Kan Fang
Papers
7
Total Citations
136
H-Index
5
About
Kan Fang is a leading researcher in smart manufacturing, with a core focus on human–robot collaboration (HRC) in assembly line design and optimization. His work addresses the critical challenge of balancing productivity, worker safety, and flexibility in modern production systems. Fang’s major contributions include developing novel mathematical models and metaheuristic algorithms—such as tabu search and combinatorial Benders decomposition—to solve complex assembly line balancing and scheduling problems involving collaborative robots (cobots). His 2023 and 2024 papers on U-type and parallel assembly lines with HRC have each garnered 36 citations, reflecting their immediate impact on the field. Notably, his 2024 study on parallel assembly lines explores how HRC can improve production efficiency, while his improved Benders decomposition algorithm offers a powerful tool for optimizing task allocation between humans and cobots. Fang also promotes open science by sharing code and data repositories, enabling reproducibility and further research. With over 130 total citations in just two years, his work is shaping the future of human-centric, automated manufacturing.
Research Focus
Key Achievements
Top Papers
- 1Balancing U-type assembly lines with human–robot collaboration36 citations · 2023
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- 7Job shop scheduling problem with a dual-gripper robot2 citations · 2025