Dynamic Scheduling Implementation of Reverse Logistics Network Simulation Based on Deep Reinforcement Learning
Main Article Content
Abstract
Driven by the dual momentum of the circular economy and digital transformation, reverse logistics serves as a critical nexus for resource regeneration while facing volatile recycling volumes, fragmented collection networks, and the intricate sorting constraints inherent to fiber recovery. With the growing demand for intelligent information interaction and data transmission in modern industrial systems, including electromagnetic-enabled sensing and communication infrastructures, traditional static scheduling modes are increasingly unable to adapt to dynamic operational environments. Deep reinforcement learning (DRL) provides an effective technological pathway for dynamic scheduling of reverse logistics networks through its strong capability for adaptive decision-making and environment-aware optimization. Centering on the integrated framework of simulation modeling, algorithm optimization, dynamic scheduling, and empirical verification, this study employs deep reinforcement learning to optimize the dynamic scheduling of reverse logistics networks, specifically addressing the nonlinear characteristics of fiber feedstock recovery and multi-grade material flows. The proposed approach enhances scheduling adaptability and resource coordination efficiency while offering a practical reference for intelligent logistics management and information-driven optimization in advanced industrial communication environments.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
T. François-Alexandre, D. Audrey, M. Michael, P. Marier, and J. Gaudreault, “Deep reinforcement learning for continuous wood drying production line control,” Computers in Industry, pp. 154, 2024, doi: 10.1016/J.COMPIND.2023.104036.
X. Wang, H. Zhong, G. Zhang, G. Ruan, Y. He, and Z. Yu, “Adaptive look-ahead economic dispatch based on deep reinforcement learning,” Applied Energy, vol. 353, no. PB, Art. no. 122121, 2024, doi: 10.1016/J.APENERGY.2023.122121.
X. Deng, Y. Zhang, Y. Jiang, Y. Zhang, and H. Qi, “A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning,” Applied Energy, vol. 353, no. PB, Art. no. 122188, 2024, doi: 10.1016/J.APENERGY.2023.122188.
J. Peng, Y. Shen, C. Wu, C. Wang, F. Yi, and C. Ma, “Research on energy-saving driving control of hydrogen fuel bus based on deep reinforcement learning in freeway ramp weaving area,” Energy, pp. 285, 2023, doi: 10.1016/J.ENERGY.2023.129449.
Z. Li, C. Xu, Z. Zhang, and R. Wu, “Deep reinforcement learning based trajectory design and resource allocation for task-aware multi-UAV enabled MEC networks,” Computer Communications, vol. 213, pp. 88-98, 2024, doi: 10.1016/J.COMCOM.2023.11.006.
S. Mate, P. Pal, A. Jaiswal, and S. Bhartiya, “Simultaneous tuning of multiple PID controllers for multivariable systems using deep reinforcement learning,” Digital Chemical Engineering, vol. 9, Art. no. 100131, 2023, doi: 10.1016/J.DCHE.2023.100131.
H. Chen, M. C. Kim, Y. Ko, and C. S. Kim, “Compensated Motion and Position Estimation of a Cable-driven Parallel Robot Based on Deep Reinforcement Learning,” International Journal of Control, Automation and Systems, vol. 21, no. 11, pp. 3507-3518, 2023, doi: 10.1007/S12555-023-0342-6.
J. Hu, X. Li, W. Hu, Q. Xu, and Y. Hu, “Decision-making for Connected and Automated Vehicles in Chanllenging Traffic Conditions Using Imitation and Deep Reinforcement Learning,” International Journal of Automotive Technology, vol. 24, no. 6, pp. 1589-1602, 2023, doi: 10.1007/S12239-023-0128-0.
A. Mughees, M. Tahir, M. A. Sheikh, A. Amphawan, Y. K. Meng, A. Ahad, et al., “Energy-efficient joint resource allocation in 5G HetNet using Multi-Agent Parameterized Deep Reinforcement learning,” Physical Communication, vol. 61, Art. no. 102206, 2023, doi: 10.1016/J.PHYCOM.2023.102206.
A. Hussain and P. Musilek, “Energy management of buildings with energy storage and solar photovoltaic: A diversity in experience approach for deep reinforcement learning agents,” Energy and AI, vol. 15, Art. no. 100313, 2024, doi: 10.1016/J.EGYAI.2023.100313.
Z. Guan, Z. Wang, Y. Cai, and X. Wang, “Deep reinforcement learning based efficient access scheduling algorithm with an adaptive number of devices for federated learning IoT systems,” Internet of Things, vol. 24, Art. no. 100980, 2023, doi: 10.1016/J.IOT.2023.100980.
A. Gao, S. Lu, R. Xu, Z. Li, B. Wang, S. Zhu, et al., “Deep reinforcement learning based planning method in state space for lunar rovers,” Engineering Applications of Artificial Intelligence, vol. 127, no. PB, Art. no. 107287, 2024, doi: 10.1016/J.ENGAPPAI.2023.107287.
