*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 simply be to narrow down a vary 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 simple 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.

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:

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.

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

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