Logistic Demands and Forecasting of Agriculture Cold Chain Serving Beijing, Tianjin and Hebei Province

WANG Xiao-ping; YAN Fei

doi:10.19303/j.issn.1008-0384.2018.08.017

Volume 33 Issue 8

Mar. 2019

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WANG Xiao-ping, YAN Fei. Logistic Demands and Forecasting of Agriculture Cold Chain Serving Beijing, Tianjin and Hebei Province[J]. Fujian Journal of Agricultural Sciences, 2018, 33(8): 870-878. doi: 10.19303/j.issn.1008-0384.2018.08.017

Citation:

WANG Xiao-ping, YAN Fei. Logistic Demands and Forecasting of Agriculture Cold Chain Serving Beijing, Tianjin and Hebei Province[J]. Fujian Journal of Agricultural Sciences, 2018, 33(8): 870-878. doi: 10.19303/j.issn.1008-0384.2018.08.017

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Logistic Demands and Forecasting of Agriculture Cold Chain Serving Beijing, Tianjin and Hebei Province

doi: 10.19303/j.issn.1008-0384.2018.08.017

School of Logistics, Beijing Wuzi University, Beijing 101149, China

Received Date: 2018-03-27
Rev Recd Date: 2018-05-25
Publish Date: 2018-08-01

Abstract

Abstract

Understanding the demands on the logistics of an agricultural product cold chain is essential for appropriate planning, investment, and development of the system, which is unique and complex in operation. A specifically designed program is needed to accurately forecast the demands for an adequate and effective system management. This study applied a qualitative analysis and evaluated with statistical data on factors that might affect the logistics. Subsequently, forecasting models based on the grey model, support vector machine, BP neural network, RBF neural network, and genetic neural network were constructed. By challenging the models on the ability to characterize and correlate the variables as well as predict the outcomes, the following ranking was obtained:genetic neural network > RBF neural network > BP neural network > support vector machine > gray model.
- agricultural products,
- cold chain,
- logistic demand,
- genetic neural network,
- forecast,
- Beijing-Tianjin-Hebei

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References(18)

References

[1]	QI FANG, DAZHOTIG. The model of highway logistic demand forecasting based on gray neural network[J].Soft Science, 2010, 24(11):132-135. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=rkx200911028
[2]	JOSEPH BERECHUNAN.Cold Chain Development and Challenges in the Developing World[J]. ActaHorticulturae, 2003, 18(8):127-134. http://www.cabdirect.org/abstracts/20103372258.html
[3]	TERRY MOORE.An Introduction to Supply Chain Management[M].Palgrave Macmillan Ltd., 2003, 6(17): 153-165.
[4]	王之泰.冷链——从思考评述到定义[J].中国流通经济, 2010(9):15-17. doi: 10.3969/j.issn.1007-8266.2010.09.004
[5]	兰洪杰, 汝宜红.2008北京奥运食品冷链物流需求预测分析[J].中国流通经济, 2008(2):19-22. doi: 10.3969/j.issn.1007-8266.2008.02.005
[6]	朱坤萍, 江琳琳, 王赫男.河北省农产品冷链物流市场分析及对策[J].价格月刊, 2016(475):64-68. http://d.old.wanfangdata.com.cn/Periodical/jgyk201612014
[7]	陆芳, 由建勋.基于组合模型的陕西特色农产品物流需求预测[J].农业经济, 2017(12):140-141. doi: 10.3969/j.issn.1001-6139.2017.12.052
[8]	韩立民, 周海霞.我国水产品冷链物流需求分析及政策建议[J].中国渔业经济, 2012, 30(4):19-23. doi: 10.3969/j.issn.1009-590X.2012.04.003
[9]	李隽波.基于多元线性回归分析的冷链物流需求预测[J].安徽农业科学, 2011, 39(11):6519-6523. doi: 10.3969/j.issn.0517-6611.2011.11.087
[10]	海峰, 水璐, 矿玉玲, 等.湖北省农副产品发展现状及冷链物流需求趋势研究[J].物流工程与管理, 2012, 34(1):32-34. doi: 10.3969/j.issn.1674-4993.2012.01.012
[11]	原静.正向权重组合预测机制下的农产品冷链物流量需求预测[J].江苏农业科学, 2017, 45(19):341-346. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsnykx201719080
[12]	蒋智凯, 陈晓华.基于灰色模型的连云港市水产品冷链物流需求预测分析[J].淮海工学院学报, 2017, 15(8):101-103. doi: 10.3969/j.issn.2095-333X.2017.08.027
[13]	周宾.山西果品现代冷链物流需求的SD分析与对策[J].安徽农业科学, 2012, 40(21):11056-11058. doi: 10.3969/j.issn.0517-6611.2012.21.109
[14]	蔡自兴, 徐光祐.人工智能及其应用[M].北京:清华大学出版社, 2004.
[15]	AMJADY N.Day-aheaad Price Forecasting of Electricity Markets by a New Fuzzy Neural Network[J].IEEE Transaction on Power Systems, 2006, 21(2):887-896. doi: 10.1109/TPWRS.2006.873409
[16]	QWG, CHEN G, ZHU L L Short-tem Marginal Price Forcasting Based on Genetic Algorithm and Radial Basis Function Neural Network[J].Power System Technology, 2006, 30(7):18-21.
[17]	刘浩, 韩晶.MATLAB R2016a完全自学一本通[M].电子工业出版社, 2016.
[18]	周伟祝, 宦婧, 孙媛.遗传神经网络在保障资源需求预测中的应用[J].火力与指挥控制, 2013, 38(8):72-75. doi: 10.3969/j.issn.1002-0640.2013.08.020