Manufacturing Data Science (MDS) is a decision-oriented system which has the computational intelligence and self-learning ability to optimize the manufacturing process. The techniques include data science (decision tree, deep learning, feature engineering, clustering, etc.), meta-heuristic algorithms (e.g. tabu search, simulated annealing, genetic algorithm, particle swarm optimization, etc.), fault detection & classification (FDC), statistical process control (SPC), engineering data analysis (EDA), etc. These methodologies can optimize resource allocation and support trouble-shooting process. The applications are diversified such as capacity planning, production scheduling, machine configuration optimization, process fault diagnosis, facility layout, bottleneck identification, quality and yield enhancement, market segmentation, pattern recognition, etc. Real-setting empirical studies were conducted to validate the proposed model and improve the business core competence effectively.

Research Interests:

• Data Science in Manufacturing (製造數據科學)
• Data Mining for Yield Improvement (數據探勘與良率改善)
• Statistical Process Control (SPC) Big Data Analytics (統計製程管制大數據分析)
  
• Process Diagnosis & Pattern Recognition (製程診斷與樣型識別)
• Price Forecasting and Optimal Decision by Reinforcement Learning (價格預測與強化學習)
  
• Virtual Material Quality Investigation (虛擬物料品質檢測)
  
• MECE Engineering Feature Selection and Predictive Maintenance (工程參數篩選與預測保養)
  
  
• Manufacturing System Management (製造系統管理)
  
• Multi-Objective Job-Shop Stochastic Scheduling (多目標隨機生產排程)
• Demand Forecasting and Robust Capacity Planning (需求預測與穩健產能規劃)
  
• Work Study and Time Motion Study (工作研究與時間動作研究)
  
• Vendor Selection and Order Allocation in Supply Chain (供應鏈廠商評選與訂單配置)

Representative Publications:

• Shen, Po-Cheng, Lu, Meng-Xiu, and Lee, Chia-Yen*, 2022. Spatio-temporal anomaly detection for substrate strip bin map in semiconductor assembly process. IEEE Robotics and Automation Letters, 7(4), 9493-9500.
• Lee, Chia-Yen, Chou, Bai-Jian, and Huang, C.-F. 2022. Data science and reinforcement learning for price forecasting and raw material procurement in petrochemical industry. Advanced Engineering Informatics, 51, 101443.
  
• Lee, Chia-Yen, Wu, Chao-Shian, and Hung, Yu-Hsin, 2021. In-line predictive monitoring framework. IEEE Transactions on Automation Science and Engineering, 18 (4), 1668-1678.
  
• Hung, Shao-Yen, Chia-Yen Lee, and Yung-Lun Lin, 2020. Data Science for Delamination Prognosis and Online Batch Learning in Semiconductor Assembly Process. IEEE Transactions on Components, Packaging and Manufacturing Technology, 10 (2), 314-324.
  
• Lee, Chia-Yen and Bo-Syun Chen, 2018. Mutually-Exclusive-and-Collectively-Exhaustive Feature Selection Scheme. Applied Soft Computing, 68, 961-971.
• Lee, Chia-Yen and C.-F. Chien, 2014. Stochastic Programming for Vendor Portfolio Selection and Order Allocation under Delivery Uncertainty. OR Spectrum, 36 (3), 761-797.