This paper deals with four solvers for combinatorial problems: the commercial state-of-the-art solver ILOG OPLSTUDIO, and the research answer set programming (ASP) systems DLV, SMODELS and CMODELS. The first goal of t...
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This paper deals with four solvers for combinatorial problems: the commercial state-of-the-art solver ILOG OPLSTUDIO, and the research answer set programming (ASP) systems DLV, SMODELS and CMODELS. The first goal of this research is to evaluate the relative performance of such systems when used in a purely declarative way, using a reproducible and extensible experimental methodology. In particular, we consider a third-party problem library, i.e., the CSPLib, and uniform rules for modelling and instance selection. The second goal is to analyze the marginal effects of popular reformulation techniques on the various solving technologies. In particular, we consider structural symmetry breaking, the adoption of global constraints, and the addition of auxiliary predicates. Finally, we evaluate, on a subset of the problems, the impact of numbers and arithmetic constraints on the different solving technologies. Results show that there is not a single solver winning on all problems, and that reformulation is almost always beneficial: symmetry-breaking may be a good choice, but its complexity has to be carefully chosen, by taking into account also the particular solver used. Global constraints often, but not always, help OPL, and the addition of auxiliary predicates is usually worth, especially when dealing with ASP solvers. Moreover, interesting synergies among the various modelling techniques exist.
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