Making the Most of Structure in Constraint Models

Combinatorial problems are critical in a wide range of areas. As a result, there are many solving techniques available, each with different strengths. High-level modelling languages allow problems to be specified once, and then be compiled for different solving techniques to discover which to use. This thesis focuses on model structure, showing how it can be exploited to improve compilation and solving. In particular, it proposes a framework for extracting and using structure to solve problems faster and to help users write better models and debug them. In doing this, it makes it easier to solve hard combinatorial problems.