Sining An
Papers
1
Total Citations
2
H-Index
1
About
Sining An is a rising researcher in industrial and manufacturing engineering, with a focus on intelligent scheduling and automation in automotive production. Their key research areas include reinforcement learning, dynamic manufacturing systems, and the optimization of flexible assembly lines using autonomous mobile robots (AMRs). An’s most notable contribution is the development of a Deep Q-Network (DQN) approach for matrix assembly system scheduling, which addresses the challenge of demand diversification in modern automotive manufacturing. This work, published in 2024, demonstrates how deep reinforcement learning can enable real-time, adaptive scheduling in complex, multi-workstation environments—moving beyond traditional static methods. Though early in their career, An’s paper has already garnered citations, signaling its relevance to both academia and industry. By integrating AI with physical production systems, An is helping to shape the next generation of smart factories, where flexibility and efficiency are paramount. Their research holds promise for reducing downtime and improving throughput in high-variability manufacturing settings, marking them as a contributor to the ongoing digital transformation of industrial operations.
Research Focus
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Top Papers
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