Prof. TANG Loon Ching
National University of Singapore, Singapore
TANG Loon Ching is Professor at the Department of Industrial & Systems Engineering (ISE) in National University of Singapore (NUS). He is currently
Director of Temasek Defence Systems Institute and a member of the Advisory Board of the Singapore Innovation and Productivity Institute. He has served as
the Head of ISE Department from 2008-2015.
LC Tang obtained his PhD in the field of operations research from Cornell University under the NUS Overseas Scholarship. He has published widely in many
leading international journals in the field of IEOM. He was presented the IIE Transactions 2010 Best Application Paper Award and the prestigious Ralph A.
Evans/P.K. McElroy Awards for the best paper presented at 2011 Reliability and Maintainability Symposium. The latter is the first time in its 58-year history
that it went to authors affiliated to an Asian University. He is named 2014 IEOM Outstanding Educator by the IEOM Forum and is elected as the Fellow of
International Society of Engineering Asset Management.
Dr Tang is the Editor of Quality and Reliability Engineering International since January 2016 and has been on the editorial review board of the Journal of
Quality Technology, the flagship journal of American Society for Quality, since 2006, among others. He is the main author of the book: Six Sigma: Advanced
Tools for Black Belts and Master Black Belts (which won the inaugural Masing Book Prize by International Academy of Quality); and a co-author of Markov-
Modulated Processes and Semiregenerative phenomena.
Speech Title: Systems Resilience: A Unifying Framework and Associated Statistical Analysis
Abstract: There has been substantial interest the study of systems resilience in recent years as failures of subsystems and major performance loss at system level seem inevitable in the face of various threats such as terrorism and global warming In particular, 911 has heightened security concerns leading to the study of resilience of critical infrastructures. Global warming has intensified studies on issues related to sustainability; of which resilience of energy systems has been a key subject of interest. It is also generally recognized that global warming has led to extreme weather conditions and has induced more natural disasters that pose threats and disruptions to critical infrastructures and supply chain systems. At the confluence of these studies, it calls for a comprehensive treatment and definition of resilience to cater for its future evolution and applications in many diverse domains. In particular, a set of comprehensive and yet actionable metrics should be developed to measure resilience. In this presentation, a review of the current state of resilience research and a unifying framework to quantify resilience will be discussed. This is followed by more in-depth discussions on whether systems have become more resilience by learning from past disruptions and associated recovery processes. Some findings using existing data will be presented together with a proposed method to detect "learning" and possible system re-configuration after major disruptions.
Prof. Enrico ZIO
Chair on Systems Science and the Energetic Challenger
EDF Fondation, CentraleSupélec, Paris
Energy Department, Politecnico di Milano, Italy
Enrico Zio (BSc in
nuclear engng., Politecnico di Milano, 1991; MSc in
mechanical engng., UCLA, 1995; PhD, in nuclear engng.,
Politecnico di Milano, 1995; PhD, in nuclear engng.,
MIT, 1998) is Director of the Chair in Complex Systems
and the Energetic Challenge of the European Foundation
for New Energy of Electricite’ de France (EDF) at Ecole
Centrale Paris and Supelec, full professor, President
and Rector's delegate of the Alumni Association and
past-Director of the Graduate School at Politecnico di
Milano, adjunct professor at University of Stavanger. He
is the Chairman of the European Safety and Reliability
Association ESRA, member of the scientific committee of
the Accidental Risks Department of the French National
Institute for Industrial Environment and Risks, member
of the Korean Nuclear society and China Prognostics and
Health Management society, and past-Chairman of the
Italian Chapter of the IEEE Reliability Society. He is
serving as Associate Editor of IEEE Transactions on
Reliability and as editorial board member in various
international scientific journals, among which
Reliability Engineering and System Safety, Journal of
Risk and Reliability, International Journal of
Performability Engineering, Environment, Systems and
Engineering, International Journal of Computational
Intelligence Systems. He has functioned as Scientific
Chairman of three International Conferences and as
Associate General Chairman of two others. His research
focuses on the characterization and modeling of the
failure/repair/maintenance behavior of components,
complex systems and critical infrastructures for the
study of their reliability, availability,
maintainability, prognostics, safety, vulnerability and
security, mostly using a computational approach based on
advanced Monte Carlo simulation methods, soft computing
techniques and optimization heuristics. He is author or
co-author of five international books and more than 170
papers on international journals.
