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Abstract: Effective operation of critical systems requires reliable designs, that are optimally maintained, that also have effective plans to restore system functionality when extreme events suddenly trigger multiple failures. Cost effective design and maintenance or restoration planning policies can be achieved by combining the respective the decision making processes. Two related problems are presented. First, a modeling approach is presented to optimally and simultaneously design a multi-component system and determine a maintenance plan with uncertain future stress exposure. Second, given recent climatic extreme events (hurricanes, tsunamis), a modeling approach is described to combine system hardening and restoration plans for the electric power grid or other critical infrastructures. Traditionally, analytical models for system design and maintenance planning have been applied sequentially; however, this is potentially inefficient. These new models provide an integrated approach to make decisions considering the lifecycle cost of the system. Specifically considering the influence of uncertain future usage stresses or unlikely extreme events on system performance, the integrated redundancy allocation and maintenance planning problem is formulated as a two-stage stochastic programming model with recourse. The first stage decision variables determine the selection of component types and the number of components to be used in the system or hardening policies to improve reliability, and these decisions must be made before the uncertainty is revealed. The second-stage variables, involving a recourse function, are the preventive maintenance or restoration plan. The comparisons of the proposed integrated approach to traditional sequential method show advantages of the proposed model in cost saving.
Bio: David W. Coit�/strong> is a Professor in the Department of Industrial & Systems Engineering at Rutgers University, Piscataway, NJ, USA, and he is currently appointed to a 3-year position as a Visiting Professor at Tsinghua University, Beijing, China. He has also had visiting positions at King Mongkut’s University of Technology Thonburi (KMUTT), Bangkok, Thailand and University of Chinese Academy of Sciences (UCAS), Beijing, China. His current teaching and research involves system reliability modeling and optimization, and energy systems optimization. His research has been funded by U. S. National Science Foundation (NSF), U.S. Army, U.S. Navy, industry, and power utilities. He has over 120 published journal papers and over 90 peer-reviewed conference papers. He has been awarded several NSF grants, including a CAREER grant from NSF to develop new reliability optimization algorithms considering uncertainty. He was also the recipient of the P. K. McElroy award, Alain O. Plait award and William A. J. Golomski award for best papers and tutorials at the Reliability and Maintainability Symposium (RAMS). 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 an Associate Editor for IEEE Transactions on Reliability and Journal of Risk and Reliability, and previously for 15 years, he was a Department Editor for IISE Transactions.
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Abstract: Six Sigma has evolved from its origin as a programme for manufacturing excellence to other sectors that pursuing operational excellence. In this presentation, we shall give a historical account of this evolution, including Design for Six Sigma, that contributed greatly to the near perfect quality and reliability of products such as car, handphone and home appliances which we enjoy today. We then examine the challenges ahead in view of the on-going digital transformation across the entire industry and commercial sectors. In particular, we shall address the question of what lies ahead for LSS and how to close the productivity gap between service and manufacturing sectors.
Bio: Dr. Loon Ching TANG is currently professor of Department of Industrial Systems Engineering & Management at the National University of Singapore. He obtained his Ph.D degree from Cornell University in the field of Operations Research in 1992 and has published extensively in areas related to industrial engineering and operations research. He has been presented with a number of best paper awards including the IIE Transactions 2010 Best Application Paper Award and 2012 R.A. Evans/P.K. McElroy Award for the best paper at Annual RAMS. Prof Tang is the main author of the award-winning book: Six Sigma: Advanced Tools for Black Belts and Master Black Belts. Besides being active in the forefront of academic research, in the last 25 years, Prof Tang has served as a consultant for many organizations, such as the Ministry of Home Affair, Singapore Power Grid, Republic of Singapore Air Force, Seagate, HP, Phillips, etc, on a wide range of projects aimingat improving organizational and operations efficiency; especially through better management of engineering assets. He is currently a fellow of ISEAM, the Co-Editor-in-Chief of Quality & Reliability Engineering International, editorial review board member of Journal of Quality Technology and a member of the advisory board of the Singapore Innovation and Productivity Institute.
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Abstract: Various types and massive amounts of data are being collected in multiple industries. Such a big data proliferation has provided ample opportunities to develop more and better services, especially in manufacturing industries. For example, heavy equipment manufacturers monitor, diagnose, predict, and manage equipment health through prognostics and health management services using the data collected from their equipment. Consequently, equipment managers can cope with potential product breakdowns and maximize product availability for clients. Numerous companies in manufacturing industries have “servitized�their value propositions to address issues on product commoditization and sustainability. Such a transition involves intensive system data analytics, which transforms system data into useful information for the system stakeholders, leading to smart services. This talk proposes a framework of smart service development based on the analysis of data collected from a system in question. The framework is introduced using recent case studies in automobile, vessel, energy,
and healthcare industries. Observations and findings from the case studies would contribute to promoting and inspiring research on the development of smart services in various industries.
