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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:

Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem
Chupeng Su, Cong Zhang, Dan Xia, et al.
Applied Soft Computing (2023) Vol. 145, pp. 110596-110596
Closed Access | Times Cited: 20

Showing 20 citing articles:

Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities
Yanjie Song, Yutong Wu, Yangyang Guo, et al.
Swarm and Evolutionary Computation (2024) Vol. 86, pp. 101517-101517
Open Access | Times Cited: 32
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: 9
Graph neural networks for job shop scheduling problems: A survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen, et al.
Computers & Operations Research (2024), pp. 106914-106914
Open 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
Study on the Multi-Equipment Integrated Scheduling Problem of a U-Shaped Automated Container Terminal Based on Graph Neural Network and Deep Reinforcement Learning
Qinglei Zhang, Yi Zhu, Jiyun Qin, et al.
Journal of Marine Science and Engineering (2025) Vol. 13, Iss. 2, pp. 197-197
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
Towards Industry 5.0: digital twin-enhanced approach for dynamic supply chain rescheduling with real-time order arrival and acceptance
Xing Zhu, Baoyu Liao, Yexing Shen, et al.
International Journal of Production Research (2025), pp. 1-23
Closed Access
Reinforcement Learning Driven Cross‐Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect
Taixin Li, Chenxi Ye, Lang Wu, et al.
Computational Intelligence (2025) Vol. 41, Iss. 2
Closed Access
Hybrid machine learning approach for parallel machine scheduling under uncertainty
Aleksandar Stanković, Goran Petrović, Rajko Turudija, et al.
Expert Systems with Applications (2025) Vol. 279, pp. 127427-127427
Closed Access
A novel method-based reinforcement learning with deep temporal difference network for flexible double shop scheduling problem
Xiao Wang, Peisi Zhong, Mei Liu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Advancing feature ranking with hybrid feature ranking weighted majority model: a weighted majority voting strategy enhanced by the Harris hawks optimizer
Mansourah Aljohani, Yousry AbdulAzeem, Hossam Magdy Balaha, et al.
Journal of Computational Design and Engineering (2024) Vol. 11, Iss. 3, pp. 308-325
Open Access | Times Cited: 2
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
Graph reinforcement learning for flexible job shop scheduling under industrial demand response: A production and energy nexus perspective
Zhangjie Rui, Xi Tiao Zhang, Mingzhou Liu, et al.
Computers & Industrial Engineering (2024) Vol. 193, pp. 110325-110325
Closed Access | Times Cited: 1
A modified multi-agent proximal policy optimization algorithm for multi-objective dynamic partial-re-entrant hybrid flow shop scheduling problem
Jiawei Wu, Yong Liu
Engineering Applications of Artificial Intelligence (2024) Vol. 140, pp. 109688-109688
Closed Access | Times Cited: 1
A multiobjective optimizer with a K-means cluster algorithm for a distributed flexible flowshop rescheduling problem
Xin-Rui Tao, Quan-Ke Pan, Hongyan Sang, et al.
Applied Soft Computing (2024) Vol. 166, pp. 112124-112124
Closed Access | Times Cited: 1
An effective multi-agent-based graph reinforcement learning method for solving flexible job shop scheduling problem
Lanjun Wan, Long Fu, Changyun Li, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 139, pp. 109557-109557
Closed Access
A GA-Based Scheduling Algorithm for Semiconductor-Product Thermal Cycling Tests
Yeong-Chyi Lee, Tzung‐Pei Hong, Yi-Chen Chiu, et al.
Lecture notes in electrical engineering (2024), pp. 403-412
Closed Access
Solving Resource Allocation and Production Scheduling Problems in Aerospace Industry Using Double Deep Q-Network with State Feature Images
Bing Yang
(2024)
Closed Access
A Novel Reinforcement Learning Approach for Enhancing Flexible Job-Shop Scheduling with Dual Gated-Attention Network
Yingao Gao, Shuo Shi, Guojing Liu, et al.
Lecture notes in computer science (2024), pp. 480-492
Closed Access
Improved Evolutionary Strategy Reinforcement Learning for Multi-Objective Dynamic Scheduling of Hybrid Flow Shop Problem
Junjie Zhang, Yarong Chen, Jabir Mumtaz
(2024) Vol. 61, pp. 22-22
Open Access
A guided twin delayed deep deterministic reinforcement learning for vaccine allocation in human contact networks
Ehsan Ardjmand, Alireza Fallahtafti, Ehsan Yazdani, et al.
Applied Soft Computing (2024) Vol. 167, pp. 112322-112322
Open Access
An Optimization Method for Green Permutation Flow Shop Scheduling Based on Deep Reinforcement Learning and MOEA/D
Yongxin Lu, Yiping Yuan, Adilanmu Sitahong, et al.
Machines (2024) Vol. 12, Iss. 10, pp. 721-721
Open Access
Hybrid Machine Learning Approach for Parallel Machine Scheduling Under Uncertainty
Aleksandar Stanković, Goran Petrović, Rajko Turudija, et al.
(2023)
Closed Access
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