Dr. Dailin Huang, Lanzhou University of Technology, China
👨🔬 Dr. Dailin Huang is a researcher specializing in deep reinforcement learning and intelligent systems. He earned his B.E. from Nanjing University of Posts and Telecommunications (2019) and M.Eng from Lanzhou University of Technology (2022), where he explored multi-agent learning for adaptive traffic signal control. Now pursuing a PhD, his focus is on flexible job shop scheduling using DRL. Dr. Huang has published in top SCI journals (IF up to 8.7), contributed to national R&D programs 🇨🇳, and holds a patent in urban rail scheduling software 🚆. His interests include GNNs, intelligent transportation, and fault diagnosis. 📧
Publication Profile
Orcid
🎓 Education Background
Dr. Dailin Huang earned his B.E. degree in 2019 from Nanjing University of Posts and Telecommunications, majoring in Communication Engineering 📡. He completed his M.Eng degree at Lanzhou University of Technology in 2022 🎓, where he focused on multi-agent reinforcement learning algorithms for solving adaptive traffic signal control problems 🚦. During this time, he received several provincial awards 🏅, published multiple papers 📚, and secured patents 📄. In his ongoing doctoral studies, Dr. Huang is dedicated to applying deep reinforcement learning (DRL) to address flexible job shop scheduling problems in complex industrial systems 🏭
🔬 Research Interests
Dr. Dailin Huang’s research spans several cutting-edge areas in intelligent systems and machine learning 🤖. His primary focus lies in Deep Reinforcement Learning (DRL) for solving complex optimization problems 🧠. He is passionate about Flexible Job Shop Scheduling 🏭 and Multi-Agent Systems 🧑🤝🧑, aiming to enhance operational efficiency and coordination. Dr. Huang also explores Intelligent Transportation Systems 🚦, leveraging AI to optimize urban mobility. His interests extend to Fault Diagnosis and Intelligent Maintenance 🛠️, improving reliability in industrial processes, and the application of Graph Neural Networks (GNNs) 🔗 to model complex relationships in structured data.
🧪 Research Experience
Dr. Dailin Huang has actively contributed to major national and provincial research initiatives in intelligent systems and manufacturing innovation. As a Research Assistant 🧑💻 in the National Key R&D Program of China, he participated in the project on Network Collaborative Manufacturing within nonferrous metallurgy industrial clusters 🏭. He also led the Innovation Star Graduate Project 🌟 as Principal Investigator, focusing on Adaptive Traffic Signal Control using Multi-Agent Reinforcement Learning 🚦. Supported by the Gansu Provincial Education Science and Technology Innovation Project 🎓, this work demonstrated Dr. Huang’s leadership in applying AI to real-world optimization challenges
🎯 Research Focus
Dr. Dailin Huang’s research centers on the convergence of deep reinforcement learning 🤖 and graph neural networks 📊 for solving complex industrial optimization problems. His primary focus lies in flexible job shop scheduling 🏭, where he has developed intelligent dispatching and scheduling methods using multi-expert and graph-attention-based neural models. Additionally, he explores adaptive traffic signal control 🚦 using multi-agent systems and reinforcement learning to improve urban mobility. His contributions extend to fault diagnosis 🛠️ through adversarial learning and multilayer network structures, emphasizing intelligent maintenance. Dr. Huang’s work bridges AI, manufacturing, and intelligent transportation systems, earning recognition in high-impact SCI journals
Conclusion
Dr. Dailin Huang is highly suitable for the Research for Best Innovation Award. His blend of cutting-edge research in AI and reinforcement learning, practical applications in transportation and scheduling, top-tier publications, leadership roles, and patent output all indicate a strong capacity for innovative thinking and transformative research impact.
Publication Top Notes
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Optimizing the flexible job shop scheduling problem via deep reinforcement learning with mean multichannel graph attention
📘 Applied Soft Computing, 2025
🏭📊🤖
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A deep reinforcement learning method based on a multiexpert graph neural network for flexible job shop scheduling
📘 Computers & Industrial Engineering, 2024
🏭🤖📈
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Learning to Dispatch for Flexible Job Shop Scheduling based on Deep Reinforcement Learning via Graph Gated Channel Transformation
📘 IEEE Access, 2024
🏭🔄🤖
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A multi-process value-based reinforcement learning environment framework for adaptive traffic signal control
📘 Journal of Control and Decision, 2022
🚦🧠🔁
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Multi-agent deep reinforcement learning with traffic flow for traffic signal control
📘 Journal of Control and Decision, 2023
🚗🤖🔄
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Railway Adaptive Dispatching Decision Method Based on Double DQN
📘 International Conference on Information Science, Computer Technology and Transportation (ISCTT), 2020
🚆🧠📊
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Method to Enhance Deep Learning Fault Diagnosis by Generating Adversarial Samples
📘 Applied Soft Computing, 2021
🛠️🤖🔍
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Finding the optimal multilayer network structure through reinforcement learning in fault diagnosis
📘 Measurement, 2021
🛠️🧠📈
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A Fault Diagnosis Method Based on Multichannel Markov Transition Field
📘 Journal of Jilin University (Engineering and Technology Edition), Year not specified
🛠️📊📉