Keynote Speakers       


Keynote Speakers:

  • Fred Glover, University of Colorado, USA, and Gary Kochenberger, University of Colorado Denver, USA

  • Peizhuang Wang, Liaoning Technical University, China

  • Jifa Gu, Chinese Academy of Sciences, China

Meta-Analytics for Real World Inventory Management Optimization

Fred Glover and Gary Kochenberger
University of Colorado and University of Colorado Denver


Supply chain optimization and inventory management optimization stand out as prominent concerns within the realm of modern business analytics. Surprisingly, while supply chain optimization has been in the spotlight for many years, its crucial inventory management component has often been neglected. Companies that have invested in supply chain optimization have typically allowed inventory management policies to be determined by outdated textbook models or even ¡°managerial guesswork,¡± without consideration of employing advanced analytics technology.
Recent discoveries have shown, however, that many organizations can save millions of dollars annually by applying state-of-the-art analytics to optimize inventories. Moreover, substantial gains in profits over and above those obtained from ¡°good¡± analytics approaches result by using special models from a meta-analytics framework, which combines metaheuristics with analytics.
We demonstrate this finding by an integrated meta-analytics platform that combines network optimization, netform modeling and simulation optimization for inventory management. We report computational tests that compare our meta-analytics approach to the status quo methodology customarily used for inventory management and to a recent innovation in inventory management reported to save over $90 million for a major U.S. retail firm. The results show that our meta-analytics approach provides dramatic improvements over both of these alternative approaches, yielding appreciably better levels of service and greater cost savings, and having broad implications for modern inventory management policies.

Author’s  short biographical note:

Fred Glover

Dr. Fred Glover is an Emeritus professor at the University of Colorado at Boulder where he held the title of Distinguished Professor in the Leeds School of Business. He serves as the Chief Technology Officer in charge of algorithmic design and strategic planning initiatives for OptTek Systems, Inc., in Boulder, Colorado. Dr. Glover is widely known for his work in the applications of computer decision support systems, including industrial planning, financial analysis, systems design, energy and natural resources planning, logistics, transportation and large-scale allocation models. He has authored or co-authored more than 370 published articles and eight books in the fields of mathematical optimization, computer science and artificial intelligence.
Dr. Glover is the recipient of the highest honor of the Institute of Operations Research and Management Science, the von Neumann Theory Prize, and is an elected member of the National Academy of Engineering. He has also received numerous other awards and honorary fellowships from organizations around the world.

Fred Glover

Dr. Gary A. Kochenberger is a professor of Business Analytics at the University of Colorado at Denver where he is a co-director of the Business Analytics graduate program. He is known for his research on applied optimization addressing important issues in combinatorial optimization, nonlinear programming, resource allocation, pattern classification, data mining, and related areas. He has co-authored three books and more than seventy refereed articles. Dr. Kochenberger has given invited presentations at national and international meetings in the U.S., Canada, China, Japan, and several venues in Europe. He has also served as principal investigator and supporting investigator on numerous grants.
In addition to his academic work, Dr. Kochenberger serves as a Senior Consultant for OptTek Systems, Inc., engaged in research into practical applications of optimization and simulation applied to portfolio analysis, workforce planning, and a variety of other applications of operations research.

Factor Space: A Mathematical Framework for New Paradigm Driven by Big Data

Peizhuang WANG
Professor, Intelligent Engineering and Math Institute, Liaoning Technical University, China


The fourth paradigm, suggested by Tony Hey, is driving a scientific revolution with big data. The paradigm transformation requires a new kind of framework as the plate of intelligent description. The paper aims to explain that factor space theory is a mathematical framework for new paradigm under big data.

