1 citations · 1 across the 6 of their papers we have counts for
8 papers
Planning or Learning: Reliability and Cost in Multi-Asset Maintenance
Xian Yeow Lee, Chandrasekar Venkatraman, Ahmed Farahat
Industrial maintenance systems involve multiple interacting assets and shared resources, making it challenging to balance reliability and operational cost using a single decision f…
A Hybrid Agentic AI Framework for Intelligent Supply Chain Analytics
Xian Yeow Lee, Teppei Inoue, Haiyan Wang +1
Efficient utilization of supply chain analytics for decision making remains a significant challenge for planners, as critical tasks such as database querying, key performance indic…
Building Domain-Specific Small Language Models via Guided Data Generation
Aman Kumar, Ekant Muljibhai Amin, Xian Yeow Lee +5
Large Language Models (LLMs) have shown remarkable success in supporting a wide range of knowledge-intensive tasks. In specialized domains, there is growing interest in leveraging…
Weakly-supervised Latent Models for Task-specific Visual-Language Control
Xian Yeow Lee, Lasitha Vidyaratne, Gregory Sin +2
Autonomous inspection in hazardous environments requires AI agents that can interpret high-level goals and execute precise control. A key capability for such agents is spatial grou…
A Hierarchical Agentic Framework for Autonomous Drone-Based Visual Inspection
Ethan Herron, Xian Yeow Lee, Gregory Sin +3
Autonomous inspection systems are essential for ensuring the performance and longevity of industrial assets. Recently, agentic frameworks have demonstrated significant potential fo…
Exploring LLM-based Frameworks for Fault Diagnosis
Xian Yeow Lee, Lasitha Vidyaratne, Ahmed Farahat +1
Large Language Model (LLM)-based systems present new opportunities for autonomous health monitoring in sensor-rich industrial environments. This study explores the potential of LLM…
Multi-Agent Decision Transformers for Dynamic Dispatching in Material Handling Systems Leveraging Enterprise Big Data
Xian Yeow Lee, Haiyan Wang, Daisuke Katsumata +2
Dynamic dispatching rules that allocate resources to tasks in real-time play a critical role in ensuring efficient operations of many automated material handling systems across ind…
Multi-agent Reinforcement Learning for Dynamic Dispatching in Material Handling Systems
Xian Yeow Lee, Haiyan Wang, Daisuke Katsumata +2
This paper proposes a multi-agent reinforcement learning (MARL) approach to learn dynamic dispatching strategies, which is crucial for optimizing throughput in material handling sy…