Hybrid Modelling for Robust Solving

We study a balanced academic curriculum problem and an industrial steel mill slab design problem. These problems can be modelled in different ways, using both Integer Linear Programming (ILP) and Constraint Programming (CP) techniques. We consider the utility of each model. We also propose integrating the models to create hybrids that benefit from the complementary strengths of each model. Experimental results show that hybridization significantly increases the domain pruning and decreases the run-time on many instances. Furthermore, a CP/ILP hybrid model gives a more robust performance in the face of varying instance data.