Nima Zaerpour

Papers

Total Citations

116

H-Index

About

Nima Zaerpour is a leading researcher in the design and optimization of robotic warehouse systems, with a particular focus on e-commerce fulfillment. His work addresses critical challenges in robotic mobile fulfillment systems (RMFS), where mobile robots transport inventory pods to human pickers. Zaerpour’s most cited paper, “Robot scheduling for pod retrieval in a robotic mobile fulfillment system” (2020, 82 citations), develops scheduling algorithms that dramatically improve order picking efficiency by coordinating robot movements and pod retrieval sequences. Building on this, his 2021 paper “How to benefit from order data: correlated dispersed storage assignment in robotic warehouses” (34 citations) introduces innovative storage assignment strategies that leverage historical customer order data to reduce response times. By combining product dispersion with turnover-based slotting, Zaerpour demonstrates how warehouses can fully exploit order correlations to minimize travel distances and boost throughput. His research bridges operations research, robotics, and data analytics, offering practical solutions for major online retailers. With over 100 combined citations on these foundational works, Zaerpour is recognized as a key contributor to the next generation of intelligent, automated warehousing systems that power modern e-commerce logistics.