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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:

A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem
Renke Liu, Rajesh Piplani, Carlos Toro
Computers & Operations Research (2023) Vol. 159, pp. 106294-106294
Closed Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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
Dynamic job-shop scheduling using graph reinforcement learning with auxiliary strategy
Zhenyu Liu, Haoyang Mao, Guodong Sa, et al.
Journal of Manufacturing Systems (2024) Vol. 73, pp. 1-18
Closed Access | Times Cited: 7
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: 5
Exploring multi-agent reinforcement learning for unrelated parallel machine scheduling
M. Zampella, Urtzi Otamendi, Xabier Belaunzaran, et al.
The Journal of Supercomputing (2025) Vol. 81, Iss. 4
Closed Access
A novel hybrid intelligent scheduling: integrating human feedback into reinforcement learning for adaptive preference objectives
Chen Ding, Fei Qiao, Dongyuan Wang, et al.
International Journal of Production Research (2025), pp. 1-19
Closed Access
A deep multi-agent reinforcement learning approach for the micro-service migration problem with affinity in the cloud
Ning Ma, A. H. Tang, Zifeng Xiong, et al.
Expert Systems with Applications (2025) Vol. 273, pp. 126856-126856
Closed Access
A Review of Scheduling Methods for Multi-AGV Material Handling Systems in Mixed-Model Assembly Workshops
Tianyuan Mao
Frontiers in Sustainable Development (2025) Vol. 5, Iss. 3, pp. 227-237
Closed Access
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: 4
Proposing a model based on deep reinforcement learning for real-time scheduling of collaborative customization remanufacturing
Seyed Ali Yazdanparast, Seyed Hessameddin Zegordi, Toktam Khatibi
Robotics and Computer-Integrated Manufacturing (2025) Vol. 94, pp. 102980-102980
Closed Access
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
Research on dynamic job shop scheduling problem with AGV based on DQN
Zhengfeng Li, Wengpeng Gu, Huichao Shang, et al.
Cluster Computing (2025) Vol. 28, Iss. 4
Closed Access
A heuristic-assisted deep reinforcement learning algorithm for flexible job shop scheduling with transport constraints
Xiaoting Dong, Guangxi Wan, Peng Zeng
Complex & Intelligent Systems (2025) Vol. 11, Iss. 5
Open Access
Dynamic flexible flow shop scheduling via cross-attention networks and multi-agent reinforcement learning
Jinlong Zheng, Yixin Zhao, Yinya Li, et al.
Journal of Manufacturing Systems (2025) Vol. 80, pp. 395-411
Closed Access
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
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
Deep reinforcement learning driven trajectory-based meta-heuristic for distributed heterogeneous flexible job shop scheduling problem
Qichen Zhang, Weishi Shao, Zhongshi Shao, et al.
Swarm and Evolutionary Computation (2024) Vol. 91, pp. 101753-101753
Closed Access | Times Cited: 2
A logic Petri net model for dynamic multi-agent game decision-making
Haewon Byeon, Chintureena Thingom, Ismail Keshta, et al.
Decision Analytics Journal (2023) Vol. 9, pp. 100320-100320
Open Access | Times Cited: 4
Tugboat Scheduling Method Based on the NRPER-DDPG Algorithm: An Integrated DDPG Algorithm with Prioritized Experience Replay and Noise Reduction
J. Li, Xingfeng Duan, Z. Xiong, et al.
Sustainability (2024) Vol. 16, Iss. 8, pp. 3379-3379
Open Access | Times Cited: 1
A new EDA algorithm combined with Q-learning for semiconductor final testing scheduling problem
Long Zhang, Lin Yi, Chuanpei Xu, et al.
Computers & Industrial Engineering (2024) Vol. 193, pp. 110259-110259
Closed Access | Times Cited: 1
A review of reinforcement learning based hyper-heuristics
Cui-Xia Li, Xiang Wei, Jing Wang, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e2141-e2141
Open Access | Times Cited: 1
Study on the application of single-agent and multi-agent reinforcement learning to dynamic scheduling in manufacturing environments with growing complexity: Case study on the synthesis of an industrial IoT Test Bed
H. A. David, Fouad Bahrpeyma, Dirk Reichelt
Journal of Manufacturing Systems (2024) Vol. 77, pp. 525-557
Open Access | Times Cited: 1
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: 1
Solving Resource Allocation and Production Scheduling Problems in Aerospace Industry Using Double Deep Q-Network with State Feature Images
Bing Yang
(2024)
Closed Access
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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