Author’s  short biographical note:

Prof. P. Z. Wang is an academic leader in Fuzzy Sets, Factor Space theory and its applications in AI and DS in China and the world. He graduated (1957, B.S.) from, and worked, from assistant to professor, in Dept. of Math., Beijing Normal University (1957-1998). Prof. Wang was the Vice President of Chinese Fuzzy mathematics and System Association (1981-1990), and the Co-Chairman of the International Fuzzy Systems Association (1989-1993). He established the Falling Shadow Theory to build a serious mathematical basis for Evidence theory and Set-valued statistics methods in his book ¡°Fuzzy Sets and Falling Shadow of Random Sets ¡±(1985). His Ph.D students manufactured second Fuzzy Inference Machine under his supervision (May,1988). Comparing with Yamakawa¡¯s first one (July, 1987), Inference velocity from 10million/sec rises to 15million/sec. He was the chief of united project of Chinese Science Foundation ¡°Fuzzy Information Processing and Fuzzy Computing¡± (consisting of 37 Universities and Institutes) (1998-1991).
Professor Wang initiated Factor Space theory in 1982. In the same year, Formal Concept Analysis and Rough Sets have been initiated simultaneously. The three branches belong to intelligent mathematics togather: They publicly put knowledge as the object of researching. Factor space provides framework of knowledge representation by his book ¡± Mathematical theory of knowledge representation¡± (1994) and in the book ¡°Fuzzy System Theory and Fuzzy Computer ¡±(1995)
He organized the First Asian Fuzzy Engineering Symposium (AFSS) in Singapore (1993). He was members of editor boards of several Journals such as Information Technology and Decision Making, Sift Computing, Uncertainty, Fuzziness and Knowledge-based Systems; He was Editor of book series such as Advances in Fuzzy Systems: Theory and Applications. From 2012, Prof Wang has been serving as the director of Intelligent Engineering and Mathematics Institute, Liaoning Technical University and engaging to build fundamental theory for data science based on factor space.


Systems engineering and Systems Science: 10 big advances related to Big data

Jifa GU
Professor ,Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences


Now the big data has attracted a lot of scholars and practitioners to investigate and apply it from different angles. This paper will not contact too much on the theory and technical tolls from Information science. Instead we will investigate it from system point of view now. The ten big achievements by using the systems theory and technologies closed related to the Big data will be emphasized. At first we will mention the big system or open giant complex system (OCG) generating the big data naturally. The concept of OCG had been proposed by Qian XS et al in 1990. Then big data requiring the various methodologies, methods and technical tolls for dealing with them deeply. There are methodology-meta-synthesis system approach for solving OCG problems, various method as analytics to analyze the big data and new technology-complex network and Hall of workshop for Meta-synthetic Engineering. Both The methodology-meta-synthesis system approach and Hall of workshop for Meta-synthetic Engineering again proposed by Qian et al in 1990 and 1992. The various complex network, like the internet and internet of things themself is the object of OCG and the complex network analysis and social network analysis are very useful tolls to analyze various network and mining the deep and useful information from them. Then if we deal with the social problems we have to treat the big human behavior and big social psychology. Finally if we wish develop our present system well and create new systems we will use the wisdom and the Meta-synthesis of wisdom investigated by Qian in his late tens of years since 1992. Finally we may call them altogether 10 ¡°Big¡± :

1.Big system

2.Big network

3.Big Data

4.Big analytic

5. Big Methodology

6.Big psychology

7.Big behavior

8.Big meta-synthesis

9.Big Hall

10.Big wisdom

We will illustrate these 10 ¡°Big¡± in more details.

Author’s  short biographical note:

Jifa GU, Professor ,Academy of Mathematics and Systems Sciences, Chinese Academy of Sciences, Bachelor, Peking University (1957), PhD, Institute of Mathematics, USSR Academy of Sciences(1963), working in the fields of Operations Research and Systems Engineering. He published more than 30 books and 200 journal papers. He participated in practices on missile, energy, environment, water resource, regional strategy and various projects on evaluation. 1995 with Dr. Zhu ZC proposed Wuli-Shili-Renli system approach and got applications in many practical cases. In recent ten years he has engaged in the study and application of Meta-synthesis system approach and Knowledge Science He has participated in several national research programs related to social issues, digging experiences from TCM veteran doctors and study on the collective behaviors in Shanghai World Expo. He had been President of Systems Engineering Society of China, President of International Federation of System Research. Now he is academician and vice president of International Academy of System and Cybernetics Sciences, academician of Euro-Asia Academy of Sciences.


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