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Set constraint model and automated encoding into SAT: application to the social golfer problem

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Listed:
  • Frédéric Lardeux
  • Eric Monfroy
  • Broderick Crawford
  • Ricardo Soto

Abstract

On the one hand, constraint satisfaction problems allow one to expressively model problems. On the other hand, propositional satisfiability problem (SAT) solvers can handle huge SAT instances. We thus present a technique to expressively model set constraint problems and to encode them automatically into SAT instances. We apply our technique to the social golfer problem and we also use it to break symmetries of the problem. Our technique is simpler, more expressive, and less error-prone than direct modeling. The SAT instances that we automatically generate contain less clauses than improved direct instances such as in Triska and Musliu (Ann Oper Res 194(1):427–438, 2012 ), and with unit propagation they also contain less variables. Moreover, they are well-suited for SAT solvers and they are solved faster as shown when solving difficult instances of the social golfer problem. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Frédéric Lardeux & Eric Monfroy & Broderick Crawford & Ricardo Soto, 2015. "Set constraint model and automated encoding into SAT: application to the social golfer problem," Annals of Operations Research, Springer, vol. 235(1), pages 423-452, December.
    • Handle: RePEc:spr:annopr:v:235:y:2015:i:1:p:423-452:10.1007/s10479-015-1914-5
    DOI: 10.1007/s10479-015-1914-5
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    References listed on IDEAS

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    1. Markus Triska & Nysret Musliu, 2012. "An improved SAT formulation for the social golfer problem," Annals of Operations Research, Springer, vol. 194(1), pages 427-438, April.
    2. S. Prestwich, 2003. "Negative Effects of Modeling Techniques on Search Performance," Annals of Operations Research, Springer, vol. 118(1), pages 137-150, February.
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