Asghar Moeini
Papers
2
Total Citations
69
H-Index
2
About
Asghar Moeini is a leading researcher in the optimization of automated storage and retrieval systems (AS/RS) and robotic manufacturing cells. His work focuses on developing advanced computational methods to enhance efficiency in logistics and production environments. Moeini’s most influential contribution is his 2018 paper on applying the cross-entropy method to optimize robotic AS/RS, which has garnered 55 citations. In this seminal work, he addresses the complex challenge of sequencing Cartesian robot movements to simultaneously retrieve orders and create optimally packed store-ready pallets, significantly improving throughput in automated warehouses. His research also extends to stochastic scheduling in manufacturing, as demonstrated in his 2017 paper on two-machine robotic rework cells with in-process inspection systems (14 citations). This work tackles uncertainty in production lines by integrating inspection and rework processes into scheduling frameworks. Moeini’s contributions are particularly valuable for industries transitioning to Industry 4.0, where robotic automation and real-time optimization are critical. His cross-entropy methodology has become a benchmark for solving combinatorial optimization problems in robotics and logistics, establishing him as a key figure in the intersection of operations research and automation engineering.
Research Focus
Key Achievements
Top Papers
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