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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 reinforcement learning model for dynamic job-shop scheduling problem with uncertain processing time
Xinquan Wu, Xuefeng Yan, Donghai Guan, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107790-107790
Closed Access | Times Cited: 16

Showing 16 citing articles:

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: 6
A recurrent reinforcement learning strategy for optimal scheduling of partially observable job-shop and flow-shop batch chemical plants under uncertainty
Daniel Rangel-Martinez, Luis Ricardez‐Sandoval
Computers & Chemical Engineering (2024) Vol. 188, pp. 108748-108748
Open Access | Times Cited: 5
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
Multi-step look ahead deep reinforcement learning approach for automatic train regulation of urban rail transit lines with energy-saving
Yunfeng Zhang, Shukai Li, Yin Yuan, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 145, pp. 110181-110181
Closed Access
Reinforcement learning enhanced swarm intelligence and trajectory-based algorithms for parallel machine scheduling problems
Fehmi Burçin Özsoydan
Computers & Industrial Engineering (2025), pp. 110948-110948
Closed Access
Joint autonomous decision-making of conflict resolution and aircraft scheduling based on triple-aspect improved multi-agent reinforcement learning
Xiao Huang, Yong Tian, Jonathan Li, et al.
Expert Systems with Applications (2025), pp. 127024-127024
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
Harnessing heterogeneous graph neural networks for Dynamic Job-Shop Scheduling Problem solutions
Chien‐Liang Liu, P.S. Weng, Chun-Jan Tseng
Computers & Industrial Engineering (2025), pp. 111060-111060
Closed Access
Recurrent Reinforcement Learning Strategy with a Parameterized Agent for Online Scheduling of a State Task Network Under Uncertainty
Daniel Rangel-Martinez, Luis Ricardez–Sandoval
Industrial & Engineering Chemistry Research (2025)
Closed Access
A Hybrid Approach for the Multi-Criteria-Based Optimization of Sequence-Dependent Setup-Based Flow Shop Scheduling
Fatih Yiğit, Márcio Pereira Basílio, Valdecy Pereira
Mathematics (2024) Vol. 12, Iss. 13, pp. 2007-2007
Open Access | Times Cited: 2
Deep reinforcement learning for solving car resequencing with selectivity banks in automotive assembly shops
Yuzhe Huang, Gaocai Fu, Buyun Sheng, et al.
International Journal of Production Research (2024), pp. 1-22
Closed Access | Times Cited: 2
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 Self-learning Particle Swarm Optimization Algorithm for Dynamic Job Shop Scheduling Problem with New Jobs Insertion
Kaouther Ben Ali, Hassen Louati, Slim Bechikh
Lecture notes in computer science (2024), pp. 70-84
Closed Access
FlexSim-Simulated PCB Assembly Line Optimization Using Deep Q-Network
Jinhao Du, Jabir Mumtaz, Wenxi Zhao, et al.
(2024), pp. 34-34
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
Lot-streaming in energy-efficient three-stage remanufacturing system scheduling problem with inequal and consistent sublots
Wenjie Wang, Gang Yuan, Duc Truong Pham, et al.
Computers & Electrical Engineering (2024) Vol. 120, pp. 109813-109813
Closed Access
Exploring Efficient Job Shop Scheduling Using Deep Reinforcement Learning
RK Maharjan, Per‐Arne Andersen, Lei Jiao
Lecture notes in computer science (2024), pp. 251-257
Closed Access
Multi-objective scheduling optimization for dynamic parallel batch job shops based on hybrid IPSO-DQN algorithm
Yihui Tang
(2024), pp. 808-813
Closed Access
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