Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications

Evolutionary Algorithms (EAs) has attracted significantly attention with respect to complexity scheduling problems, which is referred to evolutionary scheduling. However, EAs differ in the implementation details and the nature of the particular scheduling problem applied. In order to have an effective implementation of EAs for production scheduling, this paper focuses on making a survey of researches based on using hybrid EAs. Starting from scheduling description, we identify the classification and graph representation of scheduling problems. Then, we present the various representations, hybridisation techniques and machine-learning techniques to enhancing EAs. Finally, we also present successful applications in manufacturing.