Speech Title: Risk Science and Engineering in Industry 4.0.
Abstract: As the digital, physical and human worlds continue to integrate, we experience a deep transformation in industry, which far-reaches into our lives. The 4th industrial revolution, the internet of things and big data, the industrial internet, are changing the way we design, manufacture, provide products and services. This is creating a complex network of things and people that are seamlessly connected and communicating. It is providing opportunities to make productions systems more efficient and faster, and more flexible and resilient the complex supply chains and distribution networks that tie the global economy.
In this fast-pace changing environment, the attributes related to the reliability and safety of components and systems continue to play a fundamental role for industry. The innovations that are being developed have high potential of increased wellbeing and benefits, rendering everything "better and smarter", but also generate new and unknown failure mechanisms, new and unknown functional and structural dependencies, and eventually new and unknown hazards and risks. On the other hand, the advancements in knowledge, methods and techniques, the increase in information sharing and data availability, offer new opportunities of analysis and assessment for modern system engineering and industry. Then, a new "revolution" is in the making for addressing the challenges brought about by the new and evolved, complex systems (and systems of systems), and the innovations therein; this calls for and, at the same time, drives the advancements of new methods and tools of complex system analysis, and the extension of their applications, based on the increased knowledge, information and data (KID) available, which can improve our system behavior understanding capability in support to decision making.
In this seminar, I will consider the above context and address some challenges and opportunities, focusing on desired attributes like reliability, safety, resilience, flexibility.
Prof. Emanuele Borgonovo
Department of Decision Sciences, Bocconi University Editor, European Journal of Operational Research Director, Bachelor in Economics, Management and Computer Science (BEMACS) Director, Management Science Laboratory, SDA Bocconi Business School, Milano (ITALY)
Full Professor at the Department of Decision Sciences of Bocconi University, and director of the new Bachelor in Economics, Management and Computer Science of Bocconi University. He has been director of the ELEUSI Research Center from 2008 to 2012. He holds a Ph.D. in Probabilistic Risk Assessment from the Massachusetts Institute of Technology, conducted under the supervision of Prof. George Apostolakis. He is the recipient of several national and international awards, among which the 2017 Chinese Academy of Sciences President's Visiting Professor Fellowship, the 2015 IBM Faculty Award, the honorary memberships of ", The Scientific Research Society of North America", "The Honorary Society of the American Nuclear Society," the "McCormack fellowship of the Westinghouse Corporation," as well as several best paper and excellence in refereeing awards. In 2016 he has been elected member of the Council of the Decision Analysis Society of INFORMS, and since 2013 he is the Co-Chair of the Committee on Uncertainty Analysis of the European Safety and Reliability Association.
He is co-editor-in-chief of the European Journal of Operational Research and advisor of Springer International Series in Management Science and Operations Research. He is also a member of the editorial boards of Risk Analysis, Reliability Engineering and System Safety, the Journal of Business Logistics, the International Journal of Mathematics in Operational Research, The International Journal of Risk Management. He has published more than 100 scientific articles, with works appeared in journals such as Journal of the Royal Statistical Society B, Management Science, Operations Research, Nature Climate Change, Risk Analysis, European Journal of Operational Research, Reliability Engineering & System Safety, Water Resources Research, etc..
His research interests concern quantitative methods for Sensitivity Analysis, Decision Analysis and Risk Analysis. In his works, he has introduced new reliability importance measures and new techniques in the field of sensitivity analysis of complex computer codes. He is author of the book: "Sensitivity Analysis: An Introduction for the Management Scientist", http://www.springer.com/gp/book/9783319522579.
Speech Title: Reliability Importance Measures
Abstract: Importance measures are an essential tool in reliability analysis. They provide guidance in applications ranging from redundancy allocation, to maintenance optimization. We present an overview of importance measures, with focus on two notions that characterize their definition: the notion of criticality and of time consistency. The notion of criticality plays a role in the construction of the Birnbaum and of the Barlow-Proschan importance measures. The notion of time consistency is a recent notion that we discuss in association with a new reliability importance measure based on the effect of component failures on the system mean time to failure. We also highlight the distinction between time dependent and time independent importance measures. We conclude with future research perspectives.
