Performance Comparison of Metaheuristic Optimization Algorithms in Solving Production Scheduling Problems
DOI:
https://doi.org/10.35335/jict.v15i2.197Kata Kunci:
Ant Colony Optimization, Genetic Algorithm, Metaheuristic Optimization, Neural Network Optimization, Production Scheduling.Abstrak
In the context of production scheduling, the selection of an appropriate optimization algorithm is crucial to improve time efficiency and machine capacity utilization. This study aims to compare the performance of three metaheuristic optimization algorithms, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), in solving production scheduling problems by considering machine capacity, processing time, and task order. The method used is a comparative experiment with production scheduling data that includes several tasks with different processing times, and three machines with varying capacities. Analysis of variance (ANOVA) was used to test for significant differences in the performance of the three algorithms. The results showed that ACO resulted in lower makespan and more efficient machine utilization compared to GA and PSO, with significant differences at the 0.05 significance level. The implication of these findings is that ACO can be a more effective algorithm in production scheduling applications, especially for more complex scenarios. This research also contributes to the selection of more appropriate algorithms in scheduling optimization, which can be applied on a larger industrial scale.
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