An improved grey neural network model for predicting transportation disruptions

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

27 Scopus Citations
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Author(s)

  • Chunxia Liu
  • Tong Shu
  • Shou Chen
  • Shouyang Wang
  • Kin Keung Lai
  • And 1 others
  • Lu Gan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)331-340
Journal / PublicationExpert Systems with Applications
Volume45
Online published17 Oct 2015
Publication statusPublished - 1 Mar 2016

Abstract

Transportation disruption is the direct result of various accidents in supply chains, which have multiple negative impacts on supply chains and member enterprises. After transportation disruption, market demand becomes highly unpredictable and thus it is necessary for enterprises to better predict market demand and optimize purchase, inventory and production. As such, this article endeavors to design an improved model of grey neural networks to help enterprises better predict market demand after transportation disruption and then the empirical study tests its feasibility. This improved model of grey neural networks exceeds the conventional grey model GM(1,1) with respect to the fact that the raw data tend to show exponential growth and data variation is required to be moderate, demonstrating the good attribute of nonlinear approximation in terms of neural networks, setting up standards for selecting the number of neurons in the input layer of BP neural networks, increasing the fitting degree and prediction accuracy and enhancing the stability and reliability of prediction. It can be applied to sequential data prediction in transportation disruption or mutation, contributing to the prediction of transportation disruption. The forecasting results can provide scientific evidence for demand prediction, inventory management and production of supply chain enterprises.

Research Area(s)

  • GM(1,1) model, Neural network, Prediction, Transportation disruptions

Citation Format(s)

An improved grey neural network model for predicting transportation disruptions. / Liu, Chunxia; Shu, Tong; Chen, Shou; Wang, Shouyang; Lai, Kin Keung; Gan, Lu.

In: Expert Systems with Applications, Vol. 45, 01.03.2016, p. 331-340.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review