Combined forecasting of regional logistics demand optimized by genetic algorithm

Grey Systems

Volumn 4, Issue 2, 2014, Pages 221-231

  He, Feng biao ^a   Chang, Jun ^a  

^a HUAIYIN NORMAL UNIVERSITY   (China)

Author keywords

Combined forecasting; Genetic algorithm; Grey system method; Regional logistics; Regression model

Indexed keywords

EID: 84991911345     PISSN: 20439377     EISSN: 20439385     Source Type: Journal    
DOI: 10.1108/GS-04-2014-0011     Document Type: Article

References (28)

1. Cai, X. and Chen, C. (2004), “The prediction and analyse of regional logistics demand”, Logistics Technology, Vol. 27 No. 12, pp. 15-18.
2. Chen, S., Wang, W., Qu, G. and Zhang, S. (2004), “Neural network integrated approach for traffic forecast”, Highway and Transportation Technology, Vol. 21 No. 12, pp. 80-83.
3. Gu, X. (2013), “A study of the correlation between the logistics system and the industry in the Yangtze River Delta”, Academic Exploration, Vol. 21 No. 12, pp. 95-98.
4. Hou, R. and Zhang, B. (2005), “A method for forecasting regional logistics demand based on MLP neural network and its application”, Systems and Engineering – Theory & Practice, Vol. 25 No. 12, pp. 43-47.
5. Kwasnicki, W. (2013), “Logistic growth of the global economy and competitiveness of nations”, Technological Forecasting and Social Change, Vol. 80 No. 1, pp. 50-76.
6. Lei, Y., Zhang, S., Li, X. and Zhou, C. (2005), MATLAB Genetic Algorithm Toolbox and Application, Xidian University Press, Xi’an.
7. Li, H. and Hu, Q. (2005), Predict and Decision-Making, Xidian University Press, Xi’an, pp. 43-52.
8. Li, L. and Shan, D. (2013), “Analysis and prediction of influencing factors of logistics demand in Jiangsu province”, Technology and Economy, Vol. 26 No. 6, pp. 96-100.
9. Lin, X. and Yuan, J. (2007), “Forecast of logistics of Chengdu Shuangliu airport based on gray model”, Journal of Wuhan University of Technology (Transportation Science & Engineering), Vol. 31 No. 3, pp. 457-459.
10. Liu, S., Dang, Y. and Fang, Z. (2004), Grey System Theory and Application, 3rd ed., Science Press, Beijing.
11. Ma, Y., Tang, X. and Yang, G. (1997), “A study on basic theory of the optimal combinated prediction method of non-negative weights”, Operations Research and Management Science, Vol. 6 No. 2, pp. 1-8.
12. Mahpula, A., Yang, D., Kurban, A. and Witlox, F. (2013), “An overview of 20 years of Chinese logistics research using a content-based analysis”, Journal of Transport Geography, Vol. 31 No. 7, pp. 30-34.
13. Qiu, Y., Lu, H. and Wang, H. (2008), “Prediction method for regional logistics”, Tsinghua Science & Technology, Vol. 13 No. 5, pp. 660-668.
14. Tan, G., Shi, F. and L. (2009), “Weight changeable combination forecast method of logistics quantity and its application”, Journal of Transportation Systems Engineering and Information Technology, Vol. 9 No. 2, pp. 105-109.
15. Wang, Y. (2009), “Study on the forecasting methods of regional logistics demand”, Logistics Technology, Vol. 28 No. 12, pp. 169-171.
16. Wei, L. and Pang, M. (2004), “Prediction of logistics amount based on artificial neural network”, Journal of Chang’an University (Natural Science Edition), Vol. 24 No. 6, pp. 55-58.
17. Xu, X. (2009), “Study on the relation between logistics industry and regional economy of Jiangsu”, Logistics Technology, Vol. 28 No. 2, pp. 41-43.
18. Yang, R., Zhang, H. and Miao, Z. (2001), “The method of moving average line in logistics predicting technique”, Journal of Wuhan University of Technology (Transportation Science & Engineering), Vol. 25 No. 3, pp. 353-355.
19. Yang, Y., Hu, C., Wu, Z., Zhang, B. and Huang, Z. (2013), “Research on the interactive relationship between regional logistics and regional economic based on logistics standards”, Northern Economy and Trade, Vol. 33 No. 10, pp. 34-36.
20. Yu, J. (2007), Econometrics, University of International Business and Economics Press, Beijing.
21. Zeng, Y. (2012), “A combined prediction method for regional logistics demand using variable coefficient weights”, Statistics and Decision Making, Vol. 28 No. 21, pp. 93-95.
22. Zhang, H. (2013), “Research on the cooperative development of modern logistics and regional economy – a case study on the Chang-Zhu-Tan economic circle”, Contemporary Economy and Management, Vol. 35 No. 12, pp. 61-67.
23. Zhu, B. (2008), “A novel combined forecasting model for the prediction of regional logistics demand”, Economic Geography, Vol. 28 No. 6, pp. 952-954.
24. Chen, X. and Zhang, Q. (2008), “Research on the demand forecasting method for regional logistics”, Industrial Engineering and Management, Vol. 12 No. 1, pp. 94-98.
25. Li, Z. and Huang, F. (2009), “Research on the forecasting indices of regional logistics demand based on PCA”, Logistics Technology, Vol. 28 No. 12, pp. 128-130.
26. Tian, H. (2013), “Study on the regional economic and regional logistics quantitative method”, Logistics Sci-Tech, No. 11, pp. 65-67.
27. Wen, P., Zhang, Z. and Luo, B. (2009), “Prediction and analysis on logistics demand in Beijing based on BP neural network”, Logistics Technology, Vol. 28 No. 6, pp. 91-93.
28. Yin, Y. (2013), “Study on relationship between logistics integration and economic growth based on grey correlation theory”, Logistics Technology, Vol. 32 No. 11, pp. 133-135.