YoungSu Yun
Papers
1
Total Citations
65
H-Index
1
About
YoungSu Yun is a leading researcher in the fields of evolutionary computation, multiobjective optimization, and manufacturing scheduling. His work focuses on developing hybrid evolutionary algorithms that address complex, real-world manufacturing challenges, particularly in multiobjective scheduling where trade-offs between competing goals—such as cost, time, and resource efficiency—are critical. His most-cited paper, "Recent advances in hybrid evolutionary algorithms for multiobjective manufacturing scheduling" (2017), has garnered 65 citations, reflecting its influence in advancing algorithmic approaches for industrial applications. Yun’s contributions include integrating genetic algorithms, particle swarm optimization, and local search techniques to enhance solution quality and computational efficiency. His research has practical implications for smart manufacturing and supply chain management, bridging the gap between theoretical optimization and industrial deployment. Recognized for his innovative methodologies, Yun continues to shape the field by providing robust tools for decision-making in dynamic production environments. His work is essential reading for researchers and students interested in the intersection of artificial intelligence and manufacturing systems.
Research Focus
Key Achievements
Top Papers
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