Complexity Indicators applied to the Job Shop Scheduling Problem to discriminate the best Algorithm

In this paper, we propose the application of discriminant analysis to select appropriately the algorithm that better solves an instance of the Job Shop Scheduling Problem. The discriminant analysis was used as a method of machine learning to find the relation between the characteristics of the problem (complexity indicators) and the performance of algorithms. The prediction of the classification obtained of the discriminant analysis was 60%.