CombineNet's Advisors

CombineNet's Advisory Board is a who's who list of operations research and computer science academics and former Fortune 100 senior operations and supply chain executives. Their combined expertise and drive for innovation is instrumental in the development of CombineNet's product strategy and direction.

 
Business Advisors

Dr. John J. Coyle

Professor Emeritus of Supply Chain Management and Director of Corporate Relations for the Center for Supply Chain Research, Smeal College of Business, Penn State University

 

Larry C. Giunipero, Ph.D., C.P.M.

I.S.M. Professor of Purchasing/Supply Chain Management, Florida State University
Former Purchasing Supervisor at Westinghouse Electric Corporation

 

Margaret McGrath

Former Vice President of Purchasing and Distribution, PPG

 

Steve C. Rogers

Senior Consultant, The Warren Company & The Cincinnati Consulting Consortium
Adjunct Professor and Member of the Board of Advisors to the Management Department of the Williams School of Business, Xavier University in Cincinnati, OH
Former Director of Worldwide Purchases Mastery, The Procter and Gamble Company

 

Gregory Rosston, Ph.D.

Deputy Director, Stanford Institute for Economic Policy Research
Visiting Lecturer in Economics, Stanford University
Former Deputy Chief Economist, Federal Communications Commission (FCC)

 

Carol A. Rubeo

President, MSE Enterprises
Former Director of Purchases Innovation, The Procter and Gamble Company

 

Ram Shriram

Founder and Managing Partner, Sherpalo Ventures
Founding board member of Google Inc.
Former Vice President of Business Development, Amazon.com

 

Dr. Thomas Speh

James Evans Reese Distinguished Professor of Distribution
Associate Dean for Academic Affairs
Richard T. Farmer School of Business
Miami University of Ohio

 
Technical Advisors

Dr. Egon Balas

University Professor of Industrial Administration and Applied Mathematics, The Thomas Lord Professor of Operations Research, The Tepper School of Business, Carnegie Mellon University

Dr. Balas is a world renowned authority on Operations Research and is a member of the National Academy of Engineering. He was awarded the degree of Doctor of Mathematics honoris causa by the University of Waterloo in 2005, the EURO Gold Medal in 2001, the John von Neumann Theory Prize of the Institute for Operations Research and the Management Sciences in 1995, and the Senior U.S. Scientist Award of the von Humboldt Foundation in 1980-1981.

 

Dr. Craig Boutilier

Professor and Chair, Department of Computer Science, University of Toronto

Dr. Boutilier is a renowned authority on decision making under uncertainty (including computational methods, reinforcement learning, and preference elicitation). He has served as Associate Editor with the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research. He is a Fellow of the American Association of Artificial Intelligence.

 

Dr. George Nemhauser

A. Russell Chandler III Chair and Professor, School of Industrial and Systems Engineering, Georgia Institute of Technology

Dr. Nemhauser has served the Operations Research Society of America (ORSA) as council member, president, and editor of Operations Research, and he is past chair of the Mathematical Programming Society. He is the founding editor of Operations Research Letters, and co-editor of Handbooks of Operations Research and Management Science. His honors and awards include membership in the National Academy of Engineering, the Kimball Medal, the Lanchester Prize (twice awarded), and Morse lecturer of ORSA.

 

Dr. David Parkes

Assistant Professor of Computer Science, Harvard University

Dr. Parkes is a renowned authority on game theory, including auction mechanism design.

 

Dr. Rakesh Vohra

John L. and Helen Kellogg Professor of Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University

Dr. Vohra is a world renowned authority on decision sciences.

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Optimal

Optimal: A Definition

(1) A solution to an optimization problem which has the minimum (or maximum) value of the objective function.

(2) The time, space, resource, etc. complexity of an algorithm which matches the best known lower bound of a problem.

— Dictionary of Algorithms and Data Structures

  
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