Papers
2
Total Citations
5
H-Index
2
About
Xingkai Wang is a researcher focused on advancing automation and robotics in manufacturing systems, particularly through the optimization of task allocation and scheduling. His key research areas include multi-robot coordination, assembly line efficiency, and conflict-free scheduling algorithms. Wang’s major contributions address the complex challenge of integrating task allocation with scheduling for multi-type robots, a problem critical to modern, customized production lines. His 2021 paper on coupled task allocation and scheduling for multi-type robots (2 citations) explores this difficult optimization problem, while his 2023 work on a look-ahead AGV scheduling algorithm for no-buffer assembly lines (3 citations) proposes a novel method to eliminate processing sequence conflicts, enhancing production efficiency without costly buffers. Though early in his career, Wang’s research tackles pressing industrial needs for flexibility and real-time coordination. His work is particularly notable for its practical focus on real-world manufacturing constraints, offering solutions that balance theoretical rigor with applicability. As the demand for customized production grows, Wang’s contributions to conflict-free scheduling and multi-robot cooperation position him as a promising voice in industrial robotics and automation research.
Research Focus
Key Achievements
Top Papers
- 1
- 2Research on Coupled Task Allocation and Scheduling of Multi-type Robots2 citations · 2021