David Simchi-Levi

Professor of Engineering Systems at MIT & Head of the MIT Data Science Lab & US National Academy of Engineering Member

Topic: Reinventing Operations Management’ s Research and Practice with Data Science

Abstract: Machine learning is playing increasingly important roles in decision making, with key applications ranging from dynamic pricing and recommendation systems to personalized medicine and clinical trials. While supervised machine learning traditionally excels at making predictions based on i.i.d. offline data, many modern decision-making tasks, in particular in operations management, require making sequential decisions based on data collected online. Such discrepancy gives rise to important challenges of bridging offline supervised learning and online interactive learning to unlock the full potential of data - driven decision making.
The presentation will focus on the integration of online and offline learning to improve decision making in operations management. We highlight three examples. In the first, we consider the challenges of reducing difficult online decision-making problems to well-understood offline supervised learning problems. In the second, we show the impact of offline data on online decision making. Finally, in clinical trials, we show how to convert offline randomized control trials into adaptive, online, experimental design.

About Speaker: David Simchi-Levi is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab.  He is considered one of the premier thought leaders in supply chain management and business analytics.

His Ph.D. students have accepted faculty positions in leading academic institutes including U. of California Berkeley, Carnegie Mellon U., Columbia U., Cornell U., Duke U., Georgia Tech, Harvard U., U. of Illinois Urbana-Champaign, U. of Michigan, Purdue U. and Virginia Tech.

Professor Simchi-Levi is the current Editor-in-Chief of Management Science, one of the two flagship journals of INFORMS. He served as the Editor-in-Chief for Operations Research (2006-2012), the other flagship journal of INFORMS and for Naval Research Logistics (2003-2005).

In 2023, he was elected a member of the National Academy of Engineering. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains.

He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize.

He was the founder of LogicTools which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain decisions. The company became part of the Accenture Applied Intelligence in 2018.

 John R. Birge

Hobart W. Williams Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business & U.S. National Academy of Engineering Member

Topic: Pricing and Capacity Decisions in Platform Competition with Network Externalities

Abstract: Many two-sided markets involve platforms that provide access and matching mechanisms between buyers and sellers. This talk will discuss models in which the platforms provide incentives or wages to provide capacity of supply while also setting prices for buyers whose preferences may also depend on the capacity.  The talk will describe the forms of equilibria that may arise in these settings. The focus of the results will be for choice models that are consistent with ride-sharing markets and will use empirical results from these markets to validate this consistency.

About Speaker: John Birge is the Hobart W. Williams Distinguished Service Professor of Operations Management at the University of Chicago Booth School of Business. Professor Birge earned a bachelor's degree in mathematics from Princeton University in 1977 and a master's degree and a PhD in operations research from Stanford University in 1979 and 1980, respectively. He joined the Chicago Booth faculty in 2004.

Before he joined Chicago Booth, he was a former dean of the Robert R. McCormick School of Engineering and Applied Sciences at Northwestern University. He has also worked as a consultant for a variety of firms including the University of Michigan Hospitals, Deutsche Bank, Allstate Insurance Company, and Morgan Stanley, and he uses cases from these experiences in his teaching.

Professor Birge's work focuses on application, theory, and computation for decision making under uncertainty with applications in the management of operations in finance, energy, health care, manufacturing, public policy, and transportation. He is an INFORMS Fellow, MSOM Society Distinguished Fellow, member of the US National Academy of Engineering, and Editor-in-Chief of Operations Research.

