Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

Abstract

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually.