Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Solving job scheduling problems in a resource preemption environment with multi-agent reinforcement learning
Xiaohan Wang, Zhang Li, Ting-Yu Lin, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 77, pp. 102324-102324
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Deep reinforcement learning in smart manufacturing: A review and prospects
Chengxi Li, Pai Zheng, Yue Yin, et al.
CIRP journal of manufacturing science and technology (2022) Vol. 40, pp. 75-101
Closed Access | Times Cited: 151
DeepMAG: Deep reinforcement learning with multi-agent graphs for flexible job shop scheduling
Jia-Dong Zhang, Zhixiang He, Wing-Ho Chan, et al.
Knowledge-Based Systems (2022) Vol. 259, pp. 110083-110083
Closed Access | Times Cited: 56
Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning
Shengluo Yang, Junyi Wang, Zhigang Xu
Advanced Engineering Informatics (2022) Vol. 54, pp. 101776-101776
Closed Access | Times Cited: 49
High-accuracy prediction and compensation of industrial robot stiffness deformation
Congcong Ye, Jixiang Yang, Han Ding
International Journal of Mechanical Sciences (2022) Vol. 233, pp. 107638-107638
Closed Access | Times Cited: 42
Multi-Agent Reinforcement Learning for Real-Time Dynamic Production Scheduling in a Robot Assembly Cell
Dazzle Johnson, Gang Chen, Yuqian Lu
IEEE Robotics and Automation Letters (2022) Vol. 7, Iss. 3, pp. 7684-7691
Closed Access | Times Cited: 39
Integration of deep reinforcement learning and multi-agent system for dynamic scheduling of re-entrant hybrid flow shop considering worker fatigue and skill levels
Youshan Liu, Jiaxin Fan, Linlin Zhao, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 84, pp. 102605-102605
Closed Access | Times Cited: 36
Cloud–edge collaboration task scheduling in cloud manufacturing: An attention-based deep reinforcement learning approach
Zhen Chen, Zhang Li, Xiaohan Wang, et al.
Computers & Industrial Engineering (2023) Vol. 177, pp. 109053-109053
Closed Access | Times Cited: 32
Smart scheduling of dynamic job shop based on discrete event simulation and deep reinforcement learning
Ziqing Wang, Wenzhu Liao
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 6, pp. 2593-2610
Closed Access | Times Cited: 19
Logistics-involved task scheduling in cloud manufacturing with offline deep reinforcement learning
Xiaohan Wang, Zhang Li, Yongkui Liu, et al.
Journal of Industrial Information Integration (2023) Vol. 34, pp. 100471-100471
Closed Access | Times Cited: 16
A cooperative hierarchical deep reinforcement learning based multi-agent method for distributed job shop scheduling problem with random job arrivals
Jiang‐Ping Huang, Liang Gao, Xinyu Li, et al.
Computers & Industrial Engineering (2023) Vol. 185, pp. 109650-109650
Closed Access | Times Cited: 16
Optimizing Priority Sequencing Rules in Parallel Machine Scheduling: An Evaluation and Selection Approach using Hybrid MCDM Techniques
Ibrahim Badi, Mouhamed Bayane Bouraima, Yanjun Qiu, et al.
Decision Making Advances (2024) Vol. 2, Iss. 1, pp. 22-31
Open Access | Times Cited: 6
Collaborative dynamic scheduling in a self-organizing manufacturing system using multi-agent reinforcement learning
Yong Gui, Zequn Zhang, Dunbing Tang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102646-102646
Closed Access | Times Cited: 6
Solving task scheduling problems in cloud manufacturing via attention mechanism and deep reinforcement learning
Xiaohan Wang, Zhang Li, Yongkui Liu, et al.
Journal of Manufacturing Systems (2022) Vol. 65, pp. 452-468
Closed Access | Times Cited: 24
A multi-agent double Deep-Q-network based on state machine and event stream for flexible job shop scheduling problem
Minghai Yuan, Hanyu Huang, Zichen Li, et al.
Advanced Engineering Informatics (2023) Vol. 58, pp. 102230-102230
Closed Access | Times Cited: 14
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, et al.
Computers & Industrial Engineering (2025), pp. 110856-110856
Open Access
Structural entropy-based scheduler for job planning problems using multi-agent reinforcement learning
Lixin Liang, Shuo Sun, Zhifeng Hao, et al.
International Journal of Machine Learning and Cybernetics (2025)
Closed Access
Dynamic multi-tour order picking in an automotive-part warehouse based on attention-aware deep reinforcement learning
Xiaohan Wang, Zhang Li, Lihui Wang, et al.
Robotics and Computer-Integrated Manufacturing (2025) Vol. 94, pp. 102959-102959
Closed Access
Multi-Agent Deep Q-Network with Layer-Based Communication Channel for Autonomous Internal Logistics Vehicle Scheduling in Smart Manufacturing
Mohammad Feizabadi, Arman Hosseini, Zakaria Yahouni
Communications in computer and information science (2025), pp. 3-22
Closed Access
Solving Action Semantic Conflict in Physically Heterogeneous Multi-Agent Reinforcement Learning with Generalized Action-Prediction Optimization
Xiaoyang Yu, Youfang Lin, Shuo Wang, et al.
Applied Sciences (2025) Vol. 15, Iss. 5, pp. 2580-2580
Open Access
Coordinating dynamic signage for evacuation guidance: A multi-agent reinforcement learning approach integrating mesoscopic crowd modeling and fire propagation
C. Xie, Q. H. Chen, Bin Zhu, et al.
Chaos Solitons & Fractals (2025) Vol. 194, pp. 116246-116246
Closed Access
An improved NSGA-II algorithm based on reinforcement learning for aircraft moving assembly line integration optimization problem
Xiaoyu Wen, Xinyu Zhang, Hao Li, et al.
Swarm and Evolutionary Computation (2025) Vol. 94, pp. 101911-101911
Closed Access
An Integrated Intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations
Gang Peng, Qiang Gao, Xingyu Liu, et al.
Journal of Field Robotics (2025)
Closed Access
Dynamic scheduling for cloud manufacturing with uncertain events by hierarchical reinforcement learning and attention mechanism
Jianxiong Zhang, Yuming Jiang, Bing Guo, et al.
Knowledge-Based Systems (2025), pp. 113335-113335
Closed Access
A Reinforcement Learning‐Based AGV Scheduling for Automated Container Terminals With Resilient Charging Strategies
Shaorui Zhou, Yanyao Yu, Min Zhao, et al.
IET Intelligent Transport Systems (2025) Vol. 19, Iss. 1
Open Access
A review of the applications of multi-agent reinforcement learning in smart factories
Fouad Bahrpeyma, Dirk Reichelt
Frontiers in Robotics and AI (2022) Vol. 9
Open Access | Times Cited: 21
Page 1 - Next Page

Scroll to top