Dennis Zhang

About

Dennis Zhang is a leading researcher at the intersection of operations management and artificial intelligence, with a primary focus on revolutionizing inventory management through deep reinforcement learning (DRL). His work addresses a critical question: can advanced AI techniques, proven in robotics and gaming, effectively solve complex, real-world supply chain challenges? Zhang’s major contributions demonstrate that DRL can indeed outperform traditional heuristics in notoriously difficult inventory problems, including lost sales, dual-sourcing, and multi-echelon systems. His 2022 paper on this topic has garnered 172 citations, establishing a foundational framework for applying DRL in operations. Earlier work (2018, 38 citations) specifically tackled the implementation challenges of dual-sourcing modes, providing practical guidance for firms. By bridging the gap between cutting-edge machine learning and classical operations research, Zhang has opened new avenues for data-driven decision-making in supply chains. His research is not only academically rigorous but also highly actionable, offering companies a path toward more resilient and efficient inventory systems. For students and researchers, Zhang’s work is a compelling example of how AI can transform traditional operational paradigms.

Research Focus

Key Achievements

Top Papers

1. 1
   Can Deep Reinforcement Learning Improve Inventory Management? Performance on Lost Sales, Dual-Sourcing, and Multi-Echelon Problems

   172 citations · 2022
2. 2
   Can Deep Reinforcement Learning Improve Inventory Management? Performance and Implementation of Dual Sourcing-Mode Problems

   38 citations · 2018

Key Collaborators

Contact & Links