Dual-Fairness Nurse Scheduling via the Double Direct Progressive Filling Algorithm under Qualification and Contract Constraints

Tita Putri Redytadevi (School of Data Science, Mathematics, and Informatics, IPB University, Bogor, 16680, Indonesia)
Toni Bakhtiar (School of Data Science, Mathematics, and Informatics, IPB University, Bogor, 16680, Indonesia)
Jaharuddin Jaharuddin (School of Data Science, Mathematics, and Informatics, IPB University, Bogor, 16680, Indonesia)

Zero : Jurnal Sains, Matematika, dan Terapan
Vol 10, No 2 (2026): Zero: Jurnal Sains Matematika dan Terapan

Nurse scheduling requires balancing workload distribution while satisfying qualification and employment contract constraints. This study implements a hybrid scheduling framework integrating Goal Programming (GP), the Double Direct Progressive Filling Algorithm (DDPFA), and the CP-SAT solver to generate feasible nurse schedules under actual and workforce-reduction scenarios in inpatient and emergency departments. Performance is evaluated using four indicators: inter-shift fairness, inter-nurse fairness, soft-constraint compliance, and computation time. The results show that the proposed approach achieves lower standard deviation values (0.15–0.42), satisfies all soft constraints, and generates feasible schedules in under 3 seconds. Compared with the evaluated manual scheduling and goal programming approaches, the framework produced more balanced workload allocation across shifts and nurses under the evaluated scenarios. These findings suggest that the proposed framework may provide a practical approach for fairness-oriented and cost aware workforce planning under the evaluated hospital conditions.