Messaoud Ahmed Ouameur
Papers
1
Total Citations
3
H-Index
1
About
Messaoud Ahmed Ouameur is a researcher whose work lies at the intersection of machine learning, autonomous systems, and industrial logistics. His primary research focuses on developing predictive models to optimize the operational efficiency of autonomous fleets, particularly battery-powered electric vehicles. His most cited paper, "Machine Learning Approach for Charging Queue Waiting Time Prediction of Electrical Autonomous Forklifts Fleet" (2022), addresses a critical bottleneck in warehouse automation: the long charging times and limited autonomy of electric forklifts. By applying machine learning to predict charging queue waiting times, Ouameur provides a practical solution to minimize downtime and improve fleet scheduling—a contribution that directly impacts the productivity of modern logistics and manufacturing environments. Though his citation count is currently modest, his work is highly relevant to the growing field of autonomous mobile robots and smart energy management. Ouameur’s research is notable for bridging theoretical machine learning models with real-world industrial challenges, making him a promising voice in the advancement of sustainable, autonomous material handling systems.
Research Focus
Key Achievements
Top Papers
- 1