Z. Wang, M. Goudarzi, M. Gong, and R. Buyya, “Deep Reinforcement Learning-based scheduling for optimizing system load and response time in edge and fog computing environments,” Future Generation Computer Systems, vol. 152, pp. 55-69, 2024, doi: 10.1016/J.FUTURE.2023.10.012.
M. E. Najafabadi and F. Haghighat, “Transfer learning for occupancy-based HVAC control: A data-driven approach using unsupervised learning of occupancy profiles and deep reinforcement learning,” Energy and Buildings, vol. 300, Art. no. 113637, 2023, doi: 10.1016/J.ENBUILD.2023.113637.
C. Liu, Z. Sheng, S. Chen, H. Shi, and B. Ran, “Longitudinal control of connected and automated vehicles among signalized intersections in mixed traffic flow with deep reinforcement learning approach,” Physica A: Statistical Mechanics and its Applications, vol. 629, Art. no. 129189, 2023, doi: 10.1016/J.PHYSA.2023.129189.
J. Wu and D. Li, “Modeling and maximizing information diffusion over hypergraphs based on deep reinforcement learning,” Physica A: Statistical Mechanics and its Applications, vol. 629, Art. no. 129193, 2023, doi: 10.1016/J.PHYSA.2023.129193.
I. Kim, S. Kim, and D. You, “Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning,” Computer Physics Communications, vol. 294, Art. no. 108962, 2024, doi: 10.1016/J.CPC.2023.108962.
J. Deng, M. Eklund, S. Sierla, J. Savolainen, H. Niemistö, T. Karhela, et al., “Deep reinforcement learning for fuel cost optimization in district heating,” Sustainable Cities and Society, vol. 99, Art. no. 104955, 2023, doi: 10.1016/J.SCS.2023.104955.
H. Hou, S. N. A. Jawaddi, and A. Ismail, “Energy efficient task scheduling based on deep reinforcement learning in cloud environment: A specialized review,” Future Generation Computer Systems, vol. 151, pp. 214-231, 2024, doi: 10.1016/J.FUTURE.2023.10.002.
R. H. Randhawa, N. Aslam, M. Alauthman, M. Khalid, and H. Rafiq, “Deep reinforcement learning based Evasion Generative Adversarial Network for botnet detection,” Future Generation Computer Systems, vol. 150, pp. 294-302, 2024, doi: 10.1016/J.FUTURE.2023.09.011.
Z. Wang, H. Huang, J. Tang, and L. Hu, “A deep reinforcement learning-based approach for autonomous lanechanging velocity control in mixed flow of vehicle group level,” Expert Systems With Applications, vol. 238, no. PD, Art. no. 122158, 2024, doi: 10.1016/J.ESWA.2023.122158.
J. Liu, Z. Zhou, W. Hong, and J. Shi, “Two-dimensional iterative learning control with deep reinforcement learning compensation for the non-repetitive uncertain batch processes,” Journal of Process Control, vol. 131, Art. no. 103106, 2023, doi: 10.1016/J.JPROCONT.2023.103106.
H. Yuan, D. Li, and J. Wang, “Integrated robust navigation and guidance for the kinetic impact of near-earth asteroids based on deep reinforcement learning,” Aerospace Science and Technology, vol. 142, no. PB, Art. no. 108666, 2023, doi: 10.1016/J.AST.2023.108666.
P. Chen and W. Fan, “Identifying critical nodes via link equations and deep reinforcement learning,” Neurocomputing, vol. 562, Art. no. 126871, 2023, doi: 10.1016/J.NEUCOM.2023.126871.
A. Alferidi, M. Alsolami, B. Lami, and S. Bensiama, “Design and implementation of an indoor environment management system using a deep reinforcement learning approach,” Ain Shams Engineering Journal, vol. 14, no. 11, Art. no. 102534, 2023, doi: 10.1016/J.ASEJ.2023.102534.
A. Mattioni, S. Zoboli, B. Mavkov, D. Astolfi, V. Andrieu, E. Witrant, et al., “Enhancing deep reinforcement learning with integral action to control tokamak safety factor,” Fusion Engineering and Design, vol. 196, Art. no. 114008, 2023, doi: 10.1016/J.FUSENGDES.2023.114008.
S. Agrawal, S. Dubey, and K. J. Naik, “Deep reinforcement learning for forecasting fish survival in open aquaculture ecosystem,” Environmental Monitoring and Assessment, vol. 195, no. 11, pp. 1389, 2023, doi: 10.1007/S10661-023-11937-9.
J. Xue, F. Han, B. K. V. Oorschot, G. Clifton, and D. Fan, “Exploring storm petrel pattering and sea-anchoring using deep reinforcement learning,” Bioinspiration & biomimetics, vol. 18, no. 6, Art. no. 066016, 2023, doi: 10.1088/1748-3190/AD00A2.
K. Guo, R. Liu, G. Duan, J. Liu, and P. Cao, “Research on dynamic decision-making for product assembly sequence based on Connector-Linked Model and deep reinforcement learning,” Journal of Manufacturing Systems, vol. 71, pp. 451-473, 2023, doi: 10.1016/J.JMSY.2023.09.015.