一、报告主要内容

了解数据驱动优化、学习和控制的研究工作（1）数据驱动的机器学习（2）数据驱动的优化和决策（3）数据驱动建模和控制 （4）大数据分析和应用 等计算机领域前沿动态，

会议特邀报告人详情请见附件。

二、报告具体安排

时间：2022年10月29日 9:00-13:50

地点：成都香格里拉酒店

三、活动面向对象

2022级人工智能拔尖班、2022级研究生代表

四、参加要求

参加学生以班、党支部等单位进行组织，统一乘车往返（具体时间地点由辅导员另行通知）。参与学生需全程遵守学校以及酒店防疫要求。

计算机与人工智能学院

2022年10月27日

特邀报告：Development Directions of Industrial Intelligence

报告人：东北大学柴天佑教授
报告时间：2022年10月29日（星期六）上午9:00-9:50
报告地点：成都香格里拉酒店

报告摘要：
       In this talk, the role of industrial automation and information technology in the industrial revolutions is analyzed, as well as the current status and main problems in automation and information for manufacturing enterprise. The connotation of industrial intelligence and the challenges in realizing industrial intelligence are put forward. Based on the analysis and application cases of industrial internet and industrial artificial intelligence, the technical basis of industrial intelligence is presented. Then, the research directions, ideas and methods of industrial intelligence are proposed. 
报告人简介：

Tianyou Chai received the Ph.D. degree in control theory and engineering in 1985 from Northeastern University, Shenyang, China, where he became a Professor in 1988. He is the founder and director of the Center of Automation, which became a National Engineering and Technology Research Center and a State Key Laboratory. He is a member of Chinese Academy of Engineering, IFAC Fellow and IEEE Fellow. He has served as director of Department of Information Science of National Natural Science Foundation of China from 2010 to 2018. His current research interests include modeling, control, optimization and integrated automation of complex industrial processes. He has published 297 peer reviewed international journal papers. His paper titled Hybrid intelligent control for optimal operation of shaft furnace roasting process was selected as one of three best papers for the Control Engineering Practice Paper Prize for 2011-2013. He has developed control technologies with applications to various industrial processes. For his contributions, he has won 5 prestigious awards of National Natural Science, National Science and Technology Progress and National Technological Innovation, the 2007 Industry Award for Excellence in Transitional Control Research from IEEE Multipleconference on Systems and Control, and the 2017 Wook Hyun Kwon Education Award from Asian Control Association.

特邀报告题目二：Data analytics and optimization for smart industry Learning

报告人：东北大学唐立新教授
报告时间：2022年10月29日（星期六）上午9:50-10:40
报告地点：成都香格里拉酒店

报告摘要：
       Data analytics is the frontier basic research direction of industrial intelligence and one of the driving forces to promote scientific development. Systems optimization is not only the basic theory of intelligent manufacturing management, but also the core basic theory of industrial intelligence, as well as the heart and engine of data analytics. This talk discusses some interesting topics on 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 multiobjective ensemble learning. 
报告人简介：

Lixin Tang is 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 Head of Center for Artificial Intelligence and Data Science, and the Chair Professor of the Frontiers Science Center for Industrial Intelligence and Systems Optimization (National Platform). He is also a member of the discipline review group of the State Council for Control Science & Engineering, the Vice Director of Artificial Intelligence Committee of Ministry of Education, China, the Vice President of Operations Research Society of China (ORSC), and the Founding Director of Data Analytics and Optimization Society for Smart Industry for 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, computational intelligence-based optimization. For 7 technologies, he mainly investigates on engineering optimization technologies for plant-wide production and logistics planning, production and logistics batching and scheduling, process optimization and optimal control in product quality; data analytics technologies including knowledge discovery such as quality prediction, process monitoring and equipment diagnosis; perception understanding technologies such as image and speech understanding and visualization. Meanwhile, he applies the above theories and technologies to engineering applications in manufacturing, logistics and energy systems. He has published more than 127 papers in international journals such as IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, IEEE Transactions on Control Systems Technology, IEEE Transactions on Automation Science and Engineering, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Power Systems, Operations Research, Manufacturing & Service Operations Management, INFORMS Journal on Computing, IISE Transactions and Naval Research Logistics. His paper published on IISE Transactions received the Best Applications Paper Award of 2017. He currently serves as an Associate Editor of IISE Transactions, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Journal of Scheduling, International Journal of Production Research, and Journal of the Operational Research Society. 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. He was invited to act as the Cluster Chair in 2018 INFORMS International Conference, and the Track Chair in 9th IFAC Conference on Manufacturing Modelling, Management and Control.

