Selection of an optimal neural network architecture for maintenance workforce size prediction using grey relational analysis
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Abstract
Prediction is necessary in industrial practices according to the norms of modern scientific development. Recognising maintenance problems before they occur potentially helps to improve workforce performance as effective preventive maintenance actions are developed. A large number of industrial systems worldwide owe their gains to predictive analysis. Furthermore, precise predictions can aid maintenance policy makers in making decisions as well as promoting the quality of decision making in maintenance, particularly on maintenance workforce issues. This study reports the use of grey relational analysis cum artificial neural networks (ANN) for maintenance workforce size prediction through the selection of an optimal neural network architecture. Workforce cost, workload, productivity and effectiveness represent the input parameters utilised in the ANN framework. The method competitively determines the most suitable ANN architecture for maintenance workforce size. Comparison of the ANN model results reveal that its performance is better than a fuzzy inference system. Conclusively, the application of the framework advanced in this work was found useful with practical data obtained from a process industry that operates as a brewery. The efficiency of the proposed approach was documented.
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