Prof. Yi Ding, Zhejiang University, China
Yi Ding is a Professor in the College of Electrical Engineering, Zhejiang University (ZJU), also a scholar of Thousand Talent Program for Young Outstanding Scientists of China. Before he joined in ZJU, he was an Associate Professor in Technical University of Denmark (DTU). He also held academic positions in University of Alberta, Canada and Nanyang Technological University, Singapore. He was a Consultant as Energy Economist for Asian Development Bank in 2010. He is editorial member of international journals of Electric Power System Research and Journal of Modern Power Systems and Clean Energy, respectively. He is also a guest editor for the special section of IEEE Trans. on Power Systems. He published more than 80 academic papers in some prestigious journals and conferences and coauthored one book devoted to multi-state system risk evaluation and optimization. His research areas include power system planning and reliability evaluation, smart grid and complex system risk assessment. He was a task leader of EU FP7 project “EcoGrid EU” – A Prototype for European Smart Grids for designing a real time market for integrating distributed energy resources and small end-consumers for future power systems. Since he joined in ZJU, he has been the PI and Co-PI of several NSFC projects focusing on reliability evaluation of smart grid. He has consulted for Development Research Center of the State Council (DRC) for planning future Chicness energy systems.
Speech Title: Reliability of Electric Power Systems with High Penetration of Smart Demand Aggregation
Abstract: With the development of information and communication technologies, smart and flexible demand has been aggregated and become more and more popular to participate in the two-way interaction between power generation and consumption. However, the growing proportion of flexible loads has made the reliability of smart grids different from that of traditional power systems. In this talk we discuss reliability of such kind of complicated system and its impact on power system security.
Prof. Jin Wang, Liverpool John Moores University, UK
Prof. Jin Wang is Director of Liverpool Logistics, Offshore and Marine (LOOM) Research Institute at Liverpool John Moores University (LJMU), UK. He is
also Associate Dean (Scholarship and Research) of Faculty of Engineering and Technology at LJMU. Following just less than 5 years’ research as a
Research Associate at Newcastle University, UK, he joined LJMU as a lecturer in 1995, and was promoted as Reader in Marine Engineering and Professor
of Marine Technology in 1999 and 2002 respectively. He has been involved in safety and reliability research of large engineering systems with significant
financial support from the UK research councils, EU, etc. He has successfully completed supervision of more than 50 doctoral/postdoctoral researchers. His
research areas are in risk-based design and operation of large maritime engineering systems such as ships and offshore installations. Prof. Wang’s
publications include two research monographs and more than 120 SCI cited journal papers (h-index 26, more than 2,000 citations in Web of Science). He
has won several research awards including two Denny Medals from the Institute of Marine Engineering, Science and Technology (IMarEST). He has led two
UK research council funded projects as PI and two EU funded projects (REFERENCE and RESET) as the coordinator. Prof. Wang was a sub-panel member
(sub-panel 12: Aeronautical, Mechanical, Chemical and Manufacturing Engineering) in the Research Excellence Framework 2014 for assessing the quality of
research in the UK’s higher education institutions.
Speech Title: Evidential Reasoning Rule and its Application to Safety and Reliability Assessment
Abtract: This presentation focuses on an evidential reasoning (ER) rule and its application to safety and reliability assessment in several areas. ER takes Dempster's rule of the Dempster-Shafer theory of evidence as a special case. It enhances Dempster's rule by identifying its missing parts for combination of highly or completely conflicting evidence. ER provides analysts with a generic evidence-based multi-criteria decision analysis (MCDA) approach for dealing with problems having both quantitative and qualitative criteria under various uncertainties. It is based on an evidence-based reasoning principle that if more pieces of evidence support a hypothesis, then it is more likely that the hypothesis is true. It has been used to support decision analysis, reliability assessment, and safety assessment over the past two decades. The application areas of ER include risk-based offshore system design, safety requirement specification in software design, port security assessment, pipeline leakage detection, emission control in ship propulsion, vessel selection for chartering, ship collision analysis and human error analysis. In addition, ER also has potential to be used in areas such as organisational quality self-assessment, performance assessment for SMEs, innovation capability assessment, R&D project performance assessment, customer satisfaction survey & assessment, company supplier selection and environmental impact assessment. Examples will be used in this presentation to demonstrate the usefulness and potential of ER with the help of the Intelligent Decision System (IDS) software.
