Simulation of Scheduling Production System by Using Integrating Simulation Models with Artificial Neural Network Model

Abstract

Traditional methods of dealing with finding the relationship between the inputs data of simulation models and the outputs data fail or takes a long time to find this relationship. Artificial neural networks (ANNs) have the ability to learn complex relationships between inputs and outputs. Their use can greatly enhance simulation models and allow for more accurate representations of real life scenarios. This paper is concerned with the application of the mechanism of integrating simulation models with artificial neural network (ANN) model. This mechanism was tested by integrating simulation models of re-tubing heat exchangers line (RTHEL) with ANN model to schedule entering exchangers to inside re-tubing workshop. The result of applying this mechanism of integration in system (RTHEL) was in reducing completion time of re-tubing batches of heat exchangers by about (12.5%).