The criticality of spare parts evaluating model using artificial neural network approach

Authors:
Lin Wang;Yurong Zeng;Jinlong Zhang;Wei Huang;Yukun Bao
Affiliations:
School of Management, Huazhong University of Science and Technology, Wuhan, China;School of Computer Science and Technology, Hubei University of Economics, Wuhan, China;School of Management, Huazhong University of Science and Technology, Wuhan, China;School of Management, Huazhong University of Science and Technology, Wuhan, China;School of Management, Huazhong University of Science and Technology, Wuhan, China
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
Year:
2006

Citing 6
Cited 2

Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation

Decision Support Systems
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Intelligent Systems for Business: Expert Systems with Neural Networks

Intelligent Systems for Business: Expert Systems with Neural Networks
Brief Application Description. Neural Networks Based Forecasting Techniques for Inventory Control Applications

Data Mining and Knowledge Discovery
Classifying inventory using an artificial neural network approach

Computers and Industrial Engineering
Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms

Neural Networks

A hybrid decision support system for slow moving spare parts joint replenishment: a case study in a nuclear power plant

International Journal of Computer Applications in Technology
A differential evolution algorithm for joint replenishment problem using direct grouping and its application

Expert Systems: The Journal of Knowledge Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents artificial neural networks (ANNs) for the criticality evaluating of spare parts in a power plant. Two learning methods were utilized in the ANNs, namely back propagation and genetic algorithms. The reliability of the models was tested by comparing their classification ability with a hold-out sample and an external data set. The results showed that both ANN models had high predictive accuracy. The results also indicate that there was no significant difference between the two learning methods. The proposed ANNs was successful in decreasing inventories holding costs significantly by modifying the unreasonable target service level setting which is confirmed by the corresponding criticality class in the organization.