TutORials

The TutORials in Operations Research series is published annually by INFORMS as an introduction to emerging and classical subfields of operations research and management science. These chapters are designed to be accessible for all constituents of the INFORMS community, including current students, practitioners, faculty, and researchers. The publication allows readers to keep pace with new developments in the field and serves as augmenting material for a selection of the tutorial presentations offered at the INFORMS Annual Meeting.

Simulation Optimization in the New Era of AI

We review simulation optimization methods and discuss how these methods underpin modern artificial intelligence (AI) techniques. In particular, we focus on three areas: stochastic gradient estimation, which plays a central role in training neural networks for deep learning and reinforcement learning; simulation sample allocation, which can be used as the node selection policy in Monte Carlo tree search; and variance reduction, which can accelerate training procedures in AI.

Speakers: Yiijie Peng, Chun-Hung Chen, and Michael Fu

Speaker Bios

Dr. Yijie Peng is currently an associate professor of the Department of Management Science and Information Systems in Guanghua School of Management at Peking University (PKU). He received his PhD from the Department of Management Science at Fudan University and his BS degree from the School of Mathematics at Wuhan University. Many of his publications appear in high-quality journals including Operations Research, INFORMS Journal on Computing, and IEEE Transactions on Automatic Control. He is awarded the 2019 Outstanding Simulation Publication Award of INFORMS Simulation Society. He serves as an associate editor for Asia-Pacific Journal of Operational Research and IEEE Control Systems Society Conference Editorial Board. His research interests include stochastic modeling and analysis, simulation optimization, machine learning, and healthcare.

Chun-Hung Chen received his PhD degree from Harvard University in 1994. He is currently a professor at George Mason University. Dr. Chen was an assistant professor at the University of Pennsylvania before joining GMU. Sponsored by NSF, NIH, DOE, NASA, FAA, and AFOSR in the U.S., he has worked on the development of very efficient methodology for simulation-based decision making and its applications. Dr. Chen has served on several editorial boards, such as IEEE Transactions on Automatic Control, IEEE Transactions on Automation Science and Engineering, IIE Transactions, Asia-Pacific Journal of Operational Research, Journal of Simulation Modeling Practice and Theory, International Journal of Simulation and Process Modeling, and Journal of Traffic and Transportation Engineering. Dr. Chen is an author of the popular book: “Stochastic Simulation Optimization: An Optimal Computing Budget Allocation.” He is an IEEE Fellow.

Michael C. Fu holds the Smith Chair of Management Science at the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research, at the University of Maryland. 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 served as the Operations Research NSF Program Director (2010–2012 & 2015) and as General Co-Chair for the 2020 INFORMS National Meeting. Recent awards include the INFORMS Simulation Society’s Distinguished Service Award (2019), the INFORMS Saul Gass Expository Writing Award (2021), and the INFORMS Kimball Medal (2022). He is a Fellow of IEEE and INFORMS.

Mixed-Integer Programming Approaches to Generalized Submodular Optimization and its Applications

Submodularity is an important concept in integer and combinatorial optimization. A classical submodular set function models the utility of selecting homogenous items from a single ground set, and such selections can be represented by binary variables. In practice, many problem contexts involve choosing heterogenous items from more than one ground set or selecting multiple copies of homogenous items, which call for extensions of submodularity. We refer to the optimization problems associated with such generalized notions of submodularity, Generalized Submodular Optimization (GSO.) GSO is found in wide-ranging applications, including infrastructure design, healthcare, online marketing, and machine learning. Due to the often highly nonlinear (even non-convex and non-concave) objective function and the mixed-integer decision space, GSO is a broad subclass of challenging mixed-integer nonlinear programming problems. In this tutorial, we first provide an overview of classical submodularity. Then we introduce two subclasses of GSO, for which we propose polyhedral theory for the mixed-integer set structures that arise from these problem classes. Our theoretical results lead to efficient and versatile exact solution methods that demonstrate their effectiveness in practical problems using real-world datasets.

