Development of machine learning‐based real time scheduling systems: using ensemble based on wrapper feature selection approach

Abstract

There are two items that significantly enhance the generalisation ability (i.e. classification accuracy) of machine learning‐based classifiers: feature selection (including parameter optimisation) and an ensemble of the classifiers. Accordingly, the objective in this study is to develop an ensemble of classifiers based on a genetic algorithm (GA) wrapper feature selection approach for real time scheduling (RTS). The proposed approach can better enhance the generalisation ability of the RTS knowledge base (i.e. classifier) in comparison with three classical machine learning‐based classifier RTS systems, including the GA‐based wrapper feature selection mechanism, in terms of the prediction accuracy of 10‐fold cross validation as measured according to all the performance criteria. The proposed ensemble classifier RTS also provides better system performance than the three machine learning‐based RTS systems, including the GA‐based wrapper feature selection mechanism and heuristic dispatching rules, under all the performance criteria, over a long period in a flexible manufacturing system (FMS) case study.

Keywords:

• real time scheduling
• shop floor control
• FMS control
• machine learning
• ensemble
• feature selection

Acknowledgements

This work was supported in part by the National Science Council, Repuublic of China, under Contract No. NSC‐98‐2221‐E‐211‐007‐MY2 and NSC-99-2221-E-15–062.