Keynotes

2022 Keynote Panel

Kenneth Lim

Assistant Chief Executive (Industry & Transformation), Maritime and Port Authority of Singapore

Ngien Hoon Ping

Group CEO, SMRT

Ong Kim Pong

Regional CEO, PSA International, Singapore

2022 Titans of Simulation

Michael Fu

Smith Chair of Management Science Decision, Operations and Information Technologies
Robert H. Smith School of Business
University of Maryland

Stochastic Gradients: From Single Sample Paths to Conditional Monte Carlo to Machine Learning

Dr. Michael Fu will make a humble attempt to convey one perspective of his journey through nearly four decades of simulation methodology research focused on stochastic gradient estimation and simulation optimization, highlighting technical advances and some real-world success stories (mainly of others) and illustrating several techniques using simple examples, sprinkled in with personal anecdotes, some historical context, and random ramblings. Perhaps by the end of the talk, with the guidance of the audience, potential future sample paths can be envisioned or reimagined.

Bio

Michael C. Fu holds the Smith Chair of Management Science in the Decision, Operations, and Information Technologies department of the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research and an affiliate appointment in the Department of Electrical & Computer Engineering (both in the Clark School of Engineering), all at the University of Maryland. He served as Chair of his department from January 2017 to June 2020.

He received bachelor’s degrees in mathematics and electrical engineering from MIT in 1985, a master’s degree in electrical engineering and computer science from MIT, and an M.S. and Ph.D. in applied mathematics from Harvard University in 1989.

His research interests include stochastic gradient estimation, simulation optimization, and applied probability. He is the co-author of the books, Conditional Monte Carlo: Gradient Estimation and Optimization Applications, which received the INFORMS Simulation Society’s 1998 Outstanding Publication Award, and Simulation-Based Algorithms for Markov Decision Processes, and editor/co-editor of four volumes: Perspectives in Operations Research, Advances in Mathematical Finance, Encyclopedia of Operations Research and Management Science (3rd edition), and Handbook of Simulation Optimization. He has served as Department Editor for Stochastic Models and Simulation at Management Science, as Simulation Area Editor for Operations Research, and on the Editorial Board of Mathematics of Operations Research, INFORMS Journal on Computing, IIE Transactions, and Production and Operations Management. He was also Guest co-Editor of a special issue of the ACM Transactions on Modeling and Computer Simulation on simulation optimization.

In 2004 he was named a Distinguished Scholar-Teacher at the University of Maryland. His other awards include the IIE Operations Research Division Award (1999), an IIE Transactions Best Paper Award, the Maryland Business School’s Allen J. Krowe Award for Teaching Excellence (1995), the INFORMS Simulation Society’s Distinguished Service Award (2019), and the INFORMS Saul Gass Expository Writing Award (2021). He served as Program Chair for the 2011 Winter Simulation Conference, as the Operations Research Program Director at the National Science Foundation from 2010 to 2012 and in 2015, and on the INFORMS Board of Directors as Treasurer from 2017-2018. He was also the General Co-Chair for the 2020 INFORMS National Meeting. He is a Fellow of IEEE and INFORMS.

Leon McGinnis

Professor Emeritus
Georgia Tech

Reimagining Simulation in Discrete-Event Logistics Systems

Over fifty years ago, when McGinnis was coding simulation models in Fortran, he imagined that would be how he would always do it. Thankfully, smarter people imagined a different and better way of creating simulation models. Today, the challenge of applying simulation in large-scale, highly automated discrete-event logistics systems to create usable and effective digital twins is more than making the coding process faster and cheaper. It requires re-imagining the relationship between the real system and the simulation of it and re-imagining the relationship between the system stakeholders and the simulationists.

