We have four invited talks in the conference, and the details of the invited talks and keynote speakers are given as follows (in alphabetical order of surnames)

Professor Alexander Dolgui

Professor Alexender Dolgui is a full Professor of Exceptional Class in France, head of Department on Automation, Production and Computer Sciences, and member of the Executive Committee of the LS2N - CNRS UMR 6004. He has served many professional society positions, such as Fellow of the European Academy for Industrial Management (AIM), Chair of the IFAC Technical Committee TC 5.2 "Manufacturing Modelling for Management and Control", etc. He is the Editor-in-Chief of the International Journal of Production Research, Consulting Editor of International Journal of Systems Science, and Area/Associate Editor or editorial board member of more than 25 well-known international journals. He has also served as Chairman, General Scientific Chair or Member of Scientific/Program Committee for more than 190 International Conferences. He has more than 770 publications, including over 170 refereed journal papers and more than 380 conference papers.


Combinatorial optimisation in the design of manufacturing systems: new problems and real life applications


A complex machine or machining line consists of a sequence of work positions through which products move one way in order to be processed. Designing such a production system represents a long-term decision problem involving different crucial decision stages. Combinatorial design is one of them; it mostly deals with assigning the set of indivisible units of work (named tasks or operations) to work positions (or stations). In literature, the most attention was paid for combinatorial design of assembly lines (assembly line balancing problems). In our work, we develop approaches and formulations of combinatorial design for machining lines and complex machines. All types of machining lines are considered: mass production transfer lines, flexible lines based on machining centers and reconfigurable manufacturing systems. A review of our results and their real life applications are presented.

Professor Dingzhu Du

Professor Dingzhu Du is a full Professor in Department of Computer Science, University of Texas at Dallas. He has ever solved several well-known conjectures, including Gilbet-Pollak Conjecture and Derman-Lieberman-Ross Conjecture, and has received many scientific and honorary awards. The proof of Gilbet-Pollak conjecture was selected by 1992 Year Book of Encyclopaedia, Britannica, as the first one among six outstanding achievements in mathematics in 1991. He is currently the Editor-in-Chief of Journal of Combinatorial Optimization, co-Editor-in-Chief of Discrete Mathematics, Algorithms and Applications, co-Editor-in-Chief of Computational Social Networks, and Editorial Board Member of over 10 journals. He has published over 200 peer-reviewed journal papers, 15 co-authored books, and more than 70 edited books or book chapters.


Viral Marketing of Online Game and Iterated Sandwich Method


The viral marketing is an important research subject on social networks. In study of viral marketing of online game, we may meet a nonsubmodular maximization problem. The sandwich method is a popular approach to deal with such a maximization problem. In this talk, we would like to introduce a new development about this method, the iterated sandwich method, and analysis on the computational complexity and the performance of the iterated sandwich method. This talk is based on a recent research work of research group in the Data Communication and Data Management Lab at University of Texas at Dallas.

Dr. Zhen Liu

Dr. Zhen Liu is the Chief Technology Officer (CTO) of Logitech, Fellow of IEEE. He is in charge of the technology strategies and their implementation for Logitech worldwide. He also build and directly supervise the Artificial Intelligence and of Cloud Engineering teams. Before joining Logitech, he has ever served several well-known international companies all over the world, and has excellent experience in operational management. He has ever been the head of Microsoft China Innovation Group, the Head of Nokia Research Center (NRC) Beijing, the Head of NRC Growth Economies Lab, and the senior manager of the Next Generation Distributed Systems department in IBM T.J. Watson Research Center. He has published more than 200 papers and obtained over 100 granted patents from US Patent Office. He has also served on the editorial boards of several journals including IEEE Transactions on Service Computing and the Journal of Performance Evaluation, and was the general chair or co-chair of several international conferences.

Professor Yugang Yu

Professor Yugang Yu is the Executive Dean and Yangtze Scholar Distinguished Professor of Logistics and Operations Management at the University of Science and Technology of China(USTC), PR China. His current research interests are in warehousing, supply chain management, and business analytics. He has published more than 80 papers in academic journals, including Productions and Operations Management, Transportation Science, IIE Transactions, International Journal of Production Research, European Journal of Operational Research, etc. Elsevier ranked him as one of “the most cited researchers in the Mainland of China” from 2014 to 2018. He has received a distinguished research scholar grant from the National Science Foundation of China (NSFC), Yangtze Scholar Distinguished Professorship from China Ministry of Education, and the first prize of natural science from China Ministry of Education.


Supply Chain Business Analytics in China


Nowadays, many Chinese companies have already accumulated abundant data resources and are seeking ways to improve their management by taking advantage of their data. In this talk, we present several instances of business analytics based on our research projects with Chinese companies. In each instance, we show the process of solving data driven problems in Chinese logistics and supply chain management, which basically includes three stages--causality analysis, prediction analysis, and optimization analysis. In particular, we find that classical methodology (like, regressions) works well in causality analysis, however, has limitation in prediction analysis; some advanced methodology (like, machine learning) exhibits good performance in prediction analysis, however, generates new challenges in the optimization analysis. Our research contributes to proposing several ideas and methodologies to analyze data, formulate data driven problems, and optimize data driven problems. At the end of this talk, we would like to introduce some innovative practices of Chinese companies and conclude several potential research directions of business analytics in China.

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