Leyuan Shi

Title: Manufacturing Execution Optimization
Abstract: 

    Many manufacturing firms use aggregated data to provide scheduling/decision solutions for handling their daily operations. Given the nature of shop floor operating in real-time, these average-based scheduling systems cannot be fully executed since unexpected events will almost always occur such as rush orders, design changes, machine breakdowns, defective parts, and delivery delays etc. Currently, shop-floor responds to unexpected events via manually scheduling or by Excel, which leads to poor predictability and visibility of performance, slow response to uncertainties and market changes, and low efficiency of their production and supply chain systems.

    In this talk, Manufacturing Execution Optimization (MEO) technologies developed by Dr. Shi and her team will be presented. MEO aims to bridge the gap between the top-level management data typically from ERP systems and the shop-floor operations. By establishing top floor to shop floor communication, manufacturing firms will be able to significantly improve their production and supply chain efficiency while achieving a faster response to changes and disturbances in the most time-optimal manner. MEO is developed based on Nested Partitions (NP) optimization framework. The coordination nature of the NP framework provides an efficient and effective platform for information sharing and exchange in real time. In this talk, several simulation optimization methods based on NP framework will also be discussed and a case study will be presented.

Brief  Introduction:

    Leyuan Shi is the Professor in the Department of Industrial and Systems Engineering at University of Wisconsin-Madison, also the founding chair of the Department of Industrial Engineering and Management at Peking University of China. She received her Ph.D. in Applied Mathematics from Harvard University in 1992. Her research interests include simulation modeling and large-scale optimization with applications to operational planning and scheduling and digital supply chain management. She has developed a novel optimization framework, the Nested Partitions Method that has been applied to many large-scale and complex systems optimization problems. Her research work has been funded by NSF, NSFC, NIH, AFSOR, ONR,MSOT, State of Wisconsin, and many private industrial companies. Her research work has been published on journals such as Operations Research, Management Science, JDEDS, IIE Trans. and IEEE Trans. She is currently serving as Editor for IEEE Trans on Automation Science and Engineering. She served on the editorial board for Manufacturing & Service Operations Management and INFORMS Journal on Computing. She was General Chair, co-Chair, and program committee for many national and international conferences. She is also one of the inventors for a set of digital management systems including Manufacturing Execution Optimization (MEO), Maintenance Repair & Overhaul Optimization (MRO2), and Dynamic Manufacturing Critical-Path Time (DMCT). She is the recipient of the Vilas Associate Award and IEEE fellow.