George L. Nemhauser

Past Awards

2010 Optimization Society Khachiyan Prize: Awardee(s) [+show more]
2010 - Awardee
1991 Philip McCord Morse Lectureship Award: Winner(s)
1989 Frederick W. Lanchester Prize: Winner(s) [+show more]

The Lanchester Prize Committee chose three "deserving" individuals and two "excellent" publications for the 1989 prize.

The 1989 Lanchester Prize was shared by Jean Walrand, author of An Introduction to Queueing Networks (Prentice Hall, 1988), and George L. Nemhauser and Laurence A. Wolsey, whose combined efforts produced Integer and Combinatorial Optimization (John Wiley, 1988). Nemhauser becomes the first two-time winner of the prestigious award. He won it in 1977 for the paper, "Location of Bank Accounts to Optimize Float: An Analytic Study of an Exact and Approximate Algorithm," coauthored by G. Cornuejols and M.L. Fisher.

"George and I are both delighted and surprised by this award, " said Wolsey, speaking on behalf of himself and Nemhauser. "It cannot be often that an introduction to an introduction gets a prize. Two years ago the Lanchester Prize was, you remember, given to Lex Schrijver for his remarkable book, Linear and Integer Programming, which was just the introductory chapters of a book on combinatorial optimization he had been writing for years. One could regard our book as an introduction to his book."

Wolsey thanked a long list of individuals and institutions "who inspired and helped us along the way," incl`uding Martin Beale, Jack Edmonds, Ray Fulkerson, Ralph Gomory, Jack Mitten, Jerry Shapiro, John Little, Cornell, M.l.T., Georgia Tech and the University of Louvain in Brussels, Belgium.

In their book Integer and Combinatorial Optimization, Nemhauser and Wolsey set out to write a graduate text and reference hook for researchers and practitioners that unifies theory and algorithms. The committee found the authors far exceeding their goal, adding that "many believe they have defined the way people will think about and discuss the field of integer programming and combinatorial optimization for years to come."

"George Nemhauser and Laurence Wolsey capture the progress that has been made in discrete optimization over the past two decades. They include many results that heretofore have only appeared in research journals and monographs, and have done so in a lucid and accessible style. Their coverage is broad, capturing essentially all of this rapidly changing field but also condenses, simplifies and synthesizes this remarkable amount of information. "

The citation sums up the importance of Nemhauser and Wolsey's work with the following words: "Whether readers are planning to conduct research, use practical algorithms, or are fascinated by theory in discrete optimization, this book is a must for their libraries."

Nemhauser, of the School of Industrial and Systems Engineering at Georgia Institute of Technology in Atlanta, and Wolsey, of the Center for Operations Research and Economics at the Catholic University of Louvain, Belgium, received honorable mention in the Lanchester Prize competition last year.

1988 George E. Kimball Medal: Awardee(s)
1977 Frederick W. Lanchester Prize: Winner(s) [+show more]

Two papers and their four authors were awarded the 1977 Lanchester Prize at the ORSA/TIMS Joint Meeting in Los Angeles. The winning papers were:

  • Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms," by Gerard Cornuejols, Marshall L. Fisher and George L. Nemhauser, Management Science, April 1977.
  • "A Probabilistic Analysis of Partitioning Algorithms for the Travelling-Salesman Problem in the Plane," by Richard M. Karp, Mathematics of Operations Research, August 1977.

The award was presented by Peter J. Kolesar of Columbia University, Chairman of the 1977 Lanchester Prize Committee. Dr. Kolesar made the following comments:

  • The solution of many important practical operations research problems depends in part on our ability to solve efficiently a wide variety of combinatorial optimization problems of formidable size. Operations Researchers, practitioners and theoreticians alike have struggled with these problems for nearly thirty years. Only relatively recently have theoretical results of Steven Cook and Richard Karp confirmed what some operations researchers had long suspected -- that many of these problems are intrinsically hard, that they are intimately related to each other, and that it is unlikely we will ever have algorithms guaranteed to find optimal solutions to large problems without excessive computational labor.
  • Thereupon, researchers have given increasing attention to the study of heuristic algorithms of the type practitioners have long been compelled to use. Much of this work focuses on answering the question of how badly a heuristic might perform — the study of worst case bounds. The work of Ronald Graham, Michael Garey, and David Johnson at Bell Laboratories broke the ground for this pursuit. The conservatism of worst case bounds does not always provide adequate guidance to the practitioner. Indeed, actual experience with heuristics is often quite good, and this has led to the study of a related set of questions about the performance of heuristic algorithms on average, and about their relative frequency of bad performance. Actually, it appears that both worst case and average case analysis will be useful in improving the design and performance of heuristics.
  • In recognition of the quality of their contributions to the science and art of heuristic problem solving, and in the expectation that this line of inquiry will continue to contribute to real understanding and better ability to solve important practical problems, we award the 1977 Lanchester Prize to two papers. The first paper analyzes a particular banking application of the plant location problem from a worst case point of view. It obtains the sharpest possible bounds for some heuristics and then compares them computationally. The second paper is the first major application of probabilistic analysis to a combinatorial optimization problem. It develops the ideas necessary for this analysis and applies them to a powerful partitioning technique for solving the traveling salesman problem.


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