Speaker: Simge Küçükyavuz and Qimeng Yu

Speaker Bio

Simge Küçükyavuz is a professor in the Industrial Engineering and Management Sciences Department at Northwestern University. She is an expert in mixed-integer, large-scale, and stochastic optimization. Her methodologies have applications in complex computational problems across numerous domains, including social networks, computing and energy infrastructure, statistical learning, and logistics. Her research has been supported by multiple grants from the National Science Foundation (NSF) and the Office of Naval Research (ONR). She is the recipient of the 2011 NSF CAREER Award and a co-winner of the 2015 INFORMS Computing Society (ICS) Prize. She is the past chair of ICS and serves on the editorial boards of Mathematics of Operations Research, Mathematical Programming, SIAM Journal on Optimization, and MOS-SIAM Optimization Book Series. She received her PhD in industrial engineering and operations research from the University of California, Berkeley.

Qimeng Yu is a PhD candidate in the Department of Industrial Engineering and Management Sciences at Northwestern University, advised by Dr. Simge Küçükyavuz. In her research, she develops theory and algorithms for mixed-integer nonlinear programming to facilitate the solution of complex models with real-world applications. Starting Fall 2023, she will be an assistant professor in the Department of Computer Science and Operations Research at Université de Montréal.

Pharmaceutical Supply Chains and Drug Shortages

While pharmaceutical (pharma) industry is vital to the economy and its supply chain efficiency directly affects the quality and cost of patient care, pharma supply chains have been largely under-researched in the healthcare field, compared to the thrived research in medical services/hospital operations by the INFORMS community. At the same time, the pharma industry faces complicated and unique economics and regulation environment. In this tutorial, we aim to provide a base understanding of the very complex pharmaceutical supply chain and present challenges it faces. Using drug shortages (a persistent and significant problem facing the pharma industry, government, and the society) as well as other examples, we demonstrate that the richness and uniqueness of the pharma supply chain provides great opportunities for research with impact.

Speaker: Hui Zhao

Speaker Bio

Dr. Hui Zhao is a professor of supply chain management and the Charles and Lilian Binder Faculty Fellow at the Smeal College of Business of the Pennsylvania State University. She is also a co-director of research at Penn State’s Center for Supply Chain Research^®. Her research applies analytics to align incentives, promote collaboration, and improve efficiency in supply chains. Her work focuses on healthcare with particular interests in pharmaceutical/healthcare supply chains, health public policy, and innovations in healthcare systems (online platforms and telehealth systems). Her work has received multiple awards including finalist for the Pierskalla award for healthcare research 2015, the runner-up for the Ralph Gomory Best Industry Studies Paper Award 2017, SCOR innovation award 2018, and Finalist for the Best Dissertation award for Industry Studies for her PhD student 2020. Her work has been funded by DOJ, NSF, and other agencies. Her work has appeared in leading journals in the operations management field such as MS, OR, M&SOM, POM. She serves as an associate/senior editor for M&SOM, POM, NRL, and DSJ. Aside from academia, since she started working on pharmaceutical supply chain in 2009, her work has been well recognized by the FDA (e.g., being an invited speaker many times and serving on expert panels for e.g., drug shortages) and industry (e.g. serving as a co-lead on the Pharma subteam of the Artificial Intelligence in Healthcare Initiative). She serves on industry advisory boards and collaborates widely with industry. She teaches extensively on quantitative decision making and supply chain management at the undergraduate, MBA, EMBA, and PhD levels.

