Kostas Alogariastos
Papers
1
Total Citations
2
H-Index
1
About
Kostas Alogariastos is a rising researcher at the intersection of robotics, optimization, and human-robot collaboration, with a primary focus on developing efficient and equitable algorithms for logistics and warehouse automation. His most-cited work, "Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking" (2024), tackles the critical challenge of allocating human pickers to Autonomous Mobile Robots (AMRs) in collaborative order-picking systems. By modeling the inherent uncertainty in human movement and task completion, Alogariastos proposes a learning-based optimization framework that balances system throughput with fairness among workers—a novel contribution that addresses both operational efficiency and social sustainability in Industry 5.0. Though early in his career, his work has already garnered attention (2 citations in its first year), signaling its relevance to both academia and industry. Alogariastos’s research is particularly notable for its practical orientation: it directly informs the design of decision-support tools for warehouse managers, helping to reduce worker fatigue and improve team morale. His approach—combining reinforcement learning, stochastic optimization, and human factors—positions him as a promising voice in the growing field of human-aware automation.
Research Focus
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Top Papers
- 1