**Constraint optimization**, or
**constraint programming**
(CP), is the name given to identifying feasible solutions out of a very large
set of candidates, where the problem can be modeled in terms of arbitrary
constraints.
CP problems arise in many scientific and engineering disciplines. (The word
"programming" is a bit of a misnomer, similar to how "computer" once meant
"a person who computes". Here, "programming" refers to the arrangement of a plan
, rather than programming in a computer language.)

CP is based on *feasibility* (finding a feasible solution) rather than
optimization (finding an optimal solution) and focuses on the constraints and
variables rather than the objective function. In fact, a CP problem may not even
have an objective function — the goal may be to narrow down a very
large set of possible solutions to a more manageable subset by adding
constraints to the problem.

An example of a problem that is well-suited for CP is **employee scheduling**.
The problem arises when companies that operate continuously — such as
factories — need to create weekly schedules for their employees. Here's a
very simplified example: a company runs three 8-hour shifts per day and assigns
three of its four employees to different shifts each day, while giving the
fourth the day off. Even in such a small case, the number of possible schedules
is huge: each day, there are `4! = 4 * 3 * 2 * 1 = 24`

possible employee
assignments, so the number of possible weekly schedules is 24^{7}, which
is over 4.5 billion.
Usually there will be other constraints that reduce the number of feasible
solutions — for example, that each employee works at least a minimum
number of days per week. The CP method keeps track of which solutions remain
feasible when you add new constraints, which makes it a powerful tool for
solving large, real-world scheduling problems.

CP has a widespread and very active community around the world with dedicated scientific journals, conferences, and an arsenal of different solving techniques . CP has been successfully applied in planning, scheduling, and numerous other domains with heterogeneous constraints.

## Tools

Google provides few ways to solve CP problems:

- CP-SAT solver: A
**constraint programming**solver that uses SAT (satisfiability) methods. - Original CP solver: A
**constraint programming**solver used in the routing library.

If your problem can be modeled with a linear objective and linear constraints, then you have a linear programming problem and should consider MPSolver. (However, routing problems are typically best solved with our vehicle routing library even if they can be represented with a linear model.)

## Examples

The next section describes the CP-SAT solver, the
primary OR-Tools solver for constraint programming. (SAT stands for
**satisfiability**: the solver uses techniques for solving SAT problems along
with CP methods.)

Here are some examples of scheduling problems that are well-suited for the CP-SAT solver:

Two classic CP problems are the N-queens problem and cryptarithmetic puzzles.