Shin-Li Lu | Industrial Engineering | Best Researcher Award

Prof. Shin-Li Lu | Industrial Engineering | Best Researcher Award

Prof. Shin-Li Lu | Chung Yuan Christian University | Taiwan

Prof. Shin-Li Lu is a distinguished Professor in the Department of Industrial and Systems Engineering and serves as Chairman of the Undergraduate Program at the College of Electrical Engineering and Computer Science, Chung Yuan Christian University. With extensive expertise in statistical process control, quality and production management, business intelligence, and big data analytics, Prof. Shin-Li Lu has made significant contributions to both academia and industry. He has held leadership roles including Dean of Management and Information and Chairman of Industrial Management and Enterprise Information, and has previously served as Professor and Associate Professor at Aletheia University. His prolific research includes numerous high-impact publications in SCI and EI-indexed journals on topics such as EWMA, GWMA control charts, economic-statistical design, and forecasting methods for manufacturing and energy systems. Prof. Shin-Li Lu has presented widely at international conferences, led multiple MOST-funded research projects, and contributes as an editor and reviewer for prominent journals, advancing sustainable industrial engineering, quality management, and statistical process monitoring.

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Featured Publications

Lu, S. L. (2015). An extended nonparametric exponentially weighted moving average sign control chart. Quality and Reliability Engineering International, 31(1), 3–13. [Cited by 84]

Lu, S. L. (2019). Integrating heuristic time series with modified grey forecasting for renewable energy in Taiwan. Renewable Energy, 133, 1436–1444. [Cited by 56]

Lu, S. L. (2018). Nonparametric double generally weighted moving average sign charts based on process proportion. Communications in Statistics – Theory and Methods, 47(11), 2684–2700. [Cited by 27]

Sheu, S. H., & Lu, S. L. (2009). Monitoring the mean of autocorrelated observations with one generally weighted moving average control chart. Journal of Statistical Computation and Simulation, 79(12), 1393–1406. [Cited by 26]

Huang, C. J., Tai, S. H., & Lu, S. L. (2014). Measuring the performance improvement of a double generally weighted moving average control chart. Expert Systems with Applications, 41(7), 3313–3322. [Cited by 24]