Sensitivity Analysis

As management scientists, we increasingly rely on quantitative models to analyze problems that span across diverse realms such as supply chain management and finance. The models can be simulators or machine learning tools fitted to available data. Often, the complexity of the problems and the sophistication of the modeling exercises forces us to treat these models as black-boxes. Explainability and interpretability are then essential to reinforce stakeholders trust in quantitative models. The literature suggests analysts to answer questions such as: have we used the best available method for interpreting and explaining the results of my model? Is there a method that can help us in communicating the insights better to the decision-maker? As early as 1970, John D.C. Little writes that a process starts of finding out what it was about the inputs that made the outputs come out as they did, the task of sensitivity analysis. This TutORial reviews the role of sensitivity analysis in O.R., with a view of aiding explainability. We consider how O.R. researchers have been looking at sensitivity over time, and the techniques they have developed. We analyze available methods through the lens of four main managerial insights: factor prioritization (or feature importance), trend determination, interaction quantification, and stability (robustness). We discuss a variety of techniques and illustrate them by application to toy example and a realistic application, and illustrate the corresponding subroutines, with the goal of showcasing the wide palette of tools that the literature has made available to analysts.

Speaker: Emanuele Borgonovo

Speaker Bio

Emanuele Borgonovo is a full professor and Director of the Department of Decision Sciences at Bocconi University. He is co-editor-in-chief of the European Journal of Operational Research, advisor of the Springer International Series in Management Science and Operations Research, Co-Chair of the Committee on Uncertainty Analysis of the European Safety and Reliability Association, and member of the Scientific Committee of the Fondazione Silvio Tronchetti Provera. He is the past president of the Decision Analysis Society of INFORMS of which he has been president in the years 2020-2022. At Bocconi University he has been Director of the Bachelor in Economics, Management, and Computer Science from 2016-2022 and director of the Eleusi Research Center from 2008 to 2012, as well as director of the SDA Bocconi Management Science Lab from 2013 till 2016. He holds a PhD in probabilistic risk assessment from the Massachusetts Institute of Technology and is the recipient of several national and international awards. He is a member of the editorial boards of numerous international journals, has published more than 100 scientific articles and worked on international research projects with, among others, the U.S. Defense Advanced Research Project Agency (DARPA), the U.S. Nuclear Regulatory Commission, the U.S. Department of Energy, the Idaho National Laboratory, Electricité de France, Charles River Analytics, etc. In his works, he has introduced several new sensitivity analysis techniques. The differential importance measure is part of the NASA Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners. He is the author of the book “Sensitivity Analysis: An Introduction for the Management Scientist.”

Stockpyl: A Python Package for Inventory Optimization and Simulation

Stockpyl is a Python package for inventory optimization and simulation. It implements classical single-node inventory models like the economic order quantity (EOQ), newsvendor, and Wagner–Whitin problems. It also contains algorithms for multiechelon inventory optimization (MEIO) under both stochastic-service model (SSM) and guaranteed-service model (GSM) assumptions. And, it has extensive features for simulating multi-echelon inventory systems. In this tutorial, we provide an overview of Stockpyl, including a short primer on the inventory models and algorithms that underlie the modules in Stockpyl. Python code snippets are used throughout, and a Jupyter notebook containing all of the code is available on the GitHub repository for the Stockpyl project.

Speaker: Lawrence Snyder

Speaker Bio

Larry Snyder is the Harvey E. Wagner Professor of Industrial and Systems Engineering and Director of the Institute for Data, Intelligent Systems, and Computation (I-DISC) at Lehigh University in Bethlehem, PA. He received his PhD in industrial engineering and management sciences from Northwestern University. Dr. Snyder’s research interests include modeling and solving problems in supply chain management and energy systems, particularly when the problem exhibits significant amounts of uncertainty. His research has been published in such journals as Manufacturing & Service Operations Management, Transportation Science, IEEE Transactions on Smart Grid, Naval Research Logistics, IIE Transactions, and Production and Operations Management and has been funded by NSF, DOE, state agencies, and several major corporations. He is co-author of the textbook Fundamentals of Supply Chain Theory, published in 2011 by Wiley, which won the IIE/Joint Publishers Book-of-the-Year Award in 2012; a second edition was published in 2019. He also wrote two books of puzzles called The Opex Analytics Weekly Puzzle, volumes 1 and 2. He has delivered or co-authored over 100 presentations at academic conferences, universities, and companies. He is a founding member of Lehigh’s Integrated Networks for Electricity (INE) research cluster and Power from Oceans, Rivers, and Tides (PORT) lab. He has served on the editorial boards of Transportation Science, IISE Transactions, OMEGA, and the Wiley Series on Operations Research and Management Science. He previously served as a Senior Research Fellow–Optimization for Opex Analytics. For more information, visit coral.ise.lehigh.edu/larry.

