How deals with discrete data for the reduction of simulation models using neural network
Neural and Evolutionary Computing
2009-06-11 v1
Abstract
Simulation is useful for the evaluation of a Master Production/distribution Schedule (MPS). Also, the goal of this paper is the study of the design of a simulation model by reducing its complexity. According to theory of constraints, we want to build reduced models composed exclusively by bottlenecks and a neural network. Particularly a multilayer perceptron, is used. The structure of the network is determined by using a pruning procedure. This work focuses on the impact of discrete data on the results and compares different approaches to deal with these data. This approach is applied to sawmill internal supply chain
Keywords
Cite
@article{arxiv.0906.1900,
title = {How deals with discrete data for the reduction of simulation models using neural network},
author = {Philippe Thomas and André Thomas},
journal= {arXiv preprint arXiv:0906.1900},
year = {2009}
}