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Formulating a Mixed Integer Programming Problem to Improve Solvability

Author

Listed:
  • 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)

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.

Suggested Citation

  • Cynthia Barnhart & Ellis L. Johnson & George L. Nemhauser & Gabriele Sigismondi & Pamela Vance, 1993. "Formulating a Mixed Integer Programming Problem to Improve Solvability," Operations Research, INFORMS, vol. 41(6), pages 1013-1019, December.
    • Handle: RePEc:inm:oropre:v:41:y:1993:i:6:p:1013-1019
    DOI: 10.1287/opre.41.6.1013
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    Cited by:

    1. Hadjiat, Malika & Maurras, Jean-Francois & Vaxes, Yann, 2000. "A primal partitioning approach for single and non-simultaneous multicommodity flow problems," European Journal of Operational Research, Elsevier, vol. 123(2), pages 382-393, June.

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