Ensembles of priority rules to solve one machine scheduling problem in real-time

Abstract

Priority rules are one of the most common and popular approaches to real-time scheduling. Over the last decades, several methods have been developed to generate rules automatically. In addition, it has been shown that combining rules into ensembles is better than using a single rule in many cases. In this paper, we analyze different ways to create and use ensembles previously developed through genetic programming. In our study, we classify ensembles as either collaborative or coordinated, depending on how the rules are used. In the first case, all the rules contribute to the creation of the same solution, while in the second case, each rule works independently on its own solution, and the best of them is selected as the solution of the ensemble. We found that each method has its own strengths and weaknesses, which leads us to use them in combination. Based on this hypothesis, we developed new methods to design and combine collaborative and coordinated ensembles and evaluated these methods for the One Machine Scheduling Problem with time-varying capacity and minimization of total tardiness. The results of the experimental study provided interesting insights into the use of ensembles and showed that our proposals outperform previous methods.