Junyi Hu
Papers
1
Total Citations
4
H-Index
1
About
Junyi Hu is a rising researcher in sustainable manufacturing and intelligent optimisation, with a focus on remanufacturing systems and human-robot collaboration. Their most cited work, “Enhancing remanufacturing efficiency: a genetic teaching-learning-based optimisation algorithm for human-robot shared-workstation disassembly line balancing problem” (2025, 4 citations), addresses a critical challenge in circular economy: balancing disassembly lines where humans and robots share tasks. Hu’s major contribution lies in developing a hybrid genetic teaching-learning-based optimisation algorithm that dynamically allocates tasks between human workers and robotic systems to maximise efficiency while minimising idle time and ergonomic risks. This work provides a practical framework for industries transitioning to Industry 5.0, where human-robot collaboration is key. Though early in their career, Hu’s research has already garnered attention for its novel integration of metaheuristic algorithms with real-world manufacturing constraints. Their work is particularly notable for bridging the gap between theoretical optimisation and practical remanufacturing applications, offering actionable solutions for reducing waste and energy consumption in product lifecycle management. Hu’s contributions are poised to influence both academic research and industrial practices in sustainable production systems.
Research Focus
Key Achievements
Top Papers
- 1