Productivity and Efficiency Analysis
(生產力與效率分析)/ Production Economics
efficiency analysis (PEA) is a discipline to assess
the performance of production system and drive productivity. The
technique is developed to estimate production function based on
production economics and then the efficiency of production system can
be identified. PEA supports evaluating the productive efficiency,
effectiveness, product mix,
scale size, productivity change, performance benchmarking, market
power, etc. The research focuses
on developing nonparametric method (e.g. Data Envelopment Analysis,
semi-parametric method (e.g. Stochastic semi-nonparametric envelopment
StoNED) applied to the areas of manufacturing, airlines, energy market,
power system, cap-and-trade, carbon emission, biofuel diesel, etc.
- Price for CO2? 一噸二氧化碳賣多少錢?
- Data Envelopment Analysis (數據包絡分析)/ Performance Evaluation (績效評估)
- Stochastic semi-nonparametric envelopment of data
- Marginal Abatement Cost and Allocation of Emission Permit (邊際減排成本與排放權配置)
- Productive Efficiency, Effectiveness and Scale (生產效率、有效生產與最適規模)
- Marginal Profit/Productivity Analysis (邊際利潤/邊際生產力分析)
- Nash Equilibrium in Oligopolistic Energy Market (能源寡占市場下之納許均衡)
- Lee, Chia-Yen, 2017. Mixed-Strategy Nash Equilibrium in Data Envelopment Analysis. Accepted in European Journal of Operational Research.
- Lee, Chia-Yen and Peng Zhou, 2015. Directional Shadow Price Estimation of CO2, SO2 and NOx in the United States Coal Power Industry 1990-2010. Energy Economics, 51, 493-502.
- Lee, Chia-Yen, 2015. Distinguishing Operational Performance in Power Production: A New Measure of Effectiveness by DEA. IEEE Transactions on Power Systems, 30 (6), 3160-3167.
- Lee, Chia-Yen, 2014. Meta-Data Envelopment Analysis: Finding a Direction Towards Marginal Profit Maximization. European
Journal of Operational Research, 237 (1), 207-216.
Data Science in Manufacturing
(製造數據科學)/ Intelligent Manufacturing
(IMS) is a knowledge-based system which has the computational
intelligence and self-learning ability to optimize the manufacturing
techniques include data mining (decision tree, association
rule, clustering, etc.), meta-heuristic algorithms (e.g. tabu search,
simulated annealing, genetic algorithm, neural network, 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
pattern recognition, etc. Real-setting empirical studies were conducted
to validate the proposed model and improve the business core competence
- 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 Prediction and Optimal Decision by Reinforecement 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 Field Study (工作研究與田野調查)
- Vendor Selection and Order Allocation in Supply Chain (供應鏈廠商評選與訂單配置)
- Lee, Chia-Yen and Bo-Syun Chen, 2017. Mutually-Exclusive-and-Collectively-Exhaustive Feature Selection Scheme. Accepted in Applied Soft Computing. (MOST104-2622-E-006-026-CC3, MOST103-2218-E-007-023)
- Lee, Chia-Yen and Ming-Chien Chiang, 2016. Aggregate Demand Forecast with Small Data and Robust Capacity Decision in TFT-LCD Manufacturing. Computers & Industrial Engineering, 99, 415-422.
- 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.