Huey Yuen Ng

About

Huey Yuen Ng is a rising scholar at the intersection of operations research and artificial intelligence, with a primary focus on supply chain optimization and inventory management. Their most impactful work, "A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand" (2022, 11 citations), introduces a novel framework that leverages deep reinforcement learning to tackle the complex, real-world challenge of managing inventory when both lead times and demand are uncertain. This contribution is particularly significant as it moves beyond traditional heuristic and mathematical programming methods, demonstrating how AI can dynamically adapt to volatile supply chain environments. By bridging deep learning with combinatorial optimization, Ng’s research offers a practical, data-driven pathway for improving efficiency in logistics and manufacturing. Though early in their career, this work has already garnered attention for its innovative application of reinforcement learning to a classic operations problem, positioning Ng as a promising voice in the growing field of AI-driven decision-making. Their research holds clear implications for industries seeking to reduce costs and enhance resilience in an increasingly unpredictable global market.

Research Focus

Key Achievements

Top Papers

1. 1
   A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand

   11 citations · 2022

Key Collaborators

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