University of Chinese Academy of Sciences, China
Title: Joint optimization
of condition-based maintenance and inventory control for a k-out-of-n:F
system of multi-state degrading components
Abstract: Maintaining a system of multiple degrading components requires having a supply of spare components to replace deteriorated ones in a timely manner. This work considers the joint optimization of periodic condition-based replacement of components and spare-part inventory control for a k-out-of-n:F system of non-repairable degrading components under various system statuses. We model the maintenance and inventory policy using the number of components in each discretized degradation state rather than the traditional approach, which uses the state of each component. The approach considerably reduces the solution space. Based on that, we address the joint optimization using Markov decision process along with dynamic programming and analyze the complexity of the algorithm. Sensitivity analysis is conducted on the length of inspection interval, system downtime cost, and inventory holding cost.
Biodata: Xiaoyan Zhu is a Professor in School of Economics and Management at University of Chinese Academy of Sciences, Beijing. She is Council Chair of Technique Committee on System Reliability, Systems Engineering Society of China. Her research interests are in system reliability and maintenance optimization, operations research, and supply chain and inventory management. She published about 50 journal papers in the SCI-indexed top journals such as IISE Transactions, European Journal of Operations Research, IEEE Transactions on Reliability, Transportation Research Part E: Logistics and Transportation Review and so on. She coauthored book “Importance Measures in Reliability, Risk, and Optimization: Principles and Applications” published by John Wiley & Sons. She ever won IIE Best Paper Prize in Quality Control & Reliability Engineering. She serves as Associate Editor of IISE Transactions. (More)
University of Arkansas, USA
Biodata: Dr. Haitao Liao is John and Mary Lib White
Endowed Systems Integration Chair and Professor in the Department of Industrial
Engineering at the University of Arkansas – Fayetteville.
He received a Ph.D. degree in Industrial and Systems Engineering from Rutgers University in 2004. He also earned M.S. degrees in Industrial Engineering and Statistics from Rutgers University, and a B.S. degree in Electrical Engineering from Beijing Institute of Technology. Right before joining Arkansas in 2015, he was on the SIE faculty at University of Arizona.
His research interests include: (1) reliability models, (2) maintenance and service logistics, (3) fault diagnostics and prognostics, (4) applied operations research, and (5) probabilistic risk assessment. His research has been sponsored by the National Science Foundation, Department of Energy, Nuclear Regulatory Commission, Oak Ridge National Laboratory, and industry. The findings of his group have been published in IISE Transactions, European Journal of Operational Research, Naval Research Logistics, IEEE Transactions on Reliability, IEEE Transactions on Cybernetics, IEEE Transactions on Automation Science and Engineering, Annals of Operations Research, The Engineering Economist, and Reliability Engineering & System Safety, etc. (More)
Arizona State University, USA
Biodata: Rong Pan received his doctorate in industrial engineering from the Pennsylvania State University in 2002. Prior to coming to the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University in 2006, he was an assistant professor of industrial engineering at the University of Texas at El Paso. His research interests include quality and reliability engineering, design of experiments, time series analysis, and statistical learning theory. He has published in Journal of Quality Technology, IEEE Transactions on Reliability, IIE Transactions on Quality and Reliability Engineering, Quality and Reliability Engineering International, among others. His current projects are supported by National Science Foundation (NSF). Science Foundation of Arizona (SFAz), Arizona Department of Transportation (ADOT) and U.S. Department of Education (DoEd). (More)
University of Kent, UK
Title: some recent extensions of the geometric process and their applications
Abstract: The geometric process (GP) has found applications in various areas including optimisation of maintenance policies, optimisation of electricity price and modelling epidemic disease data since its introduction in 1988. It also presents some drawbacks that may present it from wider applications. Such drawbacks include its assumption of the independence and its monotonicity property. This talk will discuss some recent development on the GP and investigates its possible applications in reliability engineering.