Supply-Chain-Centric View of Working Capital, Hedging and Risk Management: Integrated Supply Chain Finance (iSCF) Tutorial

Integrated Supply Chain Finance (iSCF) is a portfolio of effective operating, financial and risk mitigating practices and techniques within supply chains, which reflect strategic concerns of participating firm agents (decision makers) within the chain, and optimize not only the management of the working capital for liquidity, but also make effective use of assets for firm profitability and risk control. The main theory and research areas within iSCF are:

Financing Working Capital in Supply Chains;
Financial Hedging in support of Supply Chain Operations;
Integrated Risk Management (IRM) in Supply Chains; and
Supply Chain Contracts and Risk Management.

This tutorial chapter will dedicate a section in each of the above topics, and it will elucidate the foundational models and the key results in each of these areas. It will conclude with thoughts on future research topics, and how emerging technologies may be shaping the future of iSCF decision making.

Speaker: Panos Kouvelis

Speaker Bio

Panagiotis (Panos) Kouvelis is the Emerson Distinguished Professor of Operations and Manufacturing Management in the Olin Business School. He also serves as Director of The Boeing Center for Supply Chain Innovation (BCSCI) —a supply chain, process excellence, business models innovation, and technology management research center at Washington University in St. Louis. He is ranked a top 5 operations management researcher (see IJPR 2015, 53:20, 6161-6197) in terms of research productivity, quality and citations, with over 12,000 citations. He has published nine books and more than 130 journal articles and has served in top editorial positions for all of the major journals in his field. He was recognized with the 2022 Distinguished MSOM Fellow Award, the highest honor to be bestowed upon a research scholar in the operations management field, and the 2016 POMS Lifetime Fellow Award, in recognition of lifetime intellectual contributions to the profession through research and teaching. Kouvelis earned his doctorate in industrial engineering and engineering management from Stanford University. He earned his master’s degrees in business administration and industrial and systems engineering from the University of Southern California. He also holds a diploma in mechanical engineering from the National Technical University of Athens.

Search and Rescue over Uncertain Terrain in Humanitarian and Military Contexts: Capturing Emerging Targets and Performance Reconnaissance

This tutorial will introduce and discuss two topics that are related to the military and security domain. The first topic is focused on search path optimization for recording emerging targets. This considers the situation where targets emerge according to a nonhomogeneous space-time Poisson process during the mission. The only provided information is the time-dependent arrival rate function for each cell in the area. The single vehicle case is discussed first, along with examples and computational results. After this, the case of camouflaging targets and multiple vehicles is considered, with a focus on how these features complicate the problem and change routing strategies. The second topic is search and exploration problems on transportation networks with unknown characteristics. In such a scenario links are divided into two classes, one class being links that are operable and the other class being links whose status is unknown and only becomes known when the traveler arrives at one of the end nodes of the link. Prize collection problems on such networks will be detailed for the single traveler case. Examples and computational results will be presented. After discussion of these two topics, the tutorial will present emerging topics related to emergency response and reconnaissance applications.

