Tu-San Pham
Papers
1
Total Citations
4
H-Index
1
About
Tu-San Pham is a rising researcher in operations research and supply chain management, whose work bridges advanced machine learning and practical logistics optimization. His most-cited paper, "Deep reinforcement learning for the real-time inventory rack storage assignment and replenishment problem" (2025), has already garnered 4 citations, signaling early impact in a field where real-time decision-making is critical. Pham’s key research areas include reinforcement learning, inventory management, and warehouse automation, with a focus on developing adaptive algorithms that can handle dynamic, high-dimensional problems like rack storage assignment and replenishment scheduling. By integrating deep reinforcement learning into operational logistics, he addresses the challenge of balancing storage efficiency with order fulfillment speed—a contribution that holds promise for e-commerce and manufacturing sectors. While still early in his career, Pham’s work demonstrates a commitment to translating theoretical advances into scalable, real-world solutions, positioning him as a researcher to watch in the intersection of AI and supply chain optimization.
Research Focus
Key Achievements
Top Papers
- 1