Biodata: Professor Shaomin Wu is currently Programme Director for the MSc Business Analytics and coordinates the Student Implant Scheme. He received his PhD and MSc in Applied Statistics. Before moving to the University of Kent, he was a senior lecturer in risk and decision analysis at Cranfield University. He was a research fellow in machine learning at Bristol University. Shaomin has six years’ industry experience. He was a senior data analyst in a company in Shanghai after his PhD graduation and a senior data analyst in a credit card company in the UK. Shaomin serves on the editorial boards of six journals including: IISE Transactions, Reliability Engineering and System Safety, IMA Journal of Management Mathematics. He has co-chaired 4 international conferences, has been invited to act as scientific committee members by more than 25 international conferences, has edited 5 journal special issues, and has published more than 90 papers in scholarly journals. Shaomin was honoured to be within the top 2% Ranking of Scientists in the World by Stanford University. (More)
Texas State University, USA
Title: Industry Best Practices for Prognostics and Health
Management: A Case Study of Semiconductor Test Equipment
Abstract: In this talk we discuss a practical approach to monitoring the degradation of automated test equipment (ATE) and further predict its remaining useful life based on the built-in self-test data. ATE is a high-end electronics system widely used to test semiconductor devices in packaging and final testing. The system is usually configured with dozens of printed circuit boards, and each board itself is comprised of thousands of micro-electronics components. A board is routinely checked by the built-in self-test program to ensure the key performance measures satisfy the pre-defined reliability criteria. The degradation precursor, characterized by voltage, current, frequency or timing signals, is modeled as a non-stationary Gaussian process with time-varying mean and variance. Hypothesis testing is applied to examine the degradation trend for the mean and the variance, from which different types of degradation paths will be extrapolated. A hybrid method that combines machine learning and time series model is proposed to forecast the system remaining useful life. A case study drawn from automated test equipment is used to demonstrate the applicability and the performance of the proposed method.
Tsinghua University, China
Biodata: Dr. Yanfu Li is currently a full professor at IE department of Tsinghua University. He was a full professor from Jan 2016 to Aug 2016 and an assistant professor from Jan 2011 to Dec 2015, in Laboratory of Industrial Engineering at CentraleSupélec, University of Paris-Saclay, France. His current research interests include RAMS (reliability, availability, maintainability, safety) assessment and optimization with the applications onto energy systems, computing systems, transportation systems, etc. He has led/participated in several projects supported by EU, France and USA government funding bodies and enterprises. He is a co-author of over 80 publications, on international journals, conference proceedings and books. He is a senior member of IEEE and a member of INFORMS and SRA. (More)
Beihang University, China
李大庆，北京航空航天大学研究员、博导、首届校务委员会委员。2018年获国家优秀青年科学基金。围绕复杂系统可靠性和弹性管理，在PNAS、Nature Physics、Nature Communications、RESS等国际著名期刊上发表了研究成果，国家自然科学基金委、中国日报、科技日报等专文报道。主持了国自然、装发预研等科研项目。获得国防科技进步一等奖、航空学会技术发明一等奖。担任了管理科学与工程学会理事，“双法”研究会高教分会副理事长，系统工程学会系统可靠性专委会常务理事，中国智能交通协会青专委专家。(More)
Suk Joo Bae
Hanyang University, korea
Title: Reliability Analysis and Maintenance Optimization
for Complex Repairable Systems
Abstract: Reliability is an important consideration issue during the development of a variety of complex systems, e.g., automobiles, airplanes, and power plants, which ensures that their performances are maintained over a specified period of time under specific use environments. As technology evolves, system complexity increases and reliability evaluation for such systems remains an important area of research and has attracted the attention of system engineers. Once, the system has launched and used in the field, failure data or maintenance data are collected so improvements can be performed to maximize system’s availability or minimize operation cost. To meet this end, maintenance modelling aims to optimally balance between the cost of maintenance and the reliability of complex systems. It provides cost-driven mathematical basis to make a decision to keep the system of interest sustained at a desired level. In this talk, recent developments for reliability analysis and decision making in maintenance of complex repairable systems are presented for safe, reliable and effective operation of such systems. In particular, the modeling approaches for repairable systems are based mainly on the nonhomogeneous Poisson process and frailty model under the Bayesian framework. Finally, I present real-world application studies based on the proposed modeling and maintenance optimization ideas.
Biodata: SUK JOO BAE (Member, IEEE) is a professor in the Department of Industrial Engineering at Hanyang University, Seoul, South Korea. He received his PhD degree from the School of Industrial and Systems Engineering at the Georgia Institute of Technology in 2003. He served as a reliability engineer at Samsung SDI, Korea from 1996 to 1999. His research interests are centered on reliability evaluation of light displays, nanodevices, and battery systems including fuel cells via accelerated life and degradation testing, fault diagnoses and prognostics for condition-based maintenance, and process monitoring for large-volumed on-line sensing data. He has published more than 60 papers in journals such as Technometrics, Journal of Quality Technology, IISE Transactions, Reliability Engineering & System Safety, and IEEE Transactions on Reliability. He was an Associate Editor in IEEE Transactions on Reliability from 2012 to 2020.