Prof. Dr. Francesca Saglietti, University of Erlangen-Nuremberg, Germany
Francesca Saglietti heads the Chair of Software Engineering at the University of Erlangen-Nuremberg, Germany. Before being appointed full professor in 2001, she had gained over 16 years experience in industrial research focused on the evaluation of software reliability and fault tolerance for safety-critical applications. She received her academic degrees in mathematics and computer science (diploma, doctoral and habilitation degrees) from the Technical University of Munich. Presently, she serves as the chair of the European Workshop on Industrial Computer Systems, Technical Committee on Reliability, Safety and Security (EWICS TC7)..
Speech Title: Testing for Fault Detection and for Reliability Assessment: from Classical Software Engineering to Robot Cooperation
Abstract: Verifying the appropriate cooperative behaviour of autonomous robotic agents poses analogous challenges as classical software testing: in fact, depending on the specific target to be pursued, both domains require systematic approaches to ensure an adequate coverage of behavioural multiplicity or probabilistic approaches to capture the operational evidence gained and to determine to which extent it can be taken to endorse reliability evaluation. This analogy has been investigated in two European projects within the ARTEMIS programme. The talk will report on experiences, insights and results gained hereby.
Prof. Jun Yang, Beihang University, China
Jun Yang is a professor of School of Reliability and Systems Engineering, Beihang University, China. He got his Ph.D. from Academy of Mathematics and Systems Science, Chinese Academy of Sciences(CAS) in 2006. His research fields are mainly on reliability modelling and statistics, statistical process control and survey sampling. Now, he has published three books and more than 70 papers，included 26+ papers indexed by SCI. Meanwhile, he presided 2 Grants from the National Natural Science Foundation of China, and he got 3 Ministerial and Provincial-Level Science and Technology Awards in China. His commonly used email address is email@example.com.
Speech Title: Research on the Reliability Growth of High Power Laser Based on the Uniform Design Method
Abstract: The exorbitant operating temperature is the main factor to limit the reliability of high power laser. To improve the reliability of high power laser, optimizations of heat sink that installed in the device are deserved to research. Total thermal resistance (TTR) is the main index to weigh the heat transfer capability of the heat sink. With the goal of achieving the lowest TTR of the heat sink and modeling the relationship between the parameters and TTR, the structure of the heat sink is illustrated in detail. Then, we schedule the experimental plan for the heat sink based on the uniform design (UD), where the width of heat sink, height of pin-fins, pressure drop of the fluid and power of the heat source are defined as the control parameters. According to the experimental data, a regression model is obtained to describe the relation between the control parameters and TTR, and then the optimal combination of the control parameters is determined. Verification tests show the effectivity of the optimal parameters and the validity of the regression model. Furthermore, for evaluating the improvement of the reliability of high power laser that contributed by the optimized heat sinks, some accelerated life tests (ALT) are conducted. Results show that the reliability of high power lasers with optimized heat sink is superior to the original ones.
Prof. Xiaoping Du, Missouri University of Science and Technology,USA
Dr. Xiaoping Du is Curator’s Distinguished Professor at the Missouri University of Science and Technology. As an engineer, researcher, and educator in reliability-based design, he has been involved in many projects in optimization, structural reliability, and reliability engineering. As Principal Investigator (PI), he received seven grants from the National Science Foundation (NSF) and also served as PI and Co-PI for many other research projects on reliability. He has published over 100 journal and conference papers. He is currently serving as Associate Editor of ASME Journal of Mechanical Design, Review Editor of Structural and Multidisciplinary Optimization, and Associate Editor of IISE (IIE) Transactions; he is also serving on the Editorial Boards for other three professional journals. He is Fellow of ASME and a recipient of Governor's Award for Excellence in Education.
Speech Title: System Reliability Prediction with Outsourced Components
Abstract: It is a challenging task to accurately predict the system reliability of products whose components are outsourced to outside suppliers. Outsourcing is a common practice nowadays because of lower operational and labor costs. The detailed designs of outsourced components, however, are mostly proprietary to component designers and are black boxes to system designers. This poses a great challenge for system designers to estimate the system reliability during the system design stage. This presentation discusses feasibility studies on exploring possible ways for system designers to predict the system reliability without revealing proprietary details of outsourced components. As a result, engineers could make more reliable, safer, and cheaper products, thereby increasing competitiveness and improving quality of life. Examples are provided to show the preliminary results..
