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Keynote Lectures

Trends and Directions in Distributed Knowledge
Elias M. Awad, University of Virginia, United States

Convex Quadratic Programming in Graphs: Links between Continuous and Discrete Problems
Domingos Moreira Cardoso, University of Aveiro, Portugal

Transforming a Complex, Global Enterprise: Operations Research for Management Innovation
Greg H. Parlier, INFORMS, United States

 

Trends and Directions in Distributed Knowledge

Elias M. Awad
University of Virginia
United States
 

Brief Bio

Dr. Elias M. Awad is the Virginia Bankers Association Professor Emeritus of Bank Management at the University of Virginia.  He has over 45 years IT experience in the academic, publishing, and consulting fields.  He is one of the world’s leading IT and Management seminar and workshop presenters in the banking industry. 

Dr. Awad is the CEO of International Technology Group, LTD, a management consulting group with a central office in Charlottesville, Virginia and overseas offices in Beirut, Damascus, and Geneva.  His book publication record goes back to the early 1960s with best sellers in management information systems, systems analysis and design, database management, knowledge management, human resources management, expert systems, and knowledge automation systems.  (See www.amazon.com). His publications have been translated into German, Spanish, Portuguese, Chinese, Arabic, Russian, and Braille.  As a senior management and IT consultant, he has delivered professional seminars and workshops internationally to major corporations and government agencies in 26 countries, including South Korea, Russia, China, Australia, Cambodia, Canada, Mexico, Philippines, Kazakhstan, Moldova, Uzbekistan, Malaysia, Saudi Arabia, Egypt, Lebanon, and Jordan.  Contact email address:  ema3z@virginia.edu


Abstract

The core of success in today's global commerce is knowledge centricity affecting individuals, organizations, government, and industries alike.  With explicit knowledge tucked away in knowledge bases, a key trend in knowledge transfer (KT) is intrinsic knowledge intensification against cultural, structural, and cognitive barriers.  KT facilitates knowledge exchange via teamwork based on mutual trust, tested integrity, and a shared vision in a knowledge-centric environment.

Networking, per se, facilitates KT but cannot guarantee implementation or integration.  As a result, behavioralists such as knowledge brokers with technology background are slowly being employed to assure connectivity, promote collaboration, instill confidence, enhance communication, and enrich decision-making.  It is a daunting challenge that will take time and experience for KT to take hold.

In the final analysis, KT imperatives must address time management ("when" of KT), HRM ("who" of KT), security management ("why" of KT), task management ("what" knowledge should be transferred), and network management ("how" knowledge should be transferred).  It is an endeavor requiring intelligence, foresight, and leadership in an increasingly complex world.



 

 

Convex Quadratic Programming in Graphs: Links between Continuous and Discrete Problems

Domingos Moreira Cardoso
University of Aveiro
Portugal
 

Brief Bio

Full professor at the Mathematics Department of the University of Aveiro, Portugal, since 2005, having got the PhD degree in Mathematics at University of Aveiro in 1993 .

President of the Portuguese OR Society – APDIO, since January 2012. Scientific Coordinator of the Centre for Optimization and Control Theory (CEOC) between 2002 and 2009. Scientific Coordinator Optimization, Graph Theory and Combinatorics Research Group of CEOC until the end of 2009 and of the Center for Research in Mathematics and Applications (CIDMA) since January 2010. Coordinator of several research projects (“Algebraic Methods in Graph Theory” 2005-2006, “Optimization of Diversity and Distribution of Cable Configuration for Automobile Industry (OPT-DDCC)” 2005, “Optimal Splice Location (OPT-SLC)" 2008-2009, “Graph Spectra and Applications (GSA)" 2011-2013”). Member of the Board of Directors of Centro Internacional de Matemática (CIM), between 2004 and 2008. Member of the Managing Board of EUROPT - The Continuous Optimization Working Group of EURO. Director of the Doctoral Program in Mathematics of University of Aveiro since 2008. Co-author of 1 book and author or co-author of about 60 papers in international journals, book chapters and proceedings. The main research topics includes optimization, operations research, discrete mathematics and spectral graph theory.



Abstract

 Many real-world applications have combinatorial problems in graphs as their mathematical models. For instance, to decide on the Hamiltonicity of a graph, to determine a maximum stable set or a maximum clique, maximum induced matchings, are examples of these combinatorial problems and most of them can be generically formulated as the determination of a maximum order k-regular induced subgraph. Since the landmark publication of Motzkin and Straus in [T. S. Motzkin and E. G. Straus, Maxima for graphs and a new proof of a theorem of Turan, Canadian Journal of Mathematics 17 (1965): 533-540], several combinatorial optimization problems have been intensively studied using continuous optimization approaches, namely quadratic programming. In particular, the use of convex quadratic programming techniques have produced very interesting results, regarding the determination of lower and upper bounds on the order of k-regular induced sub-graphs  (which define stable sets when k=0, induced matchings  when k=1, and so on) . There are many families of graphs for which these bounds are attained and where we are able to determine also a corresponding maximum order k-regular induced subgraph. In this presentation, the advances obtained by the application of convex quadratic programming techniques on combinatorial problems in graphs are surveyed and most of the main results, connecting continuous optimization properties with the combinatorial structure of the graph, are presented. In fact, in many cases, the combinatorial structure of a graph implies that the bounds obtained by convex quadratic programming are achieved and the behaviour of the optimal values of sequences of convex quadratic problems implies the presence of particular combinatorial structures in graphs. Furthermore, a few open problems related with the main challenges in this topic are proposed.



 

 

Transforming a Complex, Global Enterprise: Operations Research for Management Innovation

Greg H. Parlier
INFORMS
United States
 

Brief Bio

Greg is a retired US Army Colonel. A West Point graduate and Air Defense Artillery officer, he began his 30-year career as a section leader in an airborne infantry battalion and retired as the senior, most experienced operations research analyst in the Army. A combat veteran with multiple tours in the 82nd Airborne Division, he also served as an assistant professor of operations research at West Point. His five OR assignments focused on building, developing, and leading successively larger analytical teams confronting increasingly more demanding transformational challenges in large, complex commands. The organization he led at US Army Recruiting Command earned distinction as a finalist for the INFORMS Franz Edelman Award. He holds graduate degrees in Operations Research, Systems Engineering, National Security Studies, and was a National Defense Fellow at MIT. Currently, Greg is an independent consultant and serves on the adjunct research staff at the Institute for Defense Analyses (IDA) where he has been an advisor to several foreign governments, and recently served as IDA’s team leader and senior deployed analyst in Iraq.


Abstract

 “Management innovation as a strategic technology” - MIST, for short - is a new concept for transformational change in large-scale, complex, interdependent organizations that comprise an extended enterprise. The presentation will develop an appreciation and understanding of MIST and its emphasis on analytical architectures as a strategic management means for improved performance in public sector organizations and government institutions. The three conceptual building blocks for MIST include: data-driven, operations research–based, decision support systems (also referred to as “business intelligence” or “advanced analytics”); a transformational, rather than incremental, approach to strategic planning guided by an “engine for innovation”; and an “integrated” management science to increase the likelihood for successful transformative change by enabling, rather than impeding, organizational implementation. By describing the components of MIST and demonstrating their application to challenges faced by the US Army’s materiel enterprise, others may be encouraged to consider and, where appropriate, adapt and apply MIST to many other daunting national security challenges as well.



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