The application of genetic algorithms as an optimization step in the case of nurse scheduling at the bringkoning community health center

Abstract

Scheduling is an important activity that must be done in every job. In scheduling, there must be some rules that can minimize the occurrence of conflicts or gaps between schedules. In this study, applied a genetic algorithm to solve the problem of scheduling nurses in Bringkoning community health center. In the process of genetic algorithm, there are several processes that must exist until the creation of a result is a schedule. The number of nurses, population length, Cr and Mr values, and also number of iterations along with the number of days determined by input. The crossover method used is one cut-point crossover, Random mutation method and selected with elitism selection. Thereby, the analysis gained, is the value of the parameter genetic algorithm affects the optimization results. The small parameter size will cause the search area of the genetic algorithm to be narrower, whereas if the parameter size is too large it will require longer computing time and does not guarantee it will result in an optimal value for some of those variables. So, it depends on number of days in the input to get optimal result of the schedule for nursing care in the room of the emergency unit.