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鲜食农产品流通GM模型构建及验证
引用本文:丁丽芳.鲜食农产品流通GM模型构建及验证[J].中国农业资源与区划,2017,38(6):226-229.
作者姓名:丁丽芳
作者单位:山西农业大学经济管理学院,晋中,030801
基金项目:山西省哲学社会科学项目“山西省农产品流通效率的测度及其影响因素研究”(201512);山西农业大学经济管理学院基金 项目“山西省农产品物流产业一体化模式研究”(jgky2016005)
摘    要:目的]利用灰色系统理论模型GM模型预测鲜食农产品物流量,以期为提高鲜食农产品流通效率提出前瞻性思路。方法]基于鲜食农产品物流发展水平相对落后的问题,以2005~2013年间我国鲜食农产品的物流量为研究对象,通过灰色控制系统理论中的GM(1,1)模型原理的预测和检验方法,验证了该模型在鲜食农产品物流中的实用性。结果]2005~2013年,我国的鲜食农产品实际物流量在(10 000±2 000)万t波动,除2005年实际物流量与预测物流量等同外,2006~2013年的预测物流量均高出实际物流量,而2014年预测物流量(8 756万t)明显低于历年物流量的平均水平。结论]GM(1,1)模型预测的平均相对误差率为6.15%,勉强可适用于鲜食农产品物流量的预测。

关 键 词:鲜食农产品  物流  冷链  GM模型  灰色预测
收稿时间:2016/3/15 0:00:00

CONSTRUCTION AND VERIFICATION OF GM MODEL FOR FRESH FOOD CIRCULATION
Ding Lifang.CONSTRUCTION AND VERIFICATION OF GM MODEL FOR FRESH FOOD CIRCULATION[J].Journal of China Agricultural Resources and Regional Planning,2017,38(6):226-229.
Authors:Ding Lifang
Institution:College of Economy & Management, Shanxi Agriculture University, Jinzhong 030801, China
Abstract:The logistics of fresh agricultural products is a very important part of the agricultural economy. This paper analyzed the present situation of the development of the logistics of fresh agricultural products in China in recent years based on GM model, and pointed out many problems in the process of production and logistics development of fresh agricultural products, for example, logistics infrastructure was relatively backward, the logistics information chain was not perfect, and the cost of logistics was high. And then, it verified the practicability of GM (1, 1) model based on the highway transport of fresh agricultural products through the theory of grey control system. The results showed that the GM (1, 1) model prediction results can reflect the amount of fresh agricultural products logistics, but the modeling accuracy still needed further improvement and adjustment. The results can provide theoretical basis for the effective development of fresh agricultural products logistics system.
Keywords:fresh agricultural product  logistics  cold chain  GM model  grey predict
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