Bio

Leon McGinnis is Professor Emeritus in the Stewart School of Industrial and Systems Engineering at Georgia Tech where he remains active in teaching and research. He is internationally known for his leadership in the material handling research community and his research on discrete event logistics systems. He has received several awards from professional societies for his innovative research, including the David F. Baker Award from IIE, the Reed-Apple Award from the Material Handling Education Foundation, and the Material Handling Innovation Pioneer award from Material Handling Management Magazine. He is author or editor of eight books, one journal special issue, and more than 110 technical publications. At Georgia Tech, Professor McGinnis has held leadership positions in industry-focused centers and multi-disciplinary programs, including the Material Handling Research Center, the Computer Integrated Manufacturing Systems Program, the Manufacturing Research Center, the Sustainable Design and Manufacturing Program, the Tennenbaum Institute for Enterprise Transformation, the Model-Based Systems Engineering Center and the Physical Internet Center. His current research explores the adaptation of Model Based Systems Engineering principles and methods to the design and control of discrete event logistics systems (DELS). With support from multiple corporations and the National Institute for Science and Technology, his team is investigating novel approaches for both system design and operational control through material handling. Professor McGinnis is a Fellow of the Institute of Industrial and Systems Engineering.

MASM Keynote

Koen de Backer

Vice President Smart Manufacturing & Artificial Intelligence,
Micron Technology

Chairman, Smart Manufacturing Council
SEMI Southeast Asia

Industry 4.0 Innovation in Semiconductor Planning and Operations

Industry 4.0 innovations in smart manufacturing and AI have demonstrated impact at scale in manufacturing semiconductor industry. The big data technology concept was started in quality and yield improvement. However, much attention was not given to planning and scheduling of semiconductor industry, which is a key of productivity improvement and customer demand satisfaction. Micron Technology is a world leader of semiconductor memory and data storage and has an initiative to drive Industry 4.0 for planning and scheduling pillar with smart manufacturing and artificial intelligence. This keynote will introduce the evolutionary steps of planning and scheduling systems to support smart manufacturing and digital transformation that leads to streamlined planning and scheduling to drive the productivity improvement and demand satisfaction using digital twin, optimization, artificial intelligence.

Bio

Koen De Backer is the Vice President of smart manufacturing and artificial intelligence at Micron Technology. He is responsible for driving Micron’s smart manufacturing initiatives and digital operations including capabilities with IoT, artificial intelligence, advanced analytics, cognitive computing and machine learning to enhance our business, global operations and product development.

Prior to joining Micron, Koen led large-scale operations projects for more than a decade to help clients reduce inefficiencies and achieve excellence in manufacturing, procurement, supply chain and support functions. Most recently, Koen was a Partner at McKinsey & Company where he steered the semiconductor consulting practice in Southeast Asia and was one of the firm’s leading experts on applying artificial intelligence and automation techniques across operations and support functions such as finance, human resources and procurement. Additionally, Koen consulted with high-tech global clients while working at Deloitte Consulting, Altran Europe and CSC.

Koen holds a master’s degree in business administration from INSEAD and master’s degrees in both industrial management and electromechanical engineering from Katholieke Universiteit Leuven.

Military Keynote

Robert Hunjet

Associate Professor
Associate Editor IEEE Transactions on Artificial Intelligence
Ag/Research Leader Protection and Networked Autonomy | Land Division
Defence Science and Technology Group
Department of Defence | Edinburgh

In Pursuit of Defense AI and Autonomous Systems

With rapid advances in Artificial Intelligence, we are seeing increased maturity in Robotics, and Autonomous Systems and Artificial Intelligence (RASAI) offerings, ranging from drones, through to higher degrees of autonomy in cars. But where are they in the military context? These systems are promised to remove humans from harm’s way. Why aren’t they ubiquitous in Defense?

Building Defense RASAI is a tough ask. This talk explores some of the reasons why. It offers a perspective on why we are yet to see highly capable AI and Autonomous systems deployed in Defense scenarios; then explores how modelling and simulation might aid in the development and training of such systems.

Bio

Dr. Robert Hunjet is the Australian Defence Science and Technology Group’s (DSTG) Program Leader for Artificial Intelligence. In this role he is responsible for driving Defence’s Innovation Science and Technology investment in Artificial Intelligence. He received his Ph.D. from the University of Adelaide, Australia for his thesis on Self Adaptive Network Topologies in 2014 and serves as an adjunct Associate Professor with the Trusted Autonomy Group in the School of Engineering and Information Technology, University of New South Wales, Canberra, Australia. He is the chair of the IEEE Symposium of Computational Intelligence for Security and Defense Applications and serves as an Associate Editor for IEEE Transactions on Artificial Intelligence.

His research interests include all things AI and Autonomy with a specific interest on their use in distributed systems.