Introduction
Constraint Programming is an approach to problem solving that has been particularly successful for dealing with nonlinear constraint relations over discrete variables. In the following we therefore often use `Constraint Programming' or `CP' synonymously to `finite domain Constraint Programming'.
In the past, CP has been successfully applied to such different problem types as production scheduling (with or without resource constraints), sequencing and assignment of tasks, workforce planning and timetabling, frequency assignment, loading and cutting, and also graph coloring.
The strength of CP lies in its use of a high-level semantics for stating the constraints that preserves the original meaning of the constraint relations (such high-level constraints are referred to as global constraints). It is not necessary to translate constraints into an arithmetic form—a process whereby sometimes much of the problem structure is lost. The knowledge about a problem inherent to the constraints is exploited by the solution algorithms, rendering them more efficient.
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