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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:

Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network
Yuxin Li, Wenbin Gu, Minghai Yuan, et al.
Robotics and Computer-Integrated Manufacturing (2021) Vol. 74, pp. 102283-102283
Closed Access | Times Cited: 139

Showing 1-25 of 139 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
Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems
Yi Zhang, Haihua Zhu, Dunbing Tang, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 78, pp. 102412-102412
Closed Access | Times Cited: 129
Flexible job shop scheduling problem under Industry 5.0: A survey on human reintegration, environmental consideration and resilience improvement
Candice Destouet, Houda Tlahig, Belgacem Bettayeb, et al.
Journal of Manufacturing Systems (2023) Vol. 67, pp. 155-173
Open Access | Times Cited: 103
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
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: 59
A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 135, pp. 108789-108789
Closed Access | Times Cited: 55
A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV
Xiaoqing Han, Weiyao Cheng, Leilei Meng, et al.
Swarm and Evolutionary Computation (2024) Vol. 86, pp. 101538-101538
Closed Access | Times Cited: 25
Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV
Fayong Zhang, Rui Li, Wenyin Gong
Computers & Industrial Engineering (2024) Vol. 189, pp. 109917-109917
Closed Access | Times Cited: 23
A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations
Zhengpei Zhang, Yaping Fu, Kaizhou Gao, et al.
Computers & Industrial Engineering (2024) Vol. 196, pp. 110484-110484
Closed Access | Times Cited: 20
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: 15
Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling
Linshan Ding, Zailin Guan, Mudassar Rauf, et al.
Swarm and Evolutionary Computation (2024) Vol. 87, pp. 101550-101550
Closed Access | Times Cited: 15
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
Multi-agent deep reinforcement learning based Predictive Maintenance on parallel machines
Marcelo Luis Ruiz Rodríguez, Sylvain Kubler, Andrea de Giorgio, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 78, pp. 102406-102406
Open Access | Times Cited: 52
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
Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window
Jin Wang, Yang Liu, Shan Ren, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 79, pp. 102435-102435
Open Access | Times Cited: 46
A Pareto-based two-stage evolutionary algorithm for flexible job shop scheduling problem with worker cooperation flexibility
Qiang Luo, Qianwang Deng, Guanhua Xie, et al.
Robotics and Computer-Integrated Manufacturing (2023) Vol. 82, pp. 102534-102534
Closed Access | Times Cited: 36
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
Dynamic scheduling for flexible job shop with insufficient transportation resources via graph neural network and deep reinforcement learning
Min Zhang, Liang Wang, Fusheng Qiu, et al.
Computers & Industrial Engineering (2023) Vol. 186, pp. 109718-109718
Closed Access | Times Cited: 35
A DRL-Based Reactive Scheduling Policy for Flexible Job Shops With Random Job Arrivals
Linlin Zhao, Jiaxin Fan, Chunjiang Zhang, et al.
IEEE Transactions on Automation Science and Engineering (2023) Vol. 21, Iss. 3, pp. 2912-2923
Closed Access | Times Cited: 32
An Improved Genetic Algorithm for Solving the Multi-AGV Flexible Job Shop Scheduling Problem
Leilei Meng, Weiyao Cheng, Biao Zhang, et al.
Sensors (2023) Vol. 23, Iss. 8, pp. 3815-3815
Open Access | Times Cited: 31
Leveraging digital twin into dynamic production scheduling: A review
Nada Ouahabi, Ahmed Chebak, Oulaïd Kamach, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 89, pp. 102778-102778
Closed 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: 13
Multi-objective adaptive large neighbourhood search algorithm for dynamic flexible job shop schedule problem with transportation resource
Jiaojiao Liu, Baofeng Sun, Gendao Li, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107917-107917
Closed Access | Times Cited: 11
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: 11
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