Speaker: Rajan Batta, John Becker, Esther Jose, and Nastaran Oladzad-Abbasabady

Speaker Bio

Rajan Batta is a SUNY Distinguished Professor in the Department of Industrial and Systems Engineering and Associate Dean for Faculty Affairs and Diversity in the School of Engineering and Applied Sciences, at the University at Buffalo, where he has been a faculty member since 1984. He received his PhD in operations research from the Massachusetts Institute of Technology and a Bachelor of Technology in mechanical engineering from the Indian Institute of Technology, Delhi, India. He uses operations research techniques to develop and analyze mathematical models of systems critical to society. His research interests include military and security applications, transportation planning applications, and analysis of urban crime patterns. His recent work in the military and security domain has focused on damage estimation and reconnaissance missions over uncertain networks and terrains. He is a Fellow of both the Institute for Operations Research and the Management Sciences (INFORMS) (2022) and of the Institute of Industrial and Systems Engineers (IISE) (2006). He has won numerous awards associated with his journal papers, including the Military Operations Research Journal Award from the Military Operations Research Society (2005 and 2020), the Koopman Prize from the INFORMS Military and Security Society (2018), and the IISE Transactions Best Paper Award from IISE (2012). He has also received several key leadership and research awards from IISE: Frank and Lillian Gilbreth Industrial Engineering Award (2022); Technical Innovation in Industrial Engineering Award (2016); Albert G. Holzman Distinguished Educator Award (2015); David F. Baker Distinguished Research Award (2008).

John Becker is a PhD student in the Industrial and Systems Engineering Department at the University at Buffalo. He is advised by Dr. Rajan Batta, and his current research interests are stochastic graph traversal problems and simulation-based optimization.

Esther Jose is a PhD candidate in Operations Research at the State University of New York at Buffalo, where she is advised by Dr. Rajan Batta. Her research interest lies in Applied Operations Research. Most recently, her work has focused on military and security applications, particularly in optimizing information collection from satellites or from ground sensors. She also has experience in applying Operations Research to the mitigation and suppression of natural disasters, particularly wildfires. Esther is passionate about integrating equity and inclusion into her work whenever possible.

Nastaran Oladzad-Abbasabady is a PhD student in the Department of Industrial and Systems Engineering at the University at Buffalo-SUNY. Her research interests lie in the applications of Operations Research and Machine Learning-Deep Learning to Disaster Management.

Integer Programming Games: a Gentle Computational Overview

Nash equilibria enlighten the structure of rational behavior in multi-agent decision-making. However, besides its existence, the concept is as helpful as one can efficiently compute it. Little is known about the computation of Nash equilibria in non-convex settings, a relevant context because non-convexities, often in the form of integer requirements. We provide a gentle overview of the recent bundle of work that deals with computing Nash equilibria for integer programming games. We do that by using the general and practically relevant context of attacking and protecting a critical infrastructure, and we highlight the characteristics and compare the differences of a sequential approach (Stackelberg game) versus a simultaneous one (Nash game). Finally, we guide the reader to the use of relevant software for computing Nash equilibria for integer programming games.

Speaker: Andrea Lodi, Margarida Carvalho, Gabriele Dragotto, and Sriram Sankaranarayanan

Speaker Bio

Andrea Lodi is an Andrew H. and Ann R. Tisch Professor at the Jacobs Technion-Cornell Institute at Cornell Tech and the Technion since 2021. He received his PhD in systems engineering from the University of Bologna in 2000 and he has been Herman Goldstine Fellow at the IBM Mathematical Sciences Department, full professor of operations research at the University of Bologna, and Canada Excellence Research Chair at Polytechnique Montréal. His main research interests are in mixed-integer linear and nonlinear programming and data science. Andrea Lodi is the recipient of the INFORMS Optimization Society 2021 Farkas Prize. He serves in leading editorial positions, and has been PI and co-PI of large academic grants both in Europe and Canada. Finally, Andrea has been part-time member of the IBM CPLEX research and development team, contributing to develop one of the world-wide leading mixed-integer linear programming in the period 2006-2021.

