Application of Constraint Programming with Satisfiability in Nurse Scheduling

We applied the Google OR-Tools Constraint Programming with Satisfiability (CP-SAT) solver to the nurse scheduling problem (NSP) to efficiently generate feasible and high-quality schedules under complex real-world constraints. The proposed model integrates hard and soft constraints, including workload balance, fairness, and staffing sufficiency, within a unified optimization framework. A genetic algorithm (GA) is implemented as a baseline for comparison. Experimental results show that GA does not consistently produce feasible solutions, whereas CP-SAT achieves feasible schedules satisfying all constraints and is approximately 224.6 times faster than GA on the tested instance. This demonstrates CP-SAT’s superior efficiency, robustness, and applicability for solving NSP.