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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:

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

Showing 22 citing articles:

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: 16
Two-stage double deep Q-network algorithm considering external non-dominant set for multi-objective dynamic flexible job shop scheduling problems
Lei Yue, Kai Peng, Linshan Ding, et al.
Swarm and Evolutionary Computation (2024) Vol. 90, pp. 101660-101660
Closed Access | Times Cited: 8
A discrete event simulator to implement deep reinforcement learning for the dynamic flexible job shop scheduling problem
Lorenzo Tiacci, Andrea Rossi
Simulation Modelling Practice and Theory (2024) Vol. 134, pp. 102948-102948
Open Access | Times Cited: 6
Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning
Chupeng Su, Cong Zhang, Chuang Wang, et al.
Swarm and Evolutionary Computation (2024) Vol. 88, pp. 101605-101605
Closed Access | Times Cited: 5
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
R-DMDQN: A rule embedding based dynamic multi-objective deep Q-network for mass-individualized production scheduling of printed circuit board
Chunrong Pan, Teng Yu, Zhengchao Liu, et al.
Journal of Manufacturing Systems (2025) Vol. 79, pp. 466-483
Closed Access
Research on dynamic job shop scheduling problem with AGV based on DQN
Zhengfeng Li, Wanfa Gu, Huichao Shang, et al.
Cluster Computing (2025) Vol. 28, Iss. 4
Closed Access
Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning
Meng Xu, Yi Mei, Fangfang Zhang, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 6
Open Access
Categorized Attention Based Hierarchical-agents Reinforcement Learning for Multi-objective Dynamic Job Shop Scheduling Problem With Machine Deterioration
Yibing Li, X. Liang, Jun Guo, et al.
Applied Soft Computing (2025), pp. 113032-113032
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
Data-driven hierarchical multi-policy deep reinforcement learning framework for multi-objective multiplicity dynamic flexible job shop scheduling
Linshan Ding, Zailin Guan, Dan Luo, et al.
Journal of Manufacturing Systems (2025) Vol. 80, pp. 536-562
Closed Access
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
Multi-Objective Flexible Flow Shop Production Scheduling Problem Based on the Double Deep Q-Network Algorithm
Hua Gong, Wanning Xu, Wenjuan Sun, et al.
Processes (2023) Vol. 11, Iss. 12, pp. 3321-3321
Open Access | Times Cited: 4
Multi-objective flexible job-shop scheduling via graph attention network and reinforcement learning
Yuanhe Li, Wenjian Zhong, Yuanqing Wu
The Journal of Supercomputing (2024) Vol. 81, Iss. 1
Closed Access | Times Cited: 1
Advancing Genetic Programming for Learning Scheduling Heuristics
Meng Xu
(2024)
Open Access
Dynamic Stability-Aware Scheduling based on Dueling Double Deep Q-Network in FJSP
J.C. Du, Fangyu Li, Honggui Han
2022 34th Chinese Control and Decision Conference (CCDC) (2024), pp. 5715-5720
Closed Access
Dynamic flexible job shop scheduling based on deep reinforcement learning
Dan Yang, Xiantao Shu, Zhen Yu, et al.
Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture (2024)
Closed Access
Optimizing Energy Efficiency in Unrelated Parallel Machine Scheduling Problem through Reinforcement Learning
Christian Pérez, Miguel Ángel, C. March
Information Sciences (2024), pp. 121674-121674
Open Access
Dynamic scheduling for flexible job shop based on MachineRank algorithm and reinforcement learning
Fujie Ren, Haibin Liu
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Smart screening, detection, warning, and control of 3R food hazards and their potential social science impacts
Jiahui Chen, Anet ­Režek ­Jambrak, Yang Dai, et al.
Trends in Food Science & Technology (2024) Vol. 156, pp. 104814-104814
Closed Access
A dynamic flexible job shop scheduling method based on collaborative agent reinforcement learning
Changshun Shao, Zhenglin Yu, Hongchang Ding, et al.
Flexible Services and Manufacturing Journal (2024)
Closed Access
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