天津港物流需求预测和物流发展策略研究

发布时间：2018-06-25 07:31

本文选题：灰色神经网络 + 港口物流需求预测　；参考：《天津大学》2012年硕士论文

【摘要】：随着近些年中国外贸交易额的持续高速增长以及世界经济一体化程度的加深，，世界各国的大型港口特别是枢纽港将在推进经济发展的历程中发挥重要作用，尤其对于港口资源及腹地企业资源的配置方面。随着全球生产与制造业正在逐步向亚洲尤其是中国转移，我国沿海港口将承担着重要的货物疏散任务，所以加快发展港口现代物流业是经济发展的客观要求，也是我国港口物流业做强做大的重要机遇，抓住这一机遇的前提是对港口物流需求的发展趋势进行预判，尤其是港口物货物和集装箱吞吐量水平的变化。考虑到物流需求的非线性变化特点及我国物流数据统计不完善的特殊情况，本文创造性地将灰色理论与神经网络算法相结合，以克服数据贫乏和数据非线性的困难。所以，作者首先对灰色系统理论和人工神经网络理论进行简述，通过分析影响区域物流需求的各项指标来研究港口物流需求发展问题，并进一步以天津港为研究对象进行实证分析。作者简单介绍物流需求预测的基本理论和方法，包括区域物流需求预测指标选取、灰色理论、神经网络算法等，这是后续实证工作展开的理论基础。其次，重点分析了影响港口物流需求的五方面因素，即经济水平、产业结构、消费水平、区域贸易及固定投资额，通过对五方面影响因素的分析，提取出用于天津港港口实际需求预测的二级指标集。然后，根据港口物流需求预测指标集的设定以及对算法可行性分析的基础上，构建了用于港口物流需求预测的灰色神经网络组合模型，该模型以影响指标和时间因素共九个指标作为输入，以港口货物吞吐量作为输出，实证研究表明模型对输入与输出之间的非线性关系进行了较好的拟合。最后，文章以天津港港口物流需求预测为实证研究的对象，对港口未来几年的发展给出预测结果，并根据预测结果及国内外成熟港口的成长模式提出天津港港口物流发展的五大方面策略，包括陆上疏运结构、内河运输、港口物流功能、港企合作和信息平台建设等。研究结果表明，采用灰色预测理论与非线性预测功能的神经网络的组合算法，能够有效地发现港口物流需求影响因素与输出指标之间的联系，本文的实证研究有效验证了该算法的可靠性和可行性，为研究港口物流需求预测乃至区域物流需求预测提供了另一种思路。
[Abstract]:With the rapid growth of China's foreign trade volume and the deepening of the integration of the world economy in recent years, the large ports around the world, especially the hub ports, will play an important role in the process of promoting economic development. Especially for the allocation of port resources and hinterland enterprise resources. With the gradual transfer of global production and manufacturing to Asia, especially China, China's coastal ports will undertake the important task of cargo evacuation, so speeding up the development of modern port logistics is the objective requirement of economic development. It is also an important opportunity for China's port logistics industry to become stronger and bigger. The premise of seizing this opportunity is to pre-judge the development trend of port logistics demand, especially the change of port cargo and container throughput level. Considering the characteristics of nonlinear change of logistics demand and the special situation of incomplete logistics data statistics in China, this paper creatively combines grey theory with neural network algorithm to overcome the difficulties of data scarcity and data nonlinearity. Therefore, firstly, the author makes a brief introduction to the grey system theory and artificial neural network theory, and studies the port logistics demand development by analyzing the indexes that affect the regional logistics demand. And further take Tianjin Port as the research object to carry on the empirical analysis. The author briefly introduces the basic theories and methods of logistics demand forecasting, including the selection of regional logistics demand forecasting indicators, grey theory, neural network algorithm, etc. This is the theoretical basis of the subsequent empirical work. Secondly, the paper analyzes the five factors that affect port logistics demand, that is, economic level, industrial structure, consumption level, regional trade and fixed investment. The second level index set for actual demand forecast of Tianjin Port is extracted. Then, according to the setting of port logistics demand forecasting index set and the feasibility analysis of the algorithm, a grey neural network combination model for port logistics demand forecasting is constructed. The model takes nine indexes of influence index and time factor as input and port cargo throughput as output. The empirical research shows that the nonlinear relationship between input and output is well fitted by the model. Finally, the article takes Tianjin port logistics demand forecast as the empirical research object, gives the forecast result to the port development in the next few years. According to the forecast results and the growth model of domestic and foreign mature ports, the paper puts forward five major strategies of port logistics development of Tianjin Port, including land transport structure, inland river transport, port logistics function, cooperation between Hong Kong and enterprises and information platform construction. The research results show that the combination algorithm of grey forecasting theory and nonlinear forecasting function can effectively find the relationship between port logistics demand influencing factors and output indexes. The empirical research in this paper effectively verifies the reliability and feasibility of the algorithm, and provides another way of thinking for the study of port logistics demand prediction and regional logistics demand prediction.
【学位授予单位】：天津大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F259.23;F552.6

【参考文献】