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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:

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: 40

Showing 1-25 of 40 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: 155
Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system
Kexin Sun, Debin Zheng, Haohao Song, et al.
Expert Systems with Applications (2022) Vol. 215, pp. 119359-119359
Closed Access | Times Cited: 76
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
James Orr, Ayan Dutta
Sensors (2023) Vol. 23, Iss. 7, pp. 3625-3625
Open Access | Times Cited: 61
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
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
Review on ensemble meta-heuristics and reinforcement learning for manufacturing scheduling problems
Yaping Fu, Yifeng Wang, Kaizhou Gao, et al.
Computers & Electrical Engineering (2024) Vol. 120, pp. 109780-109780
Closed Access | Times Cited: 11
Edge AI: A Taxonomy, Systematic Review and Future Directions
Sukhpal Singh Gill, Muhammed Golec, Jianmin Hu, et al.
Cluster Computing (2024) Vol. 28, Iss. 1
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
Multi agent reinforcement learning for online layout planning and scheduling in flexible assembly systems
Lea Kaven, Philipp Huke, Amon Göppert, et al.
Journal of Intelligent Manufacturing (2024)
Open Access | Times Cited: 8
Digital twin-based multi-level task rescheduling for robotic assembly line
Bitao Yao, Wenjun Xu, Tong Shen, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 17
Multi-Task Multi-Agent Reinforcement Learning for Real-Time Scheduling of a Dual-Resource Flexible Job Shop with Robots
Xiaofei Zhu, Jiazhong Xu, Jianghua Ge, et al.
Processes (2023) Vol. 11, Iss. 1, pp. 267-267
Open Access | Times Cited: 16
An intelligent manufacturing system based on a recursive control structure
Bingyan Teng
Frontiers in Mechanical Engineering (2025) Vol. 10
Open Access
Static scheduling method for aircraft flat-tail assembly production based on improved bi-level genetic algorithm
Tengda Li, Min Hua, Junliang Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Solving quay wall allocation problems based on deep reinforcement learning
Young-in Cho, Seung-Heon Oh, J U Choi, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 150, pp. 110598-110598
Closed 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
Hierarchical Reinforcement Learning for Multi-Objective Real-Time Flexible Scheduling in a Smart Shop Floor
Jingru Chang, Dong Yu, Zheng Zhou, et al.
Machines (2022) Vol. 10, Iss. 12, pp. 1195-1195
Open Access | Times Cited: 20
A Survey of AI-enabled Dynamic Manufacturing Scheduling: From Directed Heuristics to Autonomous Learning
Jiepin Ding, Mingsong Chen, Ting Wang, et al.
ACM Computing Surveys (2023) Vol. 55, Iss. 14s, pp. 1-36
Closed Access | Times Cited: 12
Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling
Nianmin Zhang, Yilan Shen, Ye Du, et al.
Journal of Manufacturing Systems (2023) Vol. 71, pp. 70-81
Closed Access | Times Cited: 10
Dynamic flexible job-shop scheduling by multi-agent reinforcement learning with reward-shaping
Lixiang Zhang, Yan Yan, Chen Yang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102872-102872
Closed Access | Times Cited: 3
Learning to schedule dynamic distributed reconfigurable workshops using expected deep Q-network
Shengluo Yang, Junyi Wang, Zhigang Xu
Advanced Engineering Informatics (2023) Vol. 59, pp. 102307-102307
Closed Access | Times Cited: 8
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 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
Research on an Adaptive Real-Time Scheduling Method of Dynamic Job-Shop Based on Reinforcement Learning
Haihua Zhu, Shuai Tao, Yong Gui, et al.
Machines (2022) Vol. 10, Iss. 11, pp. 1078-1078
Open Access | Times Cited: 9
Scheduling and Controlling Production in an Internet of Things Environment for Industry 4.0: An Analysis and Systematic Review of Scientific Metrological Data
Lingye Tan, Tiong Lee Kong, Ziyang Zhang, et al.
Sustainability (2023) Vol. 15, Iss. 9, pp. 7600-7600
Open Access | Times Cited: 4
Towards practicality: Navigating challenges in designing predictive-reactive scheduling
Fabian Erlenbusch, Nicole Stricker
Procedia CIRP (2024) Vol. 122, pp. 701-706
Open Access | Times Cited: 1
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