Yilin Fang
Papers
24
Total Citations
430
H-Index
9
About
Yilin Fang is a researcher specializing in intelligent optimization, robotic disassembly systems, and sustainable manufacturing. Their work sits at the intersection of evolutionary computation, operations research, and applied robotics, with a particular focus on solving complex line-balancing problems in remanufacturing and end-of-life product recovery. Fang's most significant contributions center on the design and optimization of multi-robotic disassembly lines, addressing real-world complexities such as mixed-model production, interval processing times, and resource constraints. Their 2019 paper on multi-objective evolutionary simulated annealing has garnered 118 citations, while their 2018 study on many-objective evolutionary optimization for multi-robotic workstations has attracted 99 citations — together establishing them as a leading voice in robotic disassembly line balancing. Their research progressively evolved to incorporate human-robot collaboration, dynamic environments, and transfer learning, reflecting a sophisticated awareness of industrial realities. Beyond optimization, Fang has demonstrated breadth by applying deep reinforcement learning to mobile robot path planning and disassembly line balancing, bridging classical operations research with modern artificial intelligence. With over 380 total citations across ten papers, their body of work makes a compelling case for automated, sustainable manufacturing systems — offering practical algorithmic tools that advance both environmental responsibility and industrial efficiency.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6Optimisation of Robotic Disassembly for Remanufacturing18 citations · 2021
- 7
- 8
- 9
- 10