2025 The 9th International Conference on System Reliability and Safety
Turin, Italy - November 26-28, 2025

Liudong Xing
University of Massachusetts, USA

Liudong Xing is currently a Commonwealth Professor and College of Engineering Director of Research at the University of Massachusetts Dartmouth. She received her Ph.D. degree in Electrical Engineering from the University of Virginia, Charlottesville in 2002. Her research focuses on reliability modeling and analysis of complex systems and networks. She has authored or co-authored over 320 journal articles and three monographs entitled “Reliability and Resilience in the Internet of Things”, “Binary Decision Diagrams and Extensions for System Reliability Analysis”, and “Dynamic System Reliability: Modeling and Analysis of Dynamic and Dependent Behaviors”. Prof. Xing has received multiple teaching and scholarly awards from her university and IEEE. She was also a co-recipient of eight best (student) paper awards at international conferences and journals. She currently serves as associate editor or editorial board member for Reliability Engineering & System Safety, IEEE Internet of Things Journal, IEEE Access (Reliability Society section), and other journals. She is an IEEE ComSoc Distinguished Lecturer for the class of 2024-2025 and a fellow of the International Society of Engineering Asset Management.
Speech Title: Reliability Challenges in the Internet of Things
 Abstract:
The Internet of Things (IoT) aims to create seamless connections between people and diverse objects, driving the transformation of our society toward greater efficiency, intelligence, and convenience, with potentially significant economic and environmental benefits. Over the past decade, IoT technology has advanced rapidly in various application domains, including smart healthcare, smart energy, smart manufacturing, smart agriculture, smart environmental monitoring, smart supply chains, and more. Given the critical nature of IoT applications, reliability is a vital requirement for deploying and operating robust IoT systems. Based on a layered IoT architecture, this talk will explore key reliability challenges in IoT systems, focusing on reliability modeling, analysis, and design issues, as well as solution methods for IoT perception technologies, data communications, support technologies, and applications. Several open research problems in IoT reliability will also be presented.

 

 

Yu Liu
University of Electronic Science and Technology of China, China

Yu Liu is the Director of University Undergraduate Teaching Affair Office at the University of Electronic Science and Technology of China. He is also the Full Professor and was the former Dean of the School of Mechanical and Electrical Engineering at the University of Electronic Science and Technology of China. He received his PhD degree in Mechatronics Engineering from the University of Electronic Science and Technology of China. He was a Visiting Pre-doctoral Fellow in the Department of Mechanical Engineering at Northwestern University, Evanston, U.S.A. from 2008 to 2010, and a Postdoctoral Research Fellow in the Department of Mechanical Engineering, at the University of Alberta, Edmonton, Canada from 2012 to 2013. He has published 3 Springer books and over 100 peer-reviewed papers in international journals, such as IEEE Transactions on Reliability, IISE Transactions, Naval Research Logistics, European Journal of Operational Research, Reliability Engineering and System Safety. He has been recognized as one of the Most Cited Chinese Researchers by Elsevier since 2016 and the World's Top 2% Scientists since 2020. He was a recipient of the Chang Jiang Scholars Program Distinguished Professors, National Science Fund for Excellent Young Scholars, IISE QCRE Teaching Award, the Youth Science and Technology Award of Operations Research Society of China, the Youth Science and Technology Award of Sichuan Province, and the HIWIN Doctoral Dissertation Award sponsored by Chinese Society of Mechanical Engineers and HIWIN Technologies Corporation. He serves as an Associate Editor of IISE Transactions, IEEE Transactions on Reliability, Engineering Optimization, Area Editor of Journal of Reliability Science and Engineering, and one of the editorial board members of Reliability Engineering and System Safety, Quality and Reliability Engineering International, Chinese Journal of Aeronautics, International Journal of Reliability, Quality and Safety Engineering. He also serves as the Elected President of the Reliability Committee of Operations Research Society of China and the Chair of IEEE Reliability Society Chengdu Section Chapter. He is an ISEAM Fellow.
Speech Title: Reinforcement Learning in Reliability and Maintenance Optimization
 Abstract:
Reinforcement learning is a critical paradigm in artificial intelligence that enables an agent to learn optimal behaviors through trial-and-error interactions with environments. It exhibits significant potential for solving complex decision-making problems in engineering scenarios and has been regarded as a transformative approach within the reliability and maintenance optimization community.
This talk will introduce the theoretical foundations of reinforcement learning, as well as its applicability and advancements in reliability and maintenance optimization. It will provide a comprehensive tutorial on applying reinforcement learning to address reliability and maintenance optimization problems, including Markov decision processes modeling, design and implementation of reinforcement learning algorithms, and case studies of engineering instances. Additionally, challenges and future directions associated with the application of reinforcement learning in reliability and maintenance optimization will be discussed.

 

 

 

Wei-Chang Yeh
Chair Professor, Integration & Collaboration LaboratoryDepartment of Industrial Engineering and Engineering Management, National Tsing Hua University

Yeh is currently a Chair Professor in the Department of Industrial Engineering and Engineering Management at National Tsing Hua University in Taiwan. He received his M.S. and Ph.D. from the Department of Industrial Engineering at the University of Texas at Arlington. His research primarily focuses on algorithms, including exact solution methods and soft computing.
He has published more than 350 research papers in highly ranked journals and conferences and has received multiple prestigious awards, including the Outstanding Research Award (twice), the Distinguished Scholars Research Project (once), and the Overseas Research Fellowship (twice) from the Ministry of Science and Technology (MOST) in Taiwan.
He has been invited to serve as an Associate Editor for three journals: IEEE Transactions on Reliability, IEEE Access, and Reliability Engineering & System Safety. He proposed a novel soft computing algorithm called Simplified Swarm Optimization (SSO) and developed a new implicit enumeration algorithm known as the Binary-Addition-Tree (BAT) algorithm. Both SSO and BAT have demonstrated simplicity, effectiveness, efficiency, and flexibility in solving NP-hard problems.
He has been granted more than 55 patents, listed among the top 2% of scientists globally (2020-2023) by Stanford University, and recognized with several honors, including International Fellow, MOST Fellow (2021), CIIE Fellow (Chinese Institute of Industrial Engineers, 2022), the Guoguang Invention Medal, as well as the titles of Outstanding Inventor of Taiwan and Doctor of Erudition by the Chinese Innovation and Invention Society.
Speech Title: Navigation Reliability Assessment: Binary-Addition-Tree Algorithm on Occupancy Grid Networks for AMR Navigation
 Abstract:
Autonomous Mobile Robots (AMRs) operate in uncertain, dynamic environments where traditional shortest-path algorithms like A* and Dijkstra cannot quantify navigation reliability. This paper presents a navigation reliability framework integrating Occupancy Grid Networks (OGN) with a Binary-Addition-Tree algorithm (BAT) to evaluate the probability of successful navigation from start to goal. Each grid cell is modeled as a binary-state component—traversable or failed—based on occupancy probabilities from sensor data. The BAT algorithm exploits grid network structure to compute exact connectivity reliability efficiently, eliminating Monte Carlo simulation requirements. Validation through NVIDIA Isaac Sim digital-twin experiments demonstrates that BAT achieves exact reliability estimation with significantly reduced computational cost, enabling real-time AMR decision-making. This framework bridges network reliability theory with robotic occupancy mapping for reliability-driven navigation in mission-critical applications.