山东省区域物流发展分析及需求组合预测研究

发布时间：2018-07-07 14:20

本文选题：区域物流 + 山东省　；参考：《安徽财经大学》2015年硕士论文

【摘要】：顾名思义,物流就是指货物的仓储、包装加工、搬运、装载、运输等过程。可以借助的工具和实现方式多种多样,运输效果的达成不尽相同,自然地理差异是造成输送方式存在差异的先天因素,也是一个很重要的因素。这种差异加剧了经济发展的不平衡,握有优势的地区凭借自身条件,完善基础设施,吸引优秀人才,改进技术装备,提高了物流效率,降低了企业运输环节成本,在竞争中占得先机。对一个地区物流进行研究,就要囊括这个地区的整体和部分,分析地区物流发展的有利条件和不利因素,找出这个地区的领先者和跟随者,从领先者身上学习先进的理念,从跟随者身上发现亮点,予以重视,培植增长点。选择山东省作为研究对象,是因为本地区地理环境多样,经济发展水平参差不齐,物流发展差异化明显。拥有区位优势的沿海地区,基础设施完善,港口众多,海洋运输业发达,运输成本较低,具有很强竞争力。处于边缘的山东南部地区,利用自己腹地广阔、劳动力丰富、薪资具有可比优势的特点,发展商贸物流业,渐成规模,成为重要的经济增长点,这也使得临沂、济宁两市在人均产出不高情况下,物流综合能力排名却靠前。以济南为首的省会都市圈,拥有较多资源,但分布不均衡,既有超大城市,又有规模很小城市,而人口规模又是影响需求的重要因素,规模小,发展的后备资源就有限,物流需求不足。因此,区域物流发展依赖于地区间的经济联系,在一个经济联系紧密的空间内,会形成相当规模的区域物流圈。本文采用主成分聚类分析法对山东省各地市区域物流发展情况进行综合评价,根据实证结果和经验判断,将山东省的物流区域划分成三大块:青岛及周边地区、济南及周边地区和鲁南地区。根据主成分聚类分析结果来划分物流节点中心城市,先用主成分分析法计算各市物流综合能力得分,然后根据前两个主成分结果进行聚类,最后将17地市划分成6类,再依据聚类划分结果和各市物流综合能力得分,以及参照现实情况,来确定不同空间内的物流中心城市。从最后划分结果来看,青岛、济南和临沂分别为各自区域的物流节点核心城市。将GIOWA算子的组合预测方法引入到物流需求预测中,构建山东省物流需求的一种变权系数组合预测模型,使用近20年货运量数据,在单项预测基础上,结合贴近度准则,建立算术平均最小贴近度的GIOWA算子的最优组合预测模型,预测了未来五年山东省物流需求情况,从物流需求规模变化能反映经济状况好坏的角度出发,结合运用组合预测方法得出的2015-2019年山东省货运量数值,下定结论:山东省经济企稳迹象比较明显,未来经济将会保持良好的发展势头。
[Abstract]:As the name implies, logistics refers to the warehousing, packaging, processing, handling, loading, transportation and other processes. There are a variety of tools and means of implementation, and the effect of transportation is not the same. The natural geographical difference is the innate factor that causes the difference of transportation mode, and it is also a very important factor. This difference exacerbates the imbalance in economic development, and the regions with advantages improve their infrastructure, attract talented people, improve technology and equipment, improve the efficiency of logistics, and reduce the cost of transportation links of enterprises by virtue of their own conditions. Take the lead in the competition. To study the logistics of a region, it is necessary to include the whole and part of the region, to analyze the favorable conditions and disadvantages of the development of regional logistics, to find out the leader and follower of the region, and to learn advanced ideas from the leader. Discover the bright spot from the follower, pay attention to, cultivate the growth point. Shandong Province is chosen as the research object because the geographical environment is diverse, the level of economic development is uneven, and the logistics development is obviously different. Coastal areas with advantages of location, perfect infrastructure, numerous ports, developed marine transportation, low transportation cost and strong competitiveness. On the edge of the southern part of Shandong Province, taking advantage of its vast hinterland, abundant labor force, and comparable advantages in wages, the development of trade and logistics industry has gradually become a major economic growth point, which also makes Linyi, Jining two cities in the case of low per capita output, logistics comprehensive capacity ranking is at the top. The capital metropolitan area, headed by Jinan, has more resources, but the distribution is uneven. There are both mega-cities and small cities, and the size of the population is an important factor affecting demand. If the scale is small, the reserve resources for development will be limited. Insufficient logistics demand. Therefore, the development of regional logistics depends on the economic relations between regions. In a space with close economic ties, regional logistics circles will be formed on a considerable scale. In this paper, we use principal component cluster analysis to evaluate the development of regional logistics in various cities of Shandong province. According to the empirical results and empirical judgment, the logistics region of Shandong Province is divided into three parts: Qingdao and its surrounding areas. Jinan and its surrounding areas and southern Shandong region. According to the result of principal component clustering analysis, this paper divides the central city of logistics node, first calculates the score of logistics comprehensive ability of each city by principal component analysis, then clusters according to the results of the first two principal components, and finally divides 17 cities into 6 categories. Then, according to the result of clustering division, the score of logistics comprehensive ability of each city, and referring to the actual situation, the logistics center city in different space is determined. From the final division, Qingdao, Jinan and Linyi are the core cities of logistics nodes in their respective regions. This paper introduces the combination forecasting method of GIOWA operator into logistics demand forecasting, constructs a variable weight coefficient combination forecasting model of logistics demand in Shandong Province, uses the freight volume data of nearly 20 years, on the basis of single forecast, combines the closeness degree criterion. The optimal combination forecasting model of GIOWA operator with arithmetic average minimum closeness is established, and the logistics demand in Shandong Province in the next five years is forecasted. From the point of view that the change of logistics demand scale can reflect the economic situation, Combined with the value of freight volume of Shandong Province in 2015-2019, it is concluded that the signs of economic stabilization in Shandong Province are obvious, and the future economy will maintain a good momentum of development.
【学位授予单位】：安徽财经大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：F259.27

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