Prediction models of the number of end-of-life vehicles in China

Prediction along with future trend analysis of the volume of end-of-life vehicles (ELVs) has a great impact on the execution of regulations and formulation of policies in China. To deal with such issues, this work investigates the historical data of their major influence factors including production volume, sale volume, vehicle count, turnover of highway freight, passenger turnover, GDP and income of per urban resident. Moreover, based on obtained main factors and historical data of ELV volume in China, its prediction models are established by multiple linear regressions (MLR), neural networks (NN) and optimized NN based on genetic algorithm (GA-NN) methods. In addition, a numerical example is given to illustrate the proposed models and the effectiveness of the proposed methods.

[1]  Helge V. Larsen,et al.  A European model for the number of End-of-Life Vehicles , 2007 .

[2]  MengChu Zhou,et al.  Disassembly modeling, planning, and application , 2002 .

[3]  Xin Zhao,et al.  Expected energy analysis for industrial process planning problem with fuzzy time parameters , 2011, Comput. Chem. Eng..

[4]  Guangdong Tian,et al.  Influence factor analysis and prediction models for component removal time in manufacturing , 2013 .

[5]  Li-yun Su,et al.  Prediction of multivariate chaotic time series with local polynomial fitting , 2010, Comput. Math. Appl..

[6]  Tien Chi Chen,et al.  Model reference robust speed control for induction-motor drive with time delay based on neural network , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[7]  MengChu Zhou,et al.  A Chance Constrained Programming Approach to Determine the Optimal Disassembly Sequence , 2013, IEEE Transactions on Automation Science and Engineering.

[8]  R. S. Bhoopal,et al.  Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach , 2011 .

[9]  Tatjana V. Sibalija,et al.  An integrated approach to optimise parameter design of multi-response processes based on Taguchi method and artificial intelligence , 2012, J. Intell. Manuf..

[10]  Fan-Tien Cheng,et al.  Selection Schemes of Dual Virtual-Metrology Outputs for Enhancing Prediction Accuracy , 2011, IEEE Transactions on Automation Science and Engineering.

[11]  Markus A. Reuter,et al.  Dynamic modelling and optimisation of the resource cycle of passenger vehicles , 2002 .

[12]  Guangdong Tian,et al.  Energy evaluation method and its optimization models for process planning with stochastic characteristics: A case study in disassembly decision-making , 2012, Comput. Ind. Eng..

[13]  Gareth Coates,et al.  Assessing the economics of pre-fragmentation material recovery within the UK , 2007 .

[14]  Davood Afshari,et al.  Creep feed grinding optimization by an integrated GA-NN system , 2010, J. Intell. Manuf..

[15]  Mehrdad Askarian,et al.  The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation. , 2009, Waste management.

[16]  Jiangwei Chu,et al.  Development pattern and enhancing system of automotive components remanufacturing industry in China , 2011 .

[17]  Mahmoud Salimi,et al.  Prediction of antileukemia activity of berbamine derivatives by genetic algorithm–multiple linear regression , 2011 .

[18]  Wang Hu.  Analysis on Modeling of Utilization System of Automobile Renewable Resources , 2008 .

[19]  MengChu Zhou,et al.  Probability Evaluation Models of Product Disassembly Cost Subject to Random Removal Time and Different Removal Labor Cost , 2012, IEEE Transactions on Automation Science and Engineering.

[20]  Bryan E Shepherd,et al.  Accounting for Data Errors Discovered from an Audit in Multiple Linear Regression , 2011, Biometrics.