CP with Feasbl

Constraint programming is a way to solve problems by declaring the rules that a valid solution must satisfy. Instead of writing a step-by-step algorithm, you define variables, domains of possible values, and constraints that remove invalid combinations.

Use CP with Feasbl for discrete, combinatorial problems: schedules, assignments, matching, packing, configuration, and other cases where the challenge is finding any valid solution or the best valid solution among many possible combinations.

In Feasbl

• Express hard feasibility rules directly in the model.
• Use Feasbl solver support for discrete choices and combinatorial search.
• Combine CP with planning or linear programming when a model has sequencing, allocation, and feasibility concerns.

Continue with the constraint reference or the developer reference.