Innovative Applications of Constraint Programming

Constraint programming (CP) is a declarative paradigm that enables us to model a problem in the form of constraints to be satisfied. It offers powerful constraint solvers which, by implementing general-purpose search techniques, are fast and robust to address complex constraint models automatically. Constraint programming has attracted the attention of people from various domains. By separating the definition of a problem from its solution, it is more natural for people to implement the program directly from the problem specification, reducing the cost of development and future maintenance significantly. Furthermore, CP provides the flexibility of choosing a suitable solver for a problem of a given nature, which overcomes the limitations of a unique solver. Thanks to this, CP has allowed many non-domain experts to solve emerging problems efficiently. This thesis studies the innovative applications of CP by examining two topics: constraint modeling for several novel problems, and automatic solver selection. For the modeling, we explored two case studies, namely the (sub)group activity optimization problem, and the service function chaining deployment problem that comes from the Software Defined Network (SDN) domain. Concerning the solver selection, we improved an algorithm selection technique called “SUNNY”, which generates a schedule of solvers for a given problem instance. In this work, we demonstrate with empirical experiments that the procedure we have designed to configure SUNNY parameters is effective, and it makes SUNNY scalable to an even broader range of algorithm selection problems not restricted to CP.