Papers
7
Total Citations
229
H-Index
6
About
Yuyan Han is a computational intelligence researcher whose work spans two interconnected domains: intelligent scheduling for industrial systems and autonomous robot path planning. With a growing body of highly cited work, Han has established a reputation for developing sophisticated evolutionary and metaheuristic algorithms to tackle complex real-world optimization problems. Han's most impactful contribution, garnering 83 citations, introduced a bi-population balancing multi-objective evolutionary algorithm for fuzzy flexible job shop scheduling in steelmaking environments — a problem of significant industrial relevance involving uncertain processing times and energy-aware logistics. This work exemplifies Han's ability to bridge theoretical algorithm design with practical manufacturing challenges. Further contributions to distributed robotic flowshop scheduling, including an iterated greedy algorithm framework developed across multiple studies (2019–2021), demonstrate a sustained and deepening research trajectory. Equally notable is Han's parallel focus on mobile robot path planning, with an improved NSGA-II approach published in 2024 attracting 64 citations remarkably quickly, and earlier ABC and firefly algorithm-based solutions addressing objectives including path length, safety, and smoothness. Collectively, Han's publications reflect a researcher making meaningful strides in applying evolutionary computation to both smart manufacturing and intelligent robotics, with a cumulative impact exceeding 225 citations across just seven key works.
Research Focus
Key Achievements
Top Papers
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