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

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Pages (from-to)423-452
Number of pages30
JournalAnnals of Operations Research
Volume235
Issue number1
DOIs
StatePublished - 1 Dec 2015

Keywords

  • Constraint programming
  • CSP
  • SAT encoding
  • Set constraints
  • Social golfer problem

Fingerprint

Dive into the research topics of 'Set constraint model and automated encoding into SAT: application to the social golfer problem'. Together they form a unique fingerprint.

Cite this