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OpenAlex Citation Counts

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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:

Dynamic Job-Shop Scheduling Problems Using Graph Neural Network and Deep Reinforcement Learning
Chien‐Liang Liu, Tzu‐Hsuan Huang
IEEE Transactions on Systems Man and Cybernetics Systems (2023) Vol. 53, Iss. 11, pp. 6836-6848
Closed Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

A Hierarchical Multi-Action Deep Reinforcement Learning Method for Dynamic Distributed Job-Shop Scheduling Problem With Job Arrivals
Jiang‐Ping Huang, Liang Gao, Xinyu Li
IEEE Transactions on Automation Science and Engineering (2024) Vol. 22, pp. 2501-2513
Closed Access | Times Cited: 10
A K-means-Teaching Learning based optimization algorithm for parallel machine scheduling problem
Yibing Li, Jie Liu, Lei Wang, et al.
Applied Soft Computing (2024) Vol. 161, pp. 111746-111746
Closed Access | Times Cited: 10
Design patterns of deep reinforcement learning models for job shop scheduling problems
Shiyong Wang, Jiaxian Li, Qingsong Jiao, et al.
Journal of Intelligent Manufacturing (2024)
Closed Access | Times Cited: 7
Deep reinforcement learning for dynamic distributed job shop scheduling problem with transfers
Yong Lei, Qianwang Deng, Mengqi Liao, et al.
Expert Systems with Applications (2024) Vol. 251, pp. 123970-123970
Closed Access | Times Cited: 6
Scheduling techniques for addressing uncertainties in container ports: A systematic literature review
Wenfeng Li, Lei Cai, Lijun He, et al.
Applied Soft Computing (2024) Vol. 162, pp. 111820-111820
Closed Access | Times Cited: 5
Multi-agent deep reinforcement learning for dynamic reconfigurable shop scheduling considering batch processing and worker cooperation
Yuxin Li, Xinyu Li, Liang Gao, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 91, pp. 102834-102834
Closed Access | Times Cited: 5
A collaborative-learning multi-agent reinforcement learning method for distributed hybrid flow shop scheduling problem
Yuanzhu Di, Libao Deng, Lili Zhang
Swarm and Evolutionary Computation (2024) Vol. 91, pp. 101764-101764
Closed Access | Times Cited: 5
Graph neural networks for job shop scheduling problems: A survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen, et al.
Computers & Operations Research (2024), pp. 106914-106914
Open Access | Times Cited: 5
Temporal learning in predictive health management using channel-spatial attention-based deep neural networks
Chien‐Liang Liu, Huan-Ci Su
Advanced Engineering Informatics (2024) Vol. 62, pp. 102604-102604
Closed Access | Times Cited: 4
Real-time scheduling for production-logistics collaborative environment using multi-agent deep reinforcement learning
Yuxin Li, Xinyu Li, Liang Gao
Advanced Engineering Informatics (2025) Vol. 65, pp. 103216-103216
Closed Access
Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop
Biao Zhang, Hongyan Sang, Chao Lu, et al.
Information Sciences (2025), pp. 122023-122023
Closed Access
Deep Reinforcement Learning for Facilitating Human-Robot Interaction in Manufacturing
Nathan Eskue, Márcia Baptista
Springer series in advanced manufacturing (2025), pp. 69-95
Closed Access
Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning
Meng Xu, Yi Mei, Fangfang Zhang, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access
Graph Convolutional Networks for logistics optimization: A survey of scheduling and operational applications
Rahimeh Neamatian Monemi, Shahin Gelareh, Pedro Henrique González, et al.
Transportation Research Part E Logistics and Transportation Review (2025) Vol. 197, pp. 104083-104083
Closed Access
Harnessing heterogeneous graph neural networks for Dynamic Job-Shop Scheduling Problem solutions
Chien‐Liang Liu, P.S. Weng, Chun-Jan Tseng
Computers & Industrial Engineering (2025), pp. 111060-111060
Closed Access
β-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
Hacı İsmail Aslan, Philipp Wiesner, Ping Xiong, et al.
(2025), pp. 168-175
Open Access
A deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem
Youshan Liu, Y F Liu, Weiming Shen
Journal of Manufacturing Systems (2025) Vol. 80, pp. 643-661
Closed Access
Fusion Q-Learning Algorithm for Open Shop Scheduling Problem with AGVs
Xiaoyu Wen, Haobo Zhang, Hao Li, et al.
Mathematics (2024) Vol. 12, Iss. 3, pp. 452-452
Open Access | Times Cited: 2
Flow-Shop Scheduling Problem With Batch Processing Machines via Deep Reinforcement Learning for Industrial Internet of Things
Zihui Luo, Chengling Jiang, Liang Liu, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2024) Vol. 8, Iss. 5, pp. 3518-3533
Closed Access | Times Cited: 2
Deep reinforcement learning-based dynamic scheduling for resilient and sustainable manufacturing: A systematic review
Chao Zhang, Max Juraschek, Christoph Herrmann
Journal of Manufacturing Systems (2024) Vol. 77, pp. 962-989
Open Access | Times Cited: 2
A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem
Jiawei Wu, Yong Liu
Engineering Applications of Artificial Intelligence (2024) Vol. 140, pp. 109688-109688
Closed Access | Times Cited: 2
A literature review of reinforcement learning methods applied to job-shop scheduling problems
Xiehui Zhang, Guangyu Zhu
Computers & Operations Research (2024), pp. 106929-106929
Closed Access | Times Cited: 2
Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning
Hengliang Tang, Jinda Dong
Machines (2024) Vol. 12, Iss. 8, pp. 584-584
Open Access | Times Cited: 1
A reinforcement learning-based approach for solving multi-agent job shop scheduling problem
Zhuoran Dong, Tao Ren, Fang Qi, et al.
International Journal of Production Research (2024), pp. 1-26
Closed Access | Times Cited: 1
Knowledge-Based Effective Dispatch for Job Shop Scheduling
Jiepin Ding, Jun Xia, Yutong Ye, et al.
Journal of Circuits Systems and Computers (2024) Vol. 33, Iss. 15
Closed Access
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