Lixin Tang

IEEE Fellow & Vice President of Northeastern University (China) & Chinese Academy of Engineering Member

Topic: Data Analytics and Optimization for Smart Industry

Abstract: Data analytics is the frontier basic research direction of industrial intelligence and one of the driving forces to promote scientific development. Systems optimization is the core basic theory of decision-making in smart industry, as well as the heart and engine of data analytics. This talk will discuss some systems modeling methods and optimization solution methods we have been working on. The systems modeling methods are to quantitatively describe different practical problems with proper formulations, including set-packing model, space-time network model, and continuous-time based model. The optimization solution methods include integer optimization, convex optimization, intelligent optimization, and dynamic optimization. This talk will also introduce systems optimization and data analytics of production, logistics, and energy in the steel industry, including: 1) production batching and scheduling in steelmaking/continuous casting, and hot/cold rolling operations; 2) logistics scheduling in loading operations, shuffling/reshuffling, and stowage; 3) data analytics-based energy optimization, including dynamic energy allocation and scheduling, energy analytics covering energy description, diagnosis and prediction; 4) data analytics, including temperature prediction of blast furnace, dynamic analytics of BOF steelmaking process based on multi-stage modeling, temperature prediction of reheat furnace based on mechanism and machine learning, and strip quality analytics of continuous annealing based on multi-objective ensemble learning.

About Speaker: Professor Lixin Tang （唐立新）is an IEEE Fellow, the Vice President of Northeastern University, China, a member of Chinese Academy of Engineering, the Director of Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, China, the Director of Center for Artificial Intelligence and Data Science, and the Director and Chair Professor of the National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University. He is also a member of the discipline review group of the State Council for Control Science and Engineering, the Deputy Director of Artificial Intelligence Special Committee in Science and Technology Commission, Ministry of Education, China, Chief Expert in optimization algorithms and software decision-making advisory panel of China Association for Science and Technology, the Vice President of Chinese Society for Metals, the Vice President of Operations Research Society of China (ORSC), and the Founding Director of Data Analytics and Optimization Society for Smart Industry of ORSC.

His research interests cover industrial intelligence and systems optimization theories and methods, covering industrial big data, data analytics and machine learning, deep learning and evolutionary learning, reinforcement learning and dynamic optimization, convex and sparse optimization, integer and combinatorial optimization, and computational intelligence-based optimization. For technologies, he mainly investigates on systems optimization technology for plant-wide production and inventory planning, production and logistics batching and scheduling, process optimization and optimal control; quality analytics technology such as process monitoring, equipment diagnosis, and product quality perception; industrial intelligence technology such as image and speech understanding and visualization. Meanwhile, he applies the above theories and technologies to engineering applications in steel manufacturing industry, equipment/chip manufacturing industry, energy industry, logistics industry and information industry.

He currently serves as an Associate Editor of IISE Transactions, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Journal of Scheduling, and International Journal of Production Research. Meanwhile, he is on the Editorial Board of Annals of Operations Research, and serves as an Area Editor of the Asia-Pacific Journal of Operational Research.

Yun Shi

Vice President & General Manager of Supply Chain at McDonald's (China)

Topic: Smart Supply Chain Architecture and Best Practice

Abstract: In this talk, I will share what is a "Smart Supply Chain". In particular, how to upgrade from a traditional supply chain to a smart supply chain,how to establish the "superconnections" in a smart supplychain, how the supply chain technology will evolve from digitalization to intelligience. I will also talk about the oragnizational change and personal growth needed in a smart supply chain.

About Speaker: Mr. Yun Shi （施云） is currently the Vice President and General Manager of Supply Chain at McDonald's (China), a Fortune 500 company and Gartner supply chain master. He is also an expert member of the Chinese Federation of Logistics and Purchasing, a senior researcher of the China Supply Chain Research Center of Xiamen University, and a visiting professor of EMBA, EDP and MBA in business schools such as Xiamen University, Shanghai Jiao Tong University and Shanghai University.

Mr. Shi has rich practical and theoretical experience in senior management from state-owned enterprises, private enterprises to multinational enterprises, and has served as one of the Heads of the Asia-Pacific supply chain department at Dell, and the Head of digital supply chain middle platform products, General Manager of supply chain operations, and Head of digital supply chain business solutions at Alibaba Group, Alibaba Retail, and Cainiao. Mr. Shi is especially experienced in using data science and technology to improve supply chain efficiency.

Mr. Shi has published two best-selling books "Supply Chain Architect: From Strategy to Operations", and "Smart Supply Chain Architecture: From Business to Technology" and has a large group of followers on his public WeChat account.