Junqing Li
Papers
4
Total Citations
67
H-Index
3
About
Junqing Li is a leading researcher in intelligent optimization and robotics, whose work bridges the gap between theoretical algorithms and real-world autonomous systems. His primary research areas include metaheuristic optimization, distributed scheduling, and multi-objective path planning for mobile and service robots. Li’s most impactful contribution is the development of an improved iterated greedy algorithm for the distributed robotic flowshop scheduling problem with order constraints—a complex manufacturing challenge where robots transfer jobs between machines across multiple factories. This work, published in 2021, has garnered 39 citations and is complemented by a foundational 2019 study (18 citations) that established the algorithm’s effectiveness in minimizing makespan. In the domain of autonomous navigation, Li has advanced multi-objective path planning, notably through a developed firefly algorithm (8 citations) that optimizes path length, safety, and smoothness using grid-based environmental modeling. His Pareto-based optimization approach for service robots, though newer, demonstrates his commitment to practical, multi-criteria decision-making. By integrating robotic constraints with cutting-edge swarm intelligence, Li’s research provides scalable solutions for modern manufacturing and service robotics, making him a key figure in applied operations research.
Research Focus
Key Achievements
Top Papers
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