DETERMINISTIC JOB-SHOP OPTIMIZATION WITH CP-SAT AND MONTE CARLO ROBUSTNESS ASSESSMENT

This paper compares two scheduling approaches for a 3×3 job-shop case study: the Shortest Processing Time (SPT) rule and the CP-SAT method implemented in Google OR-Tools. For the nominal processing times, CP-SAT reduces the makespan from 39 to 36 minutes, while the equality between the obtained makespan and the lower bound confirms optimality. The robustness of the two schedules is evaluated through 30,000 Monte Carlo simulations considering processing-time variations of ±5%, ±10%, and ±15%. The results show that CP-SAT yields a lower makespan in 99.22% of the simulated scenarios, maintaining its advantage as uncertainty increases. The study highlights the usefulness of combining CP-SAT optimization with Monte Carlo simulation for assessing the robustness of production schedules.