Margarida Carvalho is an assistant professor in the Department of Computer Science and Operations Research at the University of Montreal, where now she holds the FRQ-IVADO Research Chair in Data Science for Combinatorial Game Theory. In 2016, she completed a PhD in computer science at the University of Porto (Portugal) for which she received the 2018 EURO Doctoral Dissertation Award. Margarida is an expert in mixed-integer programming, algorithmic game theory and computational complexity, and she is interested in the application of these tools in socio-economic problems. Currently, she is an associate editor for INFORMS Journal on Computing, OR Spectrum, and Dynamic Games and Applications.

Gabriele Dragotto is a Data X Postdoctoral Fellow at Princeton’s Center for Statistics and Machine Learning and a Postdoctoral Research Associate at Princeton’s Department of Operations Research and Financial Engineering. He holds a Ph.D. in Mathematics (2022) from Polytechnique Montréal, where he worked at the Canada Excellence Research Chair in Data Science for Real-Time Decision-Making on his thesis “Mathematical Programming Games”. His research is at the interface of algorithmic game theory and optimization, and it focuses on nonconvex games, i.e., decision-making among a set of selfish and mutually-interacting agents that decide by solving complex optimization problems. Gabriele combines optimization and algorithmic game theory methodologies to design data-driven algorithms and theoretical insights to guide decision-makers toward efficient and socially beneficial outcomes. Gabriele’s research provides tools to explain and inform decision-making in energy markets, retail operations, autonomous systems, intelligent infrastructure, and telecommunication systems.

Sriram Sankaranarayanan is an Assistant Professor at IIM Ahmedabad in the area of Operations and Decision Sciences. His research interest lies in solving game-theoretic and optimisation problems that include integer variables and other structured nonconvexities. In particular, he has worked on mixed-integer linear programming, complementarity problems and mixed-integer bilevel programming. Apart from proving structural results and developing algorithms to solve these problems, he is also interested in using these methods for real-life problems which are of social interest. He has worked on using tools from optimization to analyze energy-market policies, with a particular interest to combat climate change.

Nonlinear Dynamics Modeling and Control of Operational Data for Process Improvements

Whenever multifarious entities cooperate, compete, or interfere in manufacturing or service operations, there will be the rise of nonlinear and nonstationary dynamics. As complex systems evolve in time, operational dynamics deal with change. Whether the system settles down to the steady state, undergoes incipient changes, or deviates into more complicated variations, it is dynamics that help analyze system behaviors. Effective monitoring, modeling and control of nonlinear dynamics will increase system quality and integrity, thereby leading to significant economic and societal impacts. For example, manufacturing processes will make products with better quality and higher throughput. Gaining a deeper understanding of nonlinear dynamics of complex diseases will help improve the delivery of healthcare services, reduce the healthcare cost, and improve the health of our society.

However, nonlinear dynamics pose significant challenges for operations engineering. Particularly, nonlinear dynamical systems defy understanding based on the traditional reductionist’s approach, in which one attempts to understand a system’s behavior by combining all constituent parts that have been analyzed separately. In order to cope with system complexity and increase information visibility, modern industries are investing in advanced sensing modalities such as sensor networks and internet-of-things technology. Real-time sensing gives rise to rich datasets pertinent to operational dynamics. Realizing the full potential of such operational data for process improvements requires fundamentally new methodologies to harness and exploit complexity. Nonetheless, there is a critical gap in the knowledge base that pertains to integrating nonlinear dynamics research with operations engineering. The theory of nonlinear dynamics has been primarily studied in mathematics and physics. There is an urgent need to harness and exploit nonlinear dynamics for creating new products (or services) with exceptional features such as adaptation, customization, responsiveness, and quality in unprecedented scales.

