Real-time discrete event simulation: a framework for an intelligent expert system approach utilising decision trees

This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the applicatio...

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Bibliographic Details
Published in:	International journal of advanced manufacturing technology Vol. 110; no. 11-12; pp. 2893 - 2911
Main Authors:	Prajapat, N., Turner, C., Tiwari, A., Tiwari, D., Hutabarat, W.
Format:	Journal Article
Language:	English
Published:	London Springer London 01-10-2020 Springer Nature B.V
Subjects:	Accuracy CAE) and Design Computer-Aided Engineering (CAD Data collection Decision making Decision trees Design of experiments Design optimization Discrete event systems Engineering Expert systems Global marketing Industrial and Production Engineering Lead time Literature reviews Mechanical Engineering Media Management Model accuracy Multiple objective analysis Original Article Pareto optimization Real time Regression analysis Resource allocation Simulation Turbines Random forest Simulation Flexible manufacturing system Decision tree Expert system Discrete event simulation (DES)
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Summary:	This paper explores the use of discrete event simulation (DES) for decision making in real time based on the potential for data streamed from production line sensors. Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of discrete event simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. The literature review has identified key research gaps as the lack of example cases where multi-objective optimisation methods have been applied to simulation models and the need for a framework to visualise the relationship between inputs and outputs of simulation models. A framework is presented to enable the optimisation DES simulation models and optimise multiple objectives simultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show that the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. A real-life test case involving a turbine assembly process is presented to illustrate the use and validity of the framework. The generated regression tree expressed a general trend by demonstrating relationships between input variables and two conflicting objectives. Random forests were implemented for creating higher accuracy predictions and they produced a mean square error of ~ 0.066 on the training data and ~ 0.081 on test data.
ISSN:	0268-3768 1433-3015
DOI:	10.1007/s00170-020-06048-5