An Artificial Neural Network-Based Method for Railway Logistics Network Design

Dan Zhu Wang,Mao Xiang Lang

doi:10.4028/www.scientific.net/aef.6-7.924

Abstract

Logistics network design problem has an important position in the railway logistics development, it has aroused great concern both in railway transportation and logistics research fields. This paper proposes a method for railway logistics network design problem based on artificial neural network model. In the logistics network design method, various influencing factors of railway logistics network have been considered. An evaluation index system of a railway transportation enterprise is set up. Self-Organizing Map neural network algorithm has been used for the creation of logistics network nodes initial set. And the layers division of the logistics network has been determined with the help of BP neural network model. Subsequently an empirical study of a railway logistics enterprise is given to certificate the feasibility and accuracy of this railway logistics network design method.

Highlights

Logistics network design problem covers a wide range of subjects, such as economics, politics, mathematics, ecology and so on
A three-layer railway logistics network will be established by this BP neural network model, if necessary, the optimization and adjustment of the logistics network can be given by the experts and managers, according to their research experience and management experience
A new methodology of railway logistics network design is developed based on Artificial Neural Network

Summary

Introduction

Logistics network design problem covers a wide range of subjects, such as economics, politics, mathematics, ecology and so on. Traditional logistics network design methods based on logistics nodes location models are not applicable. In the railway logistics network design model, a three-layer network will be established by the following steps: Step 1. Establishing an evaluation index system of railway logistics nodes characteristics, which is a basis of the Self-Organizing Map neural network model and BP neural network model.

Results

Conclusion