Optimization of Nursing Scheduling in Emergency by Using Genetic Algorithm - Hasibuan, Mhd Panerangan; Cipta, Hendra (Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat, 2024)
Scheduling nurse duty is one of the problems in health organizations that is quite complicated to solve. Starting from the uncertain number of patients, serious patient illnesses, characteristics of organizational groups, requests for nurses to take time off, and the qualifications and specialization of the nurses themselves are why scheduling in the ER is difficult to optimize. The same thing is being experienced by one of the health institutions, RSUD Dr. Pirngadi. Preparing schedules or determining the number of nurses on duty is still done manually, resulting in a lack of optimization in scheduling and the number of nurses who must be on duty, especially in the emergency department. In solving this problem, an appropriate method is needed so that the process of scheduling and optimizing the number of nurses can be formed properly. This research applies the Genetic Algorithm in optimal emergency department (IGD) nurse duty scheduling. Genetic algorithms, also called search algorithms, are based on the mechanisms of natural selection and genetics. Genetic algorithms are one of the appropriate methods for solving complex optimization problems. This method is good enough to optimize shift scheduling for the Emergency Room Nursing Service in a Hospital. This Genetic Algorithm can be a solution to multi-criteria and multi-objective problems modeled using biological and evolutionary processes. So, the concept of this method can be applied in optimizing the Nursing Service schedule. The results of calculations using the Genetic Algorithm show quite significant comparisons, including several nurses losing their positions and being eliminated by mutation because they could not compete with several other strong individuals.