Texas Technology, USA
Interests: System reliability modeling and optimization, Maintenance
Optimization, Inventory Control, Operations Research, Personal Information,
Education: Ph.D., Industrial Engineering - University of Arkansas, 2009, M.S., Industrial Engineering - University of Arkansas, 2006, B.S., Industrial Engineering - Nanjing University of Aeronautics & Astronautics, China, 2003 (More)
University of Arkansas, USA
Biodata: Dr. Xiao Liu is an Assistant Professor at the Department of Industrial Engineering, University of Arkansas. Before that, he was a Research Staff Member (RSM) at IBM Thomas J. Watson Research Center, Yorktown Heights, New York (2015~2017), and IBM Smarter Cities Research Collaboratory Singapore (2012~2015). From 2013 to 2016, he served as an Adjunct Assistant Professor at the Department of Industrial and Systems Engineering, National University of Singapore. Dr. Liu's research focuses on Physics-Informed and Domain-Aware Data-Driven Methodologies for Engineering Applications. (More)
University of Alberta, Canada
Biodata: Dr. Tian’s research is focused on Prognostics, Pipeline integrity management, Reliability, Condition based maintenance, Renewable energy systems, Condition monitoring, Signal processing, and Finite element modeling. His research papers have appeared in IEEE Transactions on Reliability, Mechanical Systems and Signal Processing, Renewable Energy, and IIE Transactions, among others. He received the Best Paper Award of “Quality Control and Reliability” in the 2005 IIE Industrial Engineering Research Conference. He is also the recipient of the 2011 Petro-Canada Young Innovator Award (Technology, Industry, and the Environment). (More)
Commercial Aircraft Corporation of China Ltd, China
Biodata: English coming
Delft University of Technology, Netherland
Biodata: Education: PhD in
Industrial Systems Engineering and Management, National University of Singapore,
Oct 2017, BEng in Industrial Engineering & Management, Shanghai Jiao Tong
University, China, Jun 2013, BEco in Finance, Shanghai Jiao Tong University,
China, Jun 2013
Research Interests: Data Analysis: applications in healthcare systems, manufacturing processes and environments, Statistical Inference: uncertainty quantification, robust estimation, Bayesian inference, Reliability Engineering: survival analysis, accelerated testing and degradation modelling (More)
Biodata: Dr. Tongdan Jin is a professor of Texas State University, and serves as program coordinator of Industrial Engineering. Prior to academia, he had 5-year reliability management experience in Teradyne Inc., Boston. His research is cross-disciplinary and resides at the interface of operations management, renewable energy, reliability for new product introduction, and supply chain sustainability. He is interested in tackling both fundamental and practical problems arising from manufacturing, services, transportation, energy, economics, and finance sectors. His research has been sponsored by National Science Foundation, the U.S. Department of Agriculture, and the U.S. Department of Education. He is the author of the new book “Reliability Engineering and Services” published by Wiley in 2019. He garnered several best paper awards, including the Evans-McElroy best paper in 2014 Reliability and Maintainability Symposium. He serves as an associate editor of IISE transactions. (More)
University of Strathclyde, UK
Title: A finite-horizon condition-based maintenance policy
for a two-unit system with dependent degradation processes
Abstract: Traditional condition-based maintenance policies are evaluated under the assumption of infinite horizon, which, however, fails to meet many real scenarios, since a machine or equipment will usually be abandoned after running specific periods. In this study, we develop a condition-based maintenance model for degrading systems within a finite operating horizon. In addition, different from most existing studies that focus on a single-unit system, we consider a system with two heterogeneous components. The components are subject to dependent degradation processes, characterized by a bivariate Gamma process. Periodic inspection is performed upon the system and the components are preventively replaced when their degradation levels at inspection exceed the preventive replacement thresholds. We formulate the maintenance problem as a Markov decision process (MDP) and employ dynamic programming for the calculation purpose. The optimal maintenance policy is achieved via minimizing the expected maintenance cost. We explore the structure property of the optimal maintenance policy and obtain the boundaries for various maintenance actions. Unlike the infinite horizon which leads to a stationary maintenance policy, for the finite horizon, the optimal decision is non-stationary, which indicates that the optimal maintenance actions vary at each inspection epoch. Finally, a numerical example is performed to illustrate the proposed model.
Biodata: I am a lecturer in the Department of Management Science. Before that I was working as a Postdoctoral Fellow in University of Waterloo, Canada. I received the B.S. degree in automation from Zhejiang University, China, and the Ph.D. degree in industrial engineering from City University of Hong Kong, Hong Kong. My research interests include risk analysis, reliability and maintenance modeling, decision making under uncertainty, and data analysis. (More)
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