Sander Teck
Papers
6
Total Citations
100
H-Index
4
About
Sander Teck is a rising researcher at the forefront of warehouse automation and logistics optimization, with a focus on Robotic Mobile Fulfillment Systems (RMFS). His work addresses the critical challenge of coordinating autonomous robots and human pickers in e-commerce and distribution centers to maximize throughput and efficiency. Teck has made significant contributions by developing sophisticated optimization models and algorithms, including a bi-level memetic algorithm for integrated order and vehicle scheduling, and an efficient multi-agent approach that simultaneously tackles order picking and robot scheduling. His most influential papers, each garnering 29 citations, have established foundational frameworks for this domain. More recently, Teck has pioneered the application of deep reinforcement learning for real-time inventory rack storage assignment and replenishment, demonstrating his ability to tackle dynamic, real-world operational problems. His simulation-based genetic algorithm for semi-automated warehouses further showcases his versatility in handling processing time variability. With a growing body of work that bridges theoretical optimization and practical implementation, Teck is shaping the future of intelligent, automated logistics systems.
Research Focus
Key Achievements
Top Papers
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