Perbandingan Genetic Algorithm dan Ant Colony Optimization dalam Optimasi Penjadwalan Perawat di Rumah Sakit - Sri Dewi; Afrizal Hasan (Jurnal Nasional Teknologi Komputer, 2026)
Nurse scheduling is a complex problem that must satisfy various constraints, such as shift requirements, work hour constraints, and nurse preferences. This study compares the performance of two metaheuristic algorithms, Genetic Algorithm (GA) and Ant Colony Optimization (ACO), with each algorithm producing the best schedule. The evaluation is based on solution quality, convergence, multi-run consistency, and computation time. The results show that ACO produces higher solution quality and consistency, with an average fitness of 8268.06 and a desired shift fulfillment rate of 86%. Conversely, GA excels in time efficiency, with an average execution time of 15.07 seconds, significantly faster than ACO's 72.05 seconds. This difference creates a trade-off between optimal quality and execution speed. These findings suggest that algorithm selection is highly dependent on the hospital's operational needs. ACO, for example, is better suited for nurse satisfaction, while GA is better suited for rapid response.