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About the database ScholarIQanswers from OpenAlex & ORCID
How has Shengbo Wang's publication output changed over time?
ScholarIQpublication output · 2020–2025
Output grew200% over the shown period — from 1 works in 2020 to 3 in 2025.
1
3
3
3
2020202320242025
What are the most-cited papers on Shengbo Wang?
ScholarIQmost cited works
A Hybrid MCDM Method Using Combination Weight for the Selection of Facility Layout in the Manufacturing System: A Case Study
Shanshan Zha, Guo Yu, Shaohua Huang, Shengbo Wang
S178238111. 202041 CitationsOPEN ACCESS
Towards discrete manufacturing workshop-oriented digital twin model: Modeling, verification and evolution
Weiwei Qian, Yu Guo, Litong Zhang, Shengbo Wang, Shaohua Huang, Sai Geng
Journal of Manufacturing Systems. 202336 Citations
Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning
Shengbo Wang, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhixian Li, Zhenyu Wang, Yunpeng Ma
S69141925. 202427 Citations
Dynamic production bottleneck prediction using a data-driven method in discrete manufacturing system
Daoyuan Liu, Yu Guo, Shaohua Huang, Shengbo Wang, Tao Wu
S112141509. 202319 Citations
Digital twin driven dynamic scheduling of discrete manufacturing workshop with transportation resource constraint using multi-agent deep reinforcement learning
S. Geng, Shaohua Huang, Yu Guo, Weiwei Qian, Weiguang Fang, Litong Zhang, Shengbo Wang
Robotics and Computer-Integrated Manufacturing. 202517 Citations
Related on ScholarIQ
Hong Kong Polytechnic University
Institution
A Hybrid MCDM Method Using Combination Weight for the Selection of Facility Layout in the Manufacturing System: A Case Study
Paper
Towards discrete manufacturing workshop-oriented digital twin model: Modeling, verification and evolution
Paper
Multi-AUV Cooperative Underwater Multi-Target Tracking Based on Dynamic-Switching-Enabled Multi-Agent Reinforcement Learning
Paper
Dynamic production bottleneck prediction using a data-driven method in discrete manufacturing system
Paper
Digital twin driven dynamic scheduling of discrete manufacturing workshop with transportation resource constraint using multi-agent deep reinforcement learning
Paper