Approximation of a discrete event stochastic simulation using an evolutionary artificial neural network

Abstract EN

A computer simulation model may be regarded as a mapping function bietween a set of input and output variables.

Although simulation models are very popular experimentation tools, in many cases they are computationally expensive.

Hence, it would be essential to have fast, accurate approximation of computer simulation.

This paper examines the use of an evolutionary artificial neural network for approximating a lot size–reorder point inventory system simulation.

The proposed approach was compared with a Backpropagation trained neural network and multiple linear regression models.