特邀报告题目三：Data analytics and optimization for smart industry Learning

报告人：中国科学技术大学余玉刚教授
报告时间：2022年10月29日（星期六）上午11:00-11:50
报告地点：成都香格里拉酒店

报告摘要：
       Data-driven research is a trend in operations management research, and more and more top journal papers in recent years are supported by real-life data from enterprises and show the contribution of research solutions to enterprises. This report presents representative results of data-driven research based on the "Platform Supply Chain" project, a national innovation group project of the Foundation. Firstly, we study the optimal pricing problem of bank loyalty ecommerce platform system, and reveal the product pricing strategy and application scenarios of loyalty platform. Secondly, we study the multi-product pricing problem considering network effects, propose a consumer choice model and semimyopic pricing strategy, and practically test the obvious revenue enhancement it brings to enterprises. Finally, based on the Kiva robot logistics system in ecommerce logistics, we propose a joint goods shelving-storage location-goods picking optimization model and a two-stage optimization algorithm, which is effective in improving the existing strategies of enterprises.

报告人简介：

Yugang Yu is Chair Professor of Logistics and Operations Management at the University of Science and Technology of China (USTC). He obtained his PhD in Management Science and Engineering from the School of Management, USTC in 2003. His current research interests are in logistics, supply chain management and business analytics. He has published more than 150 papers in academic journals, including Productions and Operations Management, Manufacturing & Service Operations Management, Information Systems Research, Transportation Science, IISE Transactions, International Journal of Production Research, European Journal of Operational Research, and Navel Research Logistics. His papers were cited more than 3000 times, and Elsevier ranked him as one of “the most cited researchers in the Mainland of China” in 2014-2021. He received a career development VENI project from the Netherlands Organization for Scientific Research (NWO), a distinguished research scholar grant from the National Science Foundation of China (NSFC), and the first prize of natural science from China Ministry of Education. He is principal investigator for NSFC National Innovation Research Group Project, and vice president for the China Society of Logistics.

特邀报告题目四：Interactive Multiobjective Optimization to Support Data-Driven Decisions

报告人：于韦斯屈莱大学Kaisa Miettinen教授
报告时间：2022年10月29日（星期六）下午13:00-13:50
报告地点：成都香格里拉酒店

报告摘要：
       In data analytics, we can use descriptive analytics to understand the data or predictive analytics to make prediction, but to make recommendations or decisions based on the data, we need prescriptive or decision analytics. We can fit models in the data and derive decision problems. Real-world decisions are typically characterized by multiple conflicting objectives that should be considered simultaneously. Thus, decision problems often have multiple objectives. We can support decision making by applying multiobjective optimization methods. 

We discuss some examples of data-driven optimization problems, where a decision maker is supported in making better decisions. We formulate multiobjective optimization problems using data available and apply interactive multiobjective optimization methods to solve the problems. In interactive methods, a decision maker iteratively directs the search with one’s preference information to find the best balance between the conflicting objectives. In this way, (s)he gains insight in the phenomena involved and learns about what kind of solutions are available as well as what kind of preferences are feasible. Based on the learning, the decision maker can modify preferences and eventually gain confidence in the final solution. We also briefly introduce the open-source software framework DESDEO (desdeo.it.jyu.fi), which contains implementations of different interactive methods.

报告人简介：

Kaisa Miettinen is Professor of Industrial Optimization at the University of Jyvaskyla. Her research interests include theory, methods, applications and software of nonlinear multiobjective optimization including interactive and evolutionary approaches. She heads the Multiobjective Optimization Group and is the director of the thematic research area called Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, www.jyu.fi/demo). She has authored over 200 refereed journal, proceedings and collection papers, edited 17 proceedings, collections and special issues and written a monograph Nonlinear Multiobjective Optimization. She is a member of the Finnish Academy of Science and Letters, Section of Science and has served as the 10 President of the International Society on Multiple Criteria Decision Making (MCDM). She belongs to the editorial boards of seven international journals. She has previously worked at IIASA, International Institute for Applied Systems Analysis in Austria, KTH Royal Institute of Technology in Stockholm, Sweden and Helsinki School of Economics, Finland. She has received the Georg Cantor Award of the International Society on MCDM for independent inquiry in developing innovative ideas in the theory and methodology.