Assoc. Prof. Rong Pan, Arizona State University, USA
Rong Pan is an Associate Professor of Industrial Engineering in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He received his Ph.D. degree in Industrial Engineering from Penn State University in 2002. His research interests include failure time data analysis, system reliability, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. His research has been supported by NSF, DOE, Arizona Science Foundation, Air Force Research Lab, etc. He has published over 50 journal papers and many more refereed conference papers. He was the recipient of 2008 and 2011 Stan Ofsthum Awards and 2015 William A. Golomski Award. His papers won the Best Reliability Paper Award of Quality Engineering in 2012 and 2013. Rong Pan is a senior member of ASQ and IIE, and a member of SRE, IEEE and INFORMS. He serves on the editorial boards of Journal of Quality Technology and Quality Engineering..
Speech Title: Design and Analysis of Accelerated Life Tests with Constrained Randomization
Abstract: Accelerated life tests (ALTs) often involve experimental protocols such as subsampling or random block, which make the complete randomization of experiments impossible. As a result, lifetime data collected should be modeled by a random effect model to account for data heterogeneity. In this talk, we will discuss a generalized linear mixed model (GLMM) approach for the design and analysis of ALTs with a clustered structure. The GLMM approach provides a flexible way to model censored failure time data with random effects. Particularly, for Weibull failure time distribution, we describe an iterative procedure for the model parameters estimation and derive the asymptotic variance-covariance matrix using the approximated likelihood function.
Prof. Michael Todinov, Oxford Brookes University, UK
Prof. Michael Todorov Todinov has a background in Mechanical engineering, Applied mathematics and Computer science. From the University of Birmingham he holds a PhD related to mathematical modelling and a higher doctorate Doctor of Engineering (DEng) which is the engineering equivalent of Doctor of Science (DSc) in the area of new probabilistic concepts and models in Engineering. M.Todinov's name is associated with developing a theoretical and computational framework for analysis and optimisation of repairable flow networks and networks with disturbed flows. In 2013, he published the first book covering this important area of research. M.Todinov's name is also associated with creating the foundations of the risk-based reliability analysis (driven by the cost of failure) and publishing the first book covering this research area. In the area of reliability and risk, M.Todinov published 3 research monographs and a significant number of research papers most of which are sole-authored. M.Todinov pioneered research on reliability dependent on the relative configurations of random variables and on new methods and principles for reliability improvement and risk reduction. In 2017, he received the prestigious IMechE award for significant and sustained contributions in the area of reducing risk in Mechanical engineering. A sample of M.Todinov's results includes: creating the theory of closed and dominated parasitic flow loops in real networks; the proof that the Weibull distribution is an incorrect model for the distribution of breaking strength of materials; the variance upper bound theorem regarding the exact upper bound of properties from sampling multiple sources; a general equation for the probability of failure of brittle components with complex shape; the formulation and proof of the necessary and sufficient conditions for the Palmgren-Miner rule and Scheil's additivity rule; deriving the correct alternative of the Johnson-Mehl-Avrami-Kolmogorov equation and developing the theory of stochastic separation of risk-critical random events on a time interval. M.Todinov's research has been funded by research councils, the automotive industry, the nuclear industry and the oil and gas industry.
Speech Title: Stochastic Pruning and Its Application for Fast Estimation of the Production Availability of Complex Repairable Networks
Abstract: In this talk, I will introduce the powerful stochastic pruning method for analysing the availability of complex repairable networks, whose component failures follow a homogeneous Poisson process on a specified time interval. The presentation will demonstrate that that the key performance measure production availability is a property of the stochastically pruned network. The stochastic pruning method is based on a key result, recently obtained by the author, regarding the average total output of a repairable flow network. The average output over a specified operation time interval is given by the ratio of the expected momentary output of the stochastically pruned network, where the separate components are pruned with probabilities equal to their unavailabilities, and the maximum momentary output in the absence of component failures. The running time of the stochastic pruning algorithm is independent of the length of the operational interval and the failure frequencies of the components. This makes the stochastic pruning algorithm many orders of magnitudes faster than the best available discrete-event simulation algorithms for revealing the performance of repairable flow networks. The stochastic pruning method served as a basis for an ultra-fast solver for estimating the production availability of large repairable flow networks with complex topology. The solver has been embedded in a software tool with graphics user interface, by which a flow network topology is drawn on screen and the parameters characterising the edges and the nodes are specified. The software tool has been used to study the impact of the network topology on the network performance. In this talk, I will also demonstrate that two networks built with identical type and number of components may be characterised by very different production availabilities, because of slight differences in their topology.