Title: Performance study of artificial neural network modelling to predict carried weight in the transportation system

Authors: Saeid Jafarzadeh-Ghoushchi; Mohd Nizam Ab. Rahman

Addresses: Department of Industrial Engineering, Faculty of Engineering, Urmia University of Technology (UUT), Urmia, West Azarbaijan, Iran ' Department of Mechanical and Materials Engineering, Faculty of Engineering and Built Environment, National University of Malaysia (UKM), Bangi, Selangor, Malaysia

Abstract: The major aim of this study is to model and predict the amount of carried weight based on the five direct impact factors in the transportation system. In this study, artificial neural network (ANN) has been incorporated for developing a predictive model. Three different training algorithms, namely Levenberg-Marquardt-LM, batch backpropagation-BBP and quick propagation-QP, were used to train. The input parameters are the aforementioned five transportation factors plus two timing factors namely number of weeks and seasons while the carried weights is the output. The next purpose of this study is comparing the mentioned learning algorithm's performance based on predicting ability. The results showed that the QP algorithm with 7-4-1 network topology exhibited the highest predictive power. The available data have been trained by ANN (QP-7-4-1) and the responses were predicted. Moreover, the truck factor plays a slightly more dominant role in the prediction of carried weighs.

Keywords: artificial neural networks; ANNs; transportation systems; batch backpropagation; quick propagation; Levenberg-Marquardt; modelling; weight prediction; supply chain management; SCM; vans; lorries; trucks; fuel consumption; labour.

DOI: 10.1504/IJLSM.2016.076473

International Journal of Logistics Systems and Management, 2016 Vol.24 No.2, pp.200 - 212

Received: 22 Jan 2015
Accepted: 01 Mar 2015

Published online: 10 May 2016 *

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