Formulating a Mixed Integer Programming Problem to Improve Solvability

Cynthia Barnhart, Ellis L. Johnson, George L. Nemhauser, Gabriele Sigismondi and Pamela Vance
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Cynthia Barnhart: Georgia Institute of Technology, Atlanta, Georgia
Ellis L. Johnson: Georgia Institute of Technology, Atlanta, Georgia
George L. Nemhauser: Georgia Institute of Technology, Atlanta, Georgia
Gabriele Sigismondi: TNT Tracos S.p.A., Torino, Italy
Pamela Vance: Auburn University, Auburn, Alabama

Operations Research, 1993, vol. 41, issue 6, 1013-1019

Abstract: A standard formulation of a real-world distribution problem could not be solved, even for a good solution, by a commercial mixed integer programming code. However, after reformulating it by reducing the number of 0-1 variables and tightening the linear programming relaxation, an optimal solution could be found efficiently. The purpose of this paper is to demonstrate, with a real application, the practical importance of the need for good formulations in solving mixed integer programming problems.

Keywords: programming; integer; algorithms: solving a distribution problem; transportation; models: solving a distribution problem (search for similar items in EconPapers)
Date: 1993
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