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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 in production systems: a systematic literature review
Marcel Panzer, Benedict Bender
International Journal of Production Research (2021) Vol. 60, Iss. 13, pp. 4316-4341
Open Access | Times Cited: 150

Showing 1-25 of 150 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: 159
Reinforcement learning applied to production planning and control
Ana Esteso, David Peidro, Josefa Mula, et al.
International Journal of Production Research (2022) Vol. 61, Iss. 16, pp. 5772-5789
Open Access | Times Cited: 73
Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions
Robert X. Gao, Jörg Krüger, Marion Merklein, et al.
CIRP Annals (2024) Vol. 73, Iss. 2, pp. 723-749
Open Access | Times Cited: 25
Solving real-world optimization tasks using physics-informed neural computing
Jaemin Seo
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 20
Scheduling of stochastic distributed hybrid flow-shop by hybrid estimation of distribution algorithm and proximal policy optimization
Lin Luo, Xuesong Yan
Expert Systems with Applications (2025), pp. 126523-126523
Closed Access | Times Cited: 4
A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework
Alejandro J. del Real, Doru Stefan Andreiana, Álvaro Ojeda Roldán, et al.
Applied Sciences (2022) Vol. 12, Iss. 23, pp. 12377-12377
Open Access | Times Cited: 38
Pixel‐level thin crack detection on road surface using convolutional neural network for severely imbalanced data
Thitirat Siriborvornratanakul
Computer-Aided Civil and Infrastructure Engineering (2023) Vol. 38, Iss. 16, pp. 2300-2316
Closed Access | Times Cited: 34
Reinforcement learning for predictive maintenance: a systematic technical review
Rajesh Siraskar, Satish Kumar, Shruti Patil, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. 11, pp. 12885-12947
Closed Access | Times Cited: 30
A review on learning to solve combinatorial optimisation problems in manufacturing
Cong Zhang, Yaoxin Wu, Yining Ma, et al.
IET Collaborative Intelligent Manufacturing (2023) Vol. 5, Iss. 1
Open Access | Times Cited: 26
Cognitive intelligence in industrial robots and manufacturing
Avishek Mukherjee, A Divya, M. Sivvani, et al.
Computers & Industrial Engineering (2024) Vol. 191, pp. 110106-110106
Closed Access | Times Cited: 11
IoT‐5G and B5G/6G resource allocation and network slicing orchestration using learning algorithms
Ado Adamou Abba Ari, Faustin Samafou, Arouna Ndam Njoya, et al.
IET Networks (2025) Vol. 14, Iss. 1
Open Access | Times Cited: 1
Optimisation of manufacturing process parameters for variable component geometries using reinforcement learning
Clemens Zimmerling, Christian Poppe, Oliver Stein, et al.
Materials & Design (2022) Vol. 214, pp. 110423-110423
Open Access | Times Cited: 29
Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN
Hanting Zhou, Wenhe Chen, Changqing Shen, et al.
International Journal of Production Research (2022) Vol. 61, Iss. 23, pp. 8252-8264
Closed Access | Times Cited: 28
Reinforcement learning applied to wastewater treatment process control optimization: Approaches, challenges, and path forward
Henry C. Croll, Kaoru Ikuma, Say Kee Ong, et al.
Critical Reviews in Environmental Science and Technology (2023) Vol. 53, Iss. 20, pp. 1775-1794
Closed Access | Times Cited: 22
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: 22
Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization
Henry C. Croll, Kaoru Ikuma, Say Kee Ong, et al.
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 18382-18390
Closed Access | Times Cited: 18
Real-time AGV scheduling optimisation method with deep reinforcement learning for energy-efficiency in the container terminal yard
Lin Gong, Zijie Huang, Xi Xiang, et al.
International Journal of Production Research (2024) Vol. 62, Iss. 21, pp. 7722-7742
Closed Access | Times Cited: 7
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: 7
Review of Learning-Based Robotic Manipulation in Cluttered Environments
Marwan Qaid Mohammed, Lee Chung Kwek, Shing Chyi Chua, et al.
Sensors (2022) Vol. 22, Iss. 20, pp. 7938-7938
Open Access | Times Cited: 23
A deep reinforcement learning based hyper-heuristic for modular production control
Marcel Panzer, Benedict Bender, Norbert Gronau
International Journal of Production Research (2023) Vol. 62, Iss. 8, pp. 2747-2768
Open Access | Times Cited: 14
Look-ahead based reinforcement learning for robotic flow shop scheduling
Hyun-Jung Kim, Jun-Ho Lee
Journal of Manufacturing Systems (2023) Vol. 68, pp. 160-175
Closed Access | Times Cited: 13
Deep reinforcement learning for maintenance optimization of a scrap-based steel production line
Waldomiro Alves Ferreira Neto, Cristiano Alexandre Virgínio Cavalcante, Phuc Do
Reliability Engineering & System Safety (2024) Vol. 249, pp. 110199-110199
Open Access | Times Cited: 5
Daydreaming factories
Aydin Nassehi, Marcello Colledani, Botond Kádár, et al.
CIRP Annals (2022) Vol. 71, Iss. 2, pp. 671-692
Open Access | Times Cited: 19
Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems
Fatima Ezzahra Achamrah, Ali Attajer
International Journal of Production Research (2023) Vol. 62, Iss. 10, pp. 3460-3482
Closed Access | Times Cited: 11
gym-flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout Problems
Benjamin Heinbach, Peter Burggräf, Johannes Wagner
Operations Research Forum (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 4
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