An ANN Model to Predict Select Performance Measures for Job Scheduling System

Abstract

The purpose of this work is to design and develop an artificial neural network (ANN) model for prediction of the select performance measures (PMs) of a single machine job scheduling (SMJS) system, under the influence of varying levels of input variables. Ten thousand scheduling problems were randomly generated and for each problem values of five PMs were computed which were used to train, test, and validate different ANN models in order to develop the optimal ANN model for accurate prediction of the PMs for a given set of input variables. An ANN model with two hidden layers having 16 neurons in each hidden layer was found to be the optimum model for prediction of the PMs as it resulted in the minimum mean squared error. The actual and predicated values of the PMs obtained from the optimum ANN model were compared and it was found that they were in close agreement. The ANN model developed in this study may be used by the managers, practitioners and other decision makers to define appropriate values of input variables that will yield better PMs. Further, the approach used in the study to develop ANN model may be used for development of similar ANN models for other manufacturing systems. This study provides an ANN-based prediction model to predict PMs of the single machine job scheduling system which may however, be modified as per the requirements of other manufacturing systems to predict their PMs.