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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:

Deep reinforcement learning for dynamic scheduling of a flexible job shop
Renke Liu, Rajesh Piplani, Carlos Toro
International Journal of Production Research (2022) Vol. 60, Iss. 13, pp. 4049-4069
Closed Access | Times Cited: 130

Showing 1-25 of 130 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: 148
Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling
Fangfang Zhang, Yi Mei, Su Nguyen, et al.
IEEE Transactions on Evolutionary Computation (2023) Vol. 28, Iss. 1, pp. 147-167
Closed Access | Times Cited: 57
Large-Scale Dynamic Scheduling for Flexible Job-Shop With Random Arrivals of New Jobs by Hierarchical Reinforcement Learning
Kun Lei, Peng Guo, Yi Wang, et al.
IEEE Transactions on Industrial Informatics (2023) Vol. 20, Iss. 1, pp. 1007-1018
Closed Access | Times Cited: 47
HGNP: A PCA-based heterogeneous graph neural network for a family distributed flexible job shop
Jiake Li, Junqing Li, Ying Xu
Computers & Industrial Engineering (2025), pp. 110855-110855
Closed Access | Times Cited: 2
Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning
Yuxin Li, Qihao Liu, Xinyu Li, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 179-198
Closed Access | Times Cited: 2
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
A reinforcement learning-Variable neighborhood search method for the capacitated Vehicle Routing Problem
Panagiotis Kalatzantonakis, Angelo Sifaleras, Nikolaos Samaras
Expert Systems with Applications (2022) Vol. 213, pp. 118812-118812
Closed Access | Times Cited: 47
Dynamic distributed flexible job-shop scheduling problem considering operation inspection
Kaikai Zhu, Guiliang Gong, Ningtao Peng, et al.
Expert Systems with Applications (2023) Vol. 224, pp. 119840-119840
Closed Access | Times Cited: 35
Dynamic production scheduling towards self-organizing mass personalization: A multi-agent dueling deep reinforcement learning approach
Zhaojun Qin, Dazzle Johnson, Yuqian Lu
Journal of Manufacturing Systems (2023) Vol. 68, pp. 242-257
Closed Access | Times Cited: 35
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: 35
A novel priority dispatch rule generation method based on graph neural network and reinforcement learning for distributed job-shop scheduling
Jiang‐Ping Huang, Liang Gao, Xinyu Li, et al.
Journal of Manufacturing Systems (2023) Vol. 69, pp. 119-134
Closed Access | Times Cited: 29
Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems
Christopher Turner, John Oyekan
Sustainability (2023) Vol. 15, Iss. 13, pp. 10169-10169
Open Access | Times Cited: 29
Job shop smart manufacturing scheduling by deep reinforcement learning
Julio C. Serrano-Ruiz, Josefa Mula, Raúl Poler
Journal of Industrial Information Integration (2024) Vol. 38, pp. 100582-100582
Open Access | Times Cited: 14
Genetic Programming and Reinforcement Learning on Learning Heuristics for Dynamic Scheduling: A Preliminary Comparison
Meng Xu, Yi Mei, Fangfang Zhang, et al.
IEEE Computational Intelligence Magazine (2024) Vol. 19, Iss. 2, pp. 18-33
Closed Access | Times Cited: 12
Analyzing Industry 4.0 Adoption Enablers for Supply Chain Flexibility: Impacts on Resilience and Sustainability
Ajay Kumar Pandey, Yash Daultani, Saurabh Pratap, et al.
Global Journal of Flexible Systems Management (2024)
Closed Access | Times Cited: 11
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: 9
A Double Deep Q-Network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions
Shaojun Lu, Yongqi Wang, Min Kong, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 133, pp. 108487-108487
Closed Access | Times Cited: 8
An end-to-end deep reinforcement learning method based on graph neural network for distributed job-shop scheduling problem
Jiang‐Ping Huang, Liang Gao, Xinyu Li
Expert Systems with Applications (2023) Vol. 238, pp. 121756-121756
Closed Access | Times Cited: 21
A Survey and Perspective on Industrial Cyber-Physical Systems (ICPS): From ICPS to AI-Augmented ICPS
Jiyeong Chae, Sanghoon Lee, Junhyung Jang, et al.
IEEE Transactions on Industrial Cyber-Physical Systems (2023) Vol. 1, pp. 257-272
Open Access | Times Cited: 20
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
Efficient Multi-Objective Optimization on Dynamic Flexible Job Shop Scheduling Using Deep Reinforcement Learning Approach
Z. H. Wu, Hongbo Fan, Yimeng Sun, et al.
Processes (2023) Vol. 11, Iss. 7, pp. 2018-2018
Open Access | Times Cited: 19
A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling
Yu Du, Junqing Li
International Journal of Production Economics (2023) Vol. 268, pp. 109102-109102
Closed Access | Times Cited: 17
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
Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network
Lanjun Wan, Long Fu, Changyun Li, et al.
Knowledge-Based Systems (2024) Vol. 296, pp. 111940-111940
Closed Access | Times Cited: 6
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: 5
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