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Logical Processing for Integer Programming

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  • Monique Guignard
  • Ellis Johnson
  • Kurt Spielberg

Abstract

This paper reviews tools which have great potential for reducing the difficulty of solving IP (and also MIP) problems, if well implemented in solvers. Recent experiments with Branch and Bound solvers, in connection with “Short Start Features”, have shown that implementations need and can still be improved. Concepts which are likely to be specially important for (0,1) MIP are pointed out. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Monique Guignard & Ellis Johnson & Kurt Spielberg, 2005. "Logical Processing for Integer Programming," Annals of Operations Research, Springer, vol. 140(1), pages 263-304, November.
    • Handle: RePEc:spr:annopr:v:140:y:2005:i:1:p:263-304:10.1007/s10479-005-3974-4
    DOI: 10.1007/s10479-005-3974-4
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    References listed on IDEAS

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    1. Fisher, M.L. & Nemhauser, G.L. & Wolsey, L.A., 1978. "An analysis of approximations for maximizing submodular set functions - 1," LIDAM Reprints CORE 334, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    Cited by:

    1. L. Foulds & B. Toklu & J. Wilson, 2009. "Modelling either-or relations in integer programming," Annals of Operations Research, Springer, vol. 166(1), pages 203-222, February.
    2. Escudero Bueno, Laureano F. & Garín Martín, María Araceli & Merino Maestre, María & Pérez Sainz de Rozas, Gloria, 2011. "A parallelizable algorithmic framework for solving large scale multi-stage stochastic mixed 0-1 problems under uncertainty," BILTOKI 1134-8984, Universidad del País Vasco - Departamento de Economía Aplicada III (Econometría y Estadística).

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