Bio: Prof. Kwang-Jae Kim is a Professor in the Department of Industrial and Management Engineering and the Director of Future City Open Innovation Center in Pohang University of Science and Technology (POSTECH), Korea. His current research interests include quality assurance in product and service design, smart service systems, and analytics-driven new service development. His work has been applied in various areas including semiconductor manufacturing, steel manufacturing, automobile design and manufacturing, healthcare and wellness, smart energy, smart transport, telematics, and ICT services. His research has been supported by National Research Foundation, Ministry of Science and Technology, Ministry of Knowledge Economy, Ministry of Health and Welfare, Ministry of Trade, Industry and Energy, Ministry of ICT of Korea, and various industrial companies including Samsung, LG, POSCO, Hyundai, SK Hynix, KT, IBM, and Microsoft. He is a member of the National Academy of Engineering of Korea, a fellow of APIEMS, and an advisory board member of INFORMS QSR. More details can be found at http://quality.postech.ac.kr/.
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Abstract: This talk will present and discuss the challenges and opportunities that data science and analytics face in the era of digital transformation, and the roles we play to drive such transformation. In particular, there is a big opportunity for industrial and business analytics, under the digital transformation paradigm, in order to further explore ways of creating value from data and big data. On research: I will update the recent progress in our Quality and Data Analytics Lab on change detection in heterogeneous data streams. On education: I will share the recent development of HKUST 2.0: a cross-disciplinary paradigm and a unique Information Hub in the Greater Bay Area.
Bio: Prof. Fugee Tsung is a Chair Professor and Acting Dean of the Information Hub, Guangzhou Campus, at the Hong Kong University of Science and Technology (HKUST). He is also the Director of the Quality and Data Analytics Lab, and former Editor-in-Chief of the Journal of Quality Technology (JQT). He has been elected Academician of the International Academy for Quality (IAQ), Fellow of the American Statistical Association (ASA), Fellow of the Institute of Industrial and Systems Engineers (IISE), Fellow of the American Society for Quality (ASQ), Elected Member of the International Statistical Institute(ISI), and Fellow of the Hong Kong Institution of Engineers (HKIE). He received both his PhD and MSc in industrial and operations engineering from the University of Michigan, Ann Arbor. He has authored over 100 refereed journal publications and also received IISE Transactions' Best Paper Award three times, in 2004, 2009, and 2018, respectively. His research interests include industrial big data and quality analytics.
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Abstract: Industrial systems have become enormously complex today. Although artificial intelligence and machine learning approaches have helped in various decision making and analysis of such system, there are many new and challenging issues that we need to pay attention to. In particular, how to perform safety and reliability analysis and manage such systems is a difficult question. In this talk, we will share some thoughts and experience from systems engineering perspective. In addition to the complexity of the system, AI applications rely heavily on the availability of large amount of data that may have additional problems such as data uncertainty and deterioration. Some possible approaches to address these issues will also be discussed.
Bio: Prof. M. Xie completed his undergraduate study at Royal Institute of Technology in Sweden and received an MSc in 1984. He later received his PhD from Linkoping University, Sweden, in 1987. He joined the National University of Singapore in 1991 and in May 2011, he moved to City University of Hong Kong as Chair professor of industrial engineering. Prof Xie has published over 300 journal papers and 8 books, including “Software Reliability Modelling�by World Scientific, “Statistical Models and Control Charts for High-Yield Processes�by Springer, “Advanced QFD Applications�by ASQ Quality Press. Prof Xie has supervised over 60 PhD students and they hold regular position in finance, industry and academia in different continents. Prof Xie is an elected fellow of IEEE since 2006.
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Abstract: The generalized Pareto distribution (GPD), introduced by Pickands, is widely used to model exceedances over thresholds. It is well known that inference for the GPD is a difficult problem since the moments exist only for the limited range of parameters and the GPD violates the classical regularity conditions in the maximum likelihood method. For parameter estimation, most existing methods do not perform satisfactorily for all range of parameters. Furthermore, the interval estimation and hypothesis tests have not been studied well in the literature. In this talk, we introduce a novel framework for inference for the GPD, which works successfully for all values of shape parameter. Specifically, a new method of parameter estimation for the GPD is constructed, and some asymptotic properties of the proposed estimators and related statistics are derived. Existence and uniqueness of the proposed estimates are also established. Based on the asymptotic properties of the proposed estimators and related statistics, new confidence intervals and hypothesis tests are developed. The performances of the proposed estimators of parameters, confidence intervals and hypothesis tests are then shown by Monte Carlo simulation and real data examples.
Bio: Prof. Hideki Nagatsuka is a Professor of the Department of Industrial and Systems Engineering, Chuo University, Japan. He received BE, ME and doctor‘s degree (Ph.D) from Chuo University in 1998, 2000 and 2004, respectively. His research interests include theoretical and applied statistics, reliability engineering and quality management. He is an Elected Memberof International Statistical Institute, an Associate Editor of the Lifetime Data Analysis and an Associate Editor of Communications in Statistics.