cp-sat-modeling

Model a CP-SAT scheduling problem by selecting native primitives like interval variables, no-overlap, and cumulative constraints, then define hard or soft rules using reification and objective tiers to structure the solver's search space.

Use no-overlap constraints for single-machine scheduling where tasks cannot run simultaneously, and apply cumulative constraints for multi-capacity resources to prevent overlapping jobs from exceeding shared capacity limits.

Handle soft constraints in Google OR-Tools by applying reification to conditionally trigger penalties, then stacking these penalties into objective tiers to prioritize rule violations correctly during optimization.

Yes, CP-SAT handles factory scheduling with sequence-dependent setup times by leveraging circuit or automaton primitives to dictate valid transitions and accurately capture routing penalties between consecutive jobs.

Solver performance drops when time granularity is too fine or horizon bounds are unbounded, so applying disciplined decomposition and realistic horizon sizing dramatically reduces the CP-SAT search space.

Decomposition is necessary for large OR-Tools scheduling models, splitting complex problems into manageable sub-problems to prevent solver timeouts and ensure the CP-SAT engine finds optimal solutions efficiently.