Marshall L. Fisher

Marshall Fisher received his SB in electrical engineering and his MBA and PhD in operations research, all from MIT. In 1975, he joined the faculty of the Wharton School and is currently the UPS Professor of Operations and Information Management and co-director of the Fishman-Davidson Center for Service and Operations Management. Prior to joining Wharton, he was a systems engineer in the Boston Manufacturing and Distribution Sales office of IBM and on the faculty of the University of Chicago Graduate School of Business.

Dr. Fisher’s early research focused on combinatorial optimization. In 1977, he received the Lanchester Prize for his Management Science paper “Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms," (co-authored with G. Cornuejols, and G.L. Nemhauser). His 1981 paper in Management Science “The Lagrangian Relaxation Method for Solving Integer Programming Problems," was voted by the membership of INFORMS as one of the ten most influential papers published in Management Science during its 50 year history.

More recently, his research has focused on supply chain management, including private truck fleet scheduling, supply chain management for hard to predict products and a new methodology for retail marketing. In 1983, he and his co-researchers received the Edelman Prize for development of a large-scale logistics planning model for a major industrial gas firm. In 1984, he and his co-author R. Jaikumar received The National Council of Physical Distribution Management E. Grosvenor Plowman award for the paper "Computers in Transportation: From Integration to Intelligence".

He co-founded and served as Chairman of two companies based on his research: The first: Distribution Analysis, Research and Technology, Inc provided private truck fleet optimization software and merged with Manugistics Inc. in 1990. The second, 4R Systems, Inc. provides supply chain planning software to retailers of short lifecycle products.

Dr. Fisher served as a TIMS Council Member during 1985-87 and TIMS President during 1988-89. Within ORSA he chaired the 1979 Lanchester Prize Selection Committee, the Publications Committee (1981-1982) He has served as Chairman of the Selection Committee for the new Editor of Operations Research (1981-1982) and Manufacturing & Service Operations Management (2001). He has also served on the Organizing Committee for the Annual Practice Conference (2003-2005), the Search Committee for the 2007 Morse Lecturer, and the Search Committee for the 2006 INFORMS Practice Prize. He has also given numerous plenary and keynote addresses at INFORMS conferences.

He is a member of the National Academy of Engineering, a Fellow of three societies: INFORMS, the Production and Operations Management Society, and the Manufacturing and Service Operations Management Society and was The 2006 Philip McCord Morse Lecturer.

For his many contributions to the field of operations research and management science, and for his distinguished service to INFORMS and its predecessor organizations, the Institute for Operations Research and the Management Sciences expresses its sincere appreciation to Marshall L. Fisher by awarding him the 2007 George E. Kimball Medal.

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.