This tutorial presents a review of nonlinear dynamics methods and tools for real-time system informatics, monitoring and control. Specifically, we will discuss the characterization and modeling of recurrence dynamics, network dynamics, and self-organizing dynamics hidden in operational data for process improvements. Further, we contextualize the theory of nonlinear dynamics with real-world case studies and discuss future opportunities to improve the design, monitoring, and control of manufacturing and service operations. We posit this work will help catalyze more in-depth investigations and multi-disciplinary research efforts in the intersection of nonlinear dynamics and data mining for operational excellence.

Speaker: Hui Yang

Speaker Bio

Dr. Hui Yang is a Fellow of IISE, a professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University, University Park, PA. Also, Dr. Yang currently serves as the director of Penn State NSF Center for Health Organization Transformation (CHOT). Dr. Yang’s research interests focus on sensor-based modeling and analysis of complex systems for process monitoring, process control, system diagnostics, condition prognostics, quality improvement, and performance optimization. His research program is supported by National Science Foundation (including the prestigious NSF CAREER award), National Institute of Standards and Technology (NIST), Lockheed Martin, NSF center for e-Design, Susan G. Koman Cancer Foundation, NSF Center for Healthcare Organization Transformation, Institute of Cyberscience, James A. Harley Veterans Hospital, and Florida James and Esther King Biomedical research program. His research group received several best paper awards and best poster awards from IISE Annual Conference, IEEE EMBC, IEEE CASE, and INFORMS. Dr. Yang was the president (2017-2018) of IISE Data Analytics and Information Systems Society, the chair (2015-2016) of INFORMS Quality, Statistics and Reliability (QSR) society, and the program chair of 2016 Industrial and Systems Engineering Research Conference (ISERC). He is also the department editor for IISE Transactions Healthcare Systems Engineering, as well as associate editors for IISE Transactions, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Automation Science and Engineering (TASE), IEEE Robotics and Automation Letters (RA-L), Quality Technology and Quality Management, and an associate editor for the Proceedings of IEEE CASE, IEEE EMBC, and IEEE BHI. He has also co-authored a book “Healthcare Analytics: From Data to Knowledge to Healthcare Improvement,” John Wiley & Sons, 2016.

A Practitioner’s Guide to Digital Twin Development

This tutorial describes industrial digital twin development including advanced analytics. Using factory and supply chain digital twins as example applications, we present two different digital twin frameworks that serve as a guide for practitioners interested in developing digital twin solutions. The resulting digital twins are expected to help understand what did happen, predict what may happen and prescribe actions to address future problems before they happen. We conclude with examples of digital twin use cases and challenges of their implementations.

Speaker: Bahar Biller, Stephan Biller, and Jinxin Yi

Speaker Bio

Bahar Biller is a Principal Operations Research Specialist at the Analytics Center of Excellence of the SAS Institute. In this role, she collaborates with clients, product managers, and researchers to improve the efficiency and resiliency of industrial supply chains and healthcare and life sciences operations. Bahar is a past-President of the INFORMS Simulation Society and the General Chair of the Winter Simulation Conference 2023.

Stephan Biller is the Harold T. Amrine Distinguished Professor in the School of Industrial Engineering and the Mitchell E. Daniels, Jr. School of Business at Purdue University and serves as the Director of the Dauch Center for the Management of Manufacturing Enterprises at the Daniels School of Business. His expertise includes Smart Manufacturing, Digital Twin, Industry 4.0, and Supply Chain Management. He is passionate about how AI in the broadest sense and IoT can facilitate the Digital Transformation of large and especially small and medium manufacturing enterprises @ scale. Previously, he served as Founder and CEO of Advanced Manufacturing International, Vice President of Product Management for AI Applications & Watson IoT at IBM, Chief Manufacturing Scientist & Manufacturing Technology Director at General Electric, and Tech Fellow & Global Group Manager for Manufacturing Systems at General Motors. He is an IEEE Fellow and an elected member of the National Academy of Engineering.

