Modelling discrete optimisation problems in constraint logic programming

Constraint logic programming has become a promising new technology for solving complex combinatorial problems. In this paper, we investigate how (constraint) logic programming can support the modelling part when solving discrete optimisation problems. First, we show that the basic functionality of algebraic modelling languages can be realised very easily in a pure logic programming system like Prolog and that, even without using constraints, various additional features are available. Then we focus on the constraint-solving facilities offered by constraint logic programming systems. In particular, we explain how the constraint solver of the constraint logic programming language CLP(PB) can be used in modelling 0–1 problems.