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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 26-50 of 139 citing articles:

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
A Q-Learning based NSGA-II for dynamic flexible job shop scheduling with limited transportation resources
Rensheng Chen, Bin Wu, Hua Wang, et al.
Swarm and Evolutionary Computation (2024) Vol. 90, pp. 101658-101658
Closed Access | Times Cited: 11
Dynamic scheduling method for integrated process planning and scheduling problem with machine fault
Xiaoyu Wen, Xiaonan Lian, Yunjie Qian, et al.
Robotics and Computer-Integrated Manufacturing (2022) Vol. 77, pp. 102334-102334
Closed Access | Times Cited: 36
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
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: 18
Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources
Youjie Yao, Lin Gui, Xinyu Li, et al.
Robotics and Computer-Integrated Manufacturing (2024) Vol. 89, pp. 102782-102782
Closed Access | Times Cited: 7
Knowledge-based multi-objective evolutionary algorithm for energy-efficient flexible job shop scheduling with mobile robot transportation
Youjie Yao, Qingzheng Wang, Cuiyu Wang, et al.
Advanced Engineering Informatics (2024) Vol. 62, pp. 102647-102647
Closed Access | Times Cited: 7
Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture
Wenbin Gu, Siqi Liu, Zhenyang Guo, et al.
Computers & Industrial Engineering (2024) Vol. 191, pp. 110155-110155
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: 6
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: 6
Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review
João M. R. C. Fernandes, Seyed Mahdi Homayouni, Dalila B.M.M. Fontes
Sustainability (2022) Vol. 14, Iss. 10, pp. 6264-6264
Open Access | Times Cited: 25
Deep reinforcement learning for dynamic scheduling of energy-efficient automated guided vehicles
Lixiang Zhang, Yan Yan, Yaoguang Hu
Journal of Intelligent Manufacturing (2023) Vol. 35, Iss. 8, pp. 3875-3888
Closed Access | Times Cited: 15
A multi-agent double Deep-Q-network based on state machine and event stream for flexible job shop scheduling problem
Minghai Yuan, Hanyu Huang, Zichen Li, et al.
Advanced Engineering Informatics (2023) Vol. 58, pp. 102230-102230
Closed Access | Times Cited: 14
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
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, et al.
Computers & Industrial Engineering (2025), pp. 110856-110856
Open Access
An energy-saving real-time scheduling method based on bi-level multi-agent architecture with bargaining game for flexible job shops
Mingzhu Hu, Shengfeng Qin, Shuying Wang, et al.
Expert Systems with Applications (2025), pp. 126527-126527
Closed Access
Multi-agent deep reinforcement learning-based approach for dynamic flexible assembly job shop scheduling with uncertain processing and transport times
Hao Wang, W. Lin, Tao Peng, et al.
Expert Systems with Applications (2025), pp. 126441-126441
Closed Access
Dynamic multi-tour order picking in an automotive-part warehouse based on attention-aware deep reinforcement learning
Xiaohan Wang, Zhang Li, Lihui Wang, et al.
Robotics and Computer-Integrated Manufacturing (2025) Vol. 94, pp. 102959-102959
Closed Access
Solving Dynamic Multi-Objective Flexible Job Shop Scheduling Problems Using a Dual-Level Integrated Deep Q-Network Approach
Hua Xu, Jing-Hua Zheng, Lan Huang, et al.
Processes (2025) Vol. 13, Iss. 2, pp. 386-386
Open Access
Flexible material handling system for multi-load autonomous mobile robots in manufacturing environments: a hierarchical reinforcement learning approach
Keonwoo Park, Seongbae Jo, Youngchul Shin, et al.
International Journal of Production Research (2025), pp. 1-21
Closed Access
Intelligent Human-computer Interactive Training Assistant System for Rail Systems
Y. Z. Li, Junhua Chen, Xiangyong Luo, et al.
High-speed Railway (2025)
Open Access
Neural combinatorial optimization with reinforcement learning in industrial engineering: a survey
K. T. Chung, C.K.M. Lee, Y.P. Tsang
Artificial Intelligence Review (2025) Vol. 58, Iss. 5
Open Access
Adaptive manufacturing control with Deep Reinforcement Learning for dynamic WIP management in industry 4.0
Silvestro Vespoli, Giulio Mattera, Maria Grazia Marchesano, et al.
Computers & Industrial Engineering (2025), pp. 110966-110966
Open Access
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
Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources
Xinxin Zhou, Feimeng Wang, Bin Wu, et al.
Swarm and Evolutionary Computation (2025) Vol. 94, pp. 101885-101885
Closed Access

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