journal article Oct 01, 2025

Twin Delayed Deep Deterministic Policy Gradient Algorithm for a Heterogeneous Multifactory Remanufacturing Optimization Problem

View at Publisher Save 10.1109/tcss.2025.3540263
Topics

No keywords indexed for this article. Browse by subject →

References
39
[15]
Yao "Invasive weed optimization algorithm for solving multi-objective sequence-dependent U-shaped disassembly line balancing problem" Int. J. Res. Eng. Sci. (2022)
[25]
Multiresource-Constrained Selective Disassembly With Maximal Profit and Minimal Energy Consumption

Xiwang Guo, MengChu Zhou, Shixin Liu et al.

IEEE Transactions on Automation Science and Engine... 10.1109/tase.2020.2992220
[28]
Deep Reinforcement Learning for Autonomous Driving: A Survey

B Ravi Kiran, Ibrahim Sobh, Victor Talpaert et al.

IEEE Transactions on Intelligent Transportation Sy... 10.1109/tits.2021.3054625
[32]
Fujimoto "Addressing function approximation error in actor-critic methods" (2018)
[33]
Deep deterministic policy gradient algorithm: A systematic review

Ebrahim Hamid Sumiea, Said Jadid Abdulkadir, Hitham Seddig Alhussian et al.

Heliyon 10.1016/j.heliyon.2024.e30697
[34]
Mnih "Asynchronous methods for deep reinforcement learning" (2016)
[35]
Haarnoja "Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor" (2018)
Metrics
2
Citations
39
References
Details
Published
Oct 01, 2025
Vol/Issue
12(5)
Pages
2864-2875
License
View
Funding
Natural Science Foundation of Shandong Province Award: ZR2024MF140
National Local Joint Engineering Laboratory for Optimization of Petrochemical Process Operation and Energy saving Technology Award: LJ232410148002
Innovation Team Project of the Educational Department of Liaoning Province Award: LJ222410148036
Cite This Article
Liang Qi, Qiqi Zeng, Shixin Liu, et al. (2025). Twin Delayed Deep Deterministic Policy Gradient Algorithm for a Heterogeneous Multifactory Remanufacturing Optimization Problem. IEEE Transactions on Computational Social Systems, 12(5), 2864-2875. https://doi.org/10.1109/tcss.2025.3540263