Description
Solves constraint integer programming and mixed-integer optimization problems. It is useful for operations research, scheduling, logistics, planning, academic work, and optimization-heavy applications.
Optimization results depend on models, constraints, and solver settings. Validate formulations and do not treat a feasible solution as automatically correct for the real-world problem.