题目：Integrated System Design and Maintenance Optimization Considering Uncertainty of Future Usage Profiles
主讲人：Dave Coit 教授（Rutgers University Department of Industrial & Systems Engineering）
时间：2015年7月20 日上午10点
地点：主楼418
主讲人简介：
    David W. Coit is a Professor in the Department of Industrial & Systems Engineering at Rutgers University. His current research involves system reliability modeling and optimization, and electric power grid expansion optimization. His research has been funded by NSF, U.S. Army, U.S. Navy, industry and power utilities. He also has over ten years of experience working for IIT Research Institute, Rome NY, where he was a reliability analyst, project manager, and engineering group manager. He received a BS degree in mechanical engineering from Cornell University, an MBA from Rensselaer Polytechnic Institute, and MS and PhD in industrial engineering from the University of Pittsburgh. He is a member of IIE and INFORMS.
内容简介：
    For some system design and maintenance planning problems, it is necessary to make timely decisions although it is known that operating and usage stresses are fundamentally shifting or changing. A new modeling approach has been developed to optimally design a system structure and maintenance plan for applications exposed to distinct stresses under different and changing environments and usage conditions. A more detailed mathematical perspective is described to analyze the uncertainty of actual system usages in future scenarios that take variations and uncertainties explicitly into consideration. A four-stage optimization model is constructed to accommodate sequences of decisions over time with random future usage scenarios. In the first and second stage solved simultaneously, a two-stage stochastic model with recourse is formulated with a system cost, reliability and maintenance combined objective function to determine an initial design structure and preventive maintenance intervals or on-condition thresholds. In the third-stage, the system is fielded and data is collected and analyzed to update model parameters and coefficient estimates, and adaptive preventive maintenance optimization is performed. Bayesian posterior distributions are used to update the model reflect the fielded data. In the fourth stage, a cost saving strategy is implemented to decide whether the current system design or a revised system design can provide sufficient cost savings to justify design changes. Theoretical and simulated examples are solved to demonstrate the modeling approach and results.

（主办：管理科学与工程系）