Jinxin Yi is the Director of Analytics Center of Excellence (ACOE) at SAS Institute, Inc. He leads the ACOE to provide technical support in presales engagements and consulting services in post-sales deployment, specifically in the areas of optimization and AIML. He has been with SAS for 20 years. He has a Bachelor’s degree in Mechanical Engineering from Tsinghua University in China and a PhD in Operations Research from Carnegie Mellon University.

Incorporating Artificial Intelligence into Healthcare Workflow: Models and Insights

Artificial intelligence (AI) is poised to revolutionize healthcare delivery in the United States and around the world. As AI becomes an integral part of the healthcare workflow, it will change the way we model and analyze healthcare delivery and upend the paradigm that has dictated how operations research and management science researchers interact with healthcare practitioners. In this tutorial, we demonstrate how the integration of AI into the healthcare workflow will require a new set of models to guide rapidly changing healthcare practices, measure productivity gains in the industry, and reduce disparities in access to care. These models must be based on a thorough understanding of the variables that influence physician buy-in and patient acceptance. While medical AI promises to learn and adapt based on user interactions and data, the development, validation, and approval process also requires the creation of new models that generate useful insights. Finally, we discuss barriers and opportunities related to incentive design and ethical considerations for AI in healthcare.

Speaker: Tinglong Dai and Michael D. Abràmoff

Speaker Bio

Tinglong Dai is a professor of operations management & business analytics at the Johns Hopkins Carey Business School. He is a member of the leadership team of the Hopkins Business of Health Initiative, where he co-chairs the Johns Hopkins Workgroup on AI and Healthcare, and the Executive Committee of the Johns Hopkins Institute for Data Intensive Engineering and Science. Dr. Dai’s research interests span across healthcare analytics, human-AI interaction, global supply chains, and marketing-operations interfaces. His work has been published in leading journals such as Management Science, M&SOM, Marketing Science, and Operations Research, and has been recognized with the Johns Hopkins Discovery Award, INFORMS Public Sector Operations Research Best Paper Award, POMS Best Healthcare Paper Award, and Wickham Skinner Early Career Award. Dr. Dai is an associate editor of Management Science, M&SOM, Health Care Management Science, and Naval Research Logistics, and a senior editor of Production and Operations Management. As a leading expert on healthcare analytics and global supply chains, Dr. Dai has been quoted thousands of times in the media, including the Associated Press, Bloomberg, CNN, Fortune, New York Times, NPR, USA Today, Wall Street Journal, and Washington Post, and has appeared on national and international TV such as CNBC, PBS NewsHour, and Sky News. In 2021, he was named one of the World’s Best 40 Under 40 Business School Professors by Poets & Quants. He joined Johns Hopkins in 2013 after receiving a PhD in operations management and robotics from Carnegie Mellon University.

Michael D. Abràmoff MD, PhD, is a neuroscientist, fellowship-trained retina specialist, computer engineer, and entrepreneur. He is founder and executive chairman of Digital Diagnostics, Inc, the first company ever to receive FDA clearance for an autonomous AI diagnostic system, in any field of medicine. In primary care, it can instantaneously diagnose diabetic retinopathy and diabetic macular edema at the point of care without human oversight, in order to improve access and quality of care, remove health inequities, and lower cost. He is the Robert C. Watzke, MD Professor of Ophthalmology and Visual Sciences at the University of Iowa, with joint appointments in the College of Engineering. With his collaborators, Dr. Abràmoff has developed an ethical foundation for healthcare AI based on “metrics for ethics”, that continues to be used for the design, training, validation, and regulatory and payment pathways for autonomous AI, addressing such issues as AI bias, AI liability, patient and population outcomes, and data usage. As author of over 350 peer-reviewed publications, his scientific work has been cited 42,000 times (h-index 77), and he is the inventor on 22 issued patents as well as many patent applications. Dr. Abramoff has mentored dozens of engineering graduate students, ophthalmology residents, and vitreoretinal surgery fellows.