerged directly pertaining to the real-time scheduling in disassembly lines with self-disassembly workstations, which the existing scheduling methods are not equipped to handle. In this study, a constrained multi-agent deep reinforcement learning approach is proposed to maximize the disassembly profit by dynamically changing the batch mixing ratios of different-sized components in selfdisassembly workstations and adapting real-time scheduling to stochastic product quality, changes in operational sequences, and self-disassembly failures. The proposed approach is validated on a disassembly line for hand pulse detectors that contain heat-activated self-disassembly components. Numerical results show that the proposed achieves stable convergence under uncertainties, and the implementation of a dynamic batch mixing scheme in self-disassembly operations yields a substantial improvement in disassembly profit over the scheduling period. In addition, sensitivity analyses are conducted to evaluate the impacts of system uncertainties on the profitability of the disassembly line. |
| Author Keywords |
End-of-life management; Dynamic scheduling; Multi-agent deep reinforcement learning; Stimuli-activated self-disassembly |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001076866800001 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
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