报告内容摘要

A new modeling approach is being developed to optimally determine a system design structure and maintenance plan for applications with changing and uncertain future usage scenarios and stress variables. This research proposes a more detailed mathematical perspective in analyzing the uncertainty of actual system usages in future scenarios that take variations and uncertainties explicitly into consideration. The integrated development of cost-reliability optimization models and a system maintenance policy is a difficult and challenging problem, so often it is performed based on worst case, average or aggregate prediction of usage stress. The proposed system cost-reliability model involves optimization of a series-parallel system while specifically considering uncertainty of future usage scenarios. When systems are fielded in different future usage scenarios, the reliability of many components is subject to change due to the changing environment, stresses and operating conditions. To develop the models for applications where future stresses are not known specifically or there is unavoidable uncertainty, cost-reliability analysis for systems considering variations of stresses and operating conditions are developed. They provide an insightful mathematical representation of an integrated system optimization planning process that more directly represents the system reliability design process.

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, a two-stage stochastic model with recourse is formulated with a system cost, reliability and maintenance combined objective function to determine initial design structure and preventive maintenance intervals. 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 constructed to provide the model with timely and improved estimates to reflect the fielded data. In the fourth stage, a cost saving strategy is implemented to decide whether the current system design or a new or revised system design can provide sufficient cost saving beyond a threshold to justify design changes.

报告人简介

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, risk analysis, and multi-criteria optimization considering uncertainty. 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 also has over ten years of experience working for IIT Research Institute (IITRI), Rome NY, where he was a reliability analyst, project manager, and engineering group manager. In 1999, he was awarded a CAREER grant from NSF to develop new reliability optimization algorithms. In 2010, he was awarded a NSF grant to study the integration of quality and reliability models for evolving technologies. In 2008, he was an instructor at the System Reliability Workshop sponsored by the Chinese Academy of Sciences (CAS) and held at Beihang University, Beijing, China. He also has been funded by U.S. Navy, industry, and power utilities. He is a member of IIE and INFORMS.