Article ID: | iaor20116263 |
Volume: | 39 |
Issue: | 2 |
Start Page Number: | 424 |
End Page Number: | 436 |
Publication Date: | Feb 2012 |
Journal: | Computers and Operations Research |
Authors: | Heavey Cathal, Can Birkan |
Keywords: | heuristics: genetic algorithms, neural networks, manufacturing industries, inventory, stochastic processes |
Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodeling of discrete‐event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (