Outpatient Scheduling with Genetic Algorithm: The Power of Mutation Operators

Outpatient scheduling is a complex and time-consuming task. To address this challenge, numerous studies have developed various optimization methods, including genetic algorithms.

This study aims to develop a task-specific genetic algorithm and investigate the effect of different mutation operators on its performance, focusing on minimizing the earliest completion time of scheduled examinations.

Random and two heuristic mutation operators were designed and compared. The effect of these mutation operators and their parameters were evaluated across four fundamentally distinct scheduling scenarios.

The exponential mutation operator outperformed all others across all scheduling problems. It achieved an optimal schedule in 100% of runs for the simplest task and in 74.5% of runs for the most complex one. In comparison, the random mutation operator achieved 100% and 1%, while the polynomial operator reached 75.66% and only 0.22%, respectively.

The efficiency of the genetic algorithm developed for outpatient scheduling is strongly influenced by the choice of mutation operator. The performance of the algorithm can be greatly enhanced by employing a specialized mutation operator tailored to the objective function.