【Title】 A Gradient Approach for Efficient Design of Control Charts Under Uncertainty

【Speaker】 Dr. Lianjie Shu, Associate Professor, Faculty of Business Administration, University of Macau

【Host】 Dr. Kaibo Wang

【Time】 1:30-2:30pm, Friday, December 21, 2012

【Location】Room 510, Shun-de Building

【Abstract】Traditional cumulative sum (CUSUM) control charts are often designed to optimize the detection performance for a prescribed magnitude of mean shift when monitoring the mean level of a process. However, the shift to occur in the future is often unknown. The performance of the chart designed as such could be suboptimal when the actual shift magnitude is different from the pre-specified one. This paper considers efficient design of CUSUM charts for monitoring shifts in the process mean with uncertainty. A fast and accurate algorithm based on the gradient method is developed for this purpose. Optimal design parameters were obtained and compared with the existing simulation results. The gradient method is shown to provide more accurate and faster design of CUSUM charts under uncertainty than using Monte Carlo simulations.

【Brief bio】Dr. Lianjie Shu is currently an Associate Professor in Faculty of Business Administration at University of Macau. He received his Bachelor degree in Mechanical Engineering and Automation from Xi'an Jiao Tong University, and his Ph.D. in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology (HKUST). He currently serves an Associate Editor on Journal of Statistical Computation and Simulation and a Senior Editor on Journal of the Chinese Institute of Industrial Engineers. His recent research interests include statistical quality control, healthcare surveillance, and statistical computing. His publications appear on a wide variety of journals such as Statistics in Medicine, Naval Research Logistics, IIE Transactions, Journal of Quality Technology, etc.