Laide Guo
Papers
2
Total Citations
8
H-Index
2
About
Laide Guo is a researcher focused on advancing sustainable manufacturing through optimization of disassembly systems. Her primary research areas include disassembly line balancing, multi-objective optimization, and metaheuristic algorithms applied to industrial engineering. Guo’s major contributions center on developing novel computational approaches for the U-shaped disassembly line balancing problem, which addresses the growing challenge of efficiently processing end-of-life products in an era of rapid technological replacement. Her work on the Multi-Objective Multi-Verse Optimizer (2021, 6 citations) introduced a pioneering scheme for multi-product partial U-shaped disassembly lines, incorporating robotic disassembly to improve efficiency and reduce workshop space. Additionally, her Multi-objective Discrete Bat Optimizer (2021, 2 citations) explored how different disassembly line layouts can be optimized to match specific environmental constraints. These contributions are particularly significant given the accelerating volume of electronic waste and end-of-life products. Guo’s research provides practical solutions for manufacturers seeking to balance productivity, space utilization, and environmental sustainability in reverse logistics operations, making her work valuable for both academics and industry practitioners in green manufacturing and industrial optimization.
Research Focus
Key Achievements
Top Papers
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- 2