【作者】 王慧萍；

【导师】 王展青；

【作者基本信息】 武汉理工大学 ， 应用数学， 2010， 硕士

【摘要】 当前物流技术呈现日新月异的发展,第三方物流已经成为加快物资流通速度、节约和节省资金在途费用和仓储费用的有效途径,已引起专家学者们的广泛关注。能否科学、系统的评价第三方物流企业的竞争力,决定着第三方物流企业能否继续壮大发展。而目前对于第三方物流企业竞争力的讨论比较少,由于物流企业竞争力问题中包含着大量的不确定性和模糊性,需要按照系统的思想和方法从各个角度对其进行综合评价和预测,而一般的评价预测方法往往存在主观随意性。为此,本文引入BP神经网络方法对第三方物流企业竞争力进行评价。主要研究工作包括：1、首先探讨第三方物流发展的基本情况,分析其研究意义,研究背景,研究内容和研究方法等,研讨目前研究状况在该领域的优势和存在的不足。2、参考目前我国第三方物流企业竞争力的界定和特点,通过论述评价指标体系的设计原则和评价指标选取的原则,结合第三方物流企业竞争力的实际情况来建立第三方物流企业竞争力评价指标体系。3、通过前文提出的第三方物流企业竞争力评价指标体系来建立第三方物流企业竞争力的BP神经网络评价模型。在与其他评价方法的比较中引出BP神经网络算法,并对算法的优越性和算法改进进行阐述,给出可行的、合理的程序设计,使BP神经网络训练达到设定的误差精度。4、通过实例来验证指标体系和模型的可行性和有效性。运用训练好的BP神经网络对三个具有竞争关系的第三方物流上市公司进行竞争力评价,得出它们的竞争力排名。经过对比分析,证明了基于BP神经网络方法的第三方物流企业竞争力评价具有实效性。综上所述,在对第三方物流企业竞争力进行评价的诸多方法中,BP神经网络模型是有效和可行的,这为第三方物流企业竞争力评价提供了一种新的思路。更多还原

【Abstract】 With the continuous development of logistics technology, third party logistics is arousing great attention, as a way of improved material flow rate, saving storage costs and cost of capital in transit. Scientific and comprehensive analysis and evaluation of the competitiveness of third party logistics, has become an urgent need to address the issue for the logistics enterprises.However, the discussion of the competitiveness of third-party logistics enterprises is relatively rare at present, because of the problem of the competitiveness of logistics enterprises including a large amount of uncertainty and ambiguity, we need to follow the systematic ideas and methods from all angles to evaluate and predict the problem. Yet the president general evaluation and prediction method often has been subjective and arbitrary. Therefore, BP neural network is introduced for evaluation of TPL enterprises competitive ability. The main work includes:1. Based on the analysis of third party logistics development, discuss the necessity and importance of the study about the evaluation of TPL enterprises competitive ability. The paper analyzed the advantages and shortcomings in the field, by reviewing the president research.2. According to the current definition and characteristics of TPL enterprises competitive ability in our country and through principles of evaluation index selection and evaluation index system designation principles, the index system of TPL enterprises competitiveness evaluation has been designed.3. Combined with evaluation system to create a model of TPL enterprises competitiveness evaluation. With the comparison of other evaluation method lead to BP neural network algorithm. According to TPL enterprises competitiveness index system, the paper has designed BP neural network model and gave practical evaluation procedures. In the calculation method, the neural network toolbox with MATIAB (NNT) has been used to design and calculate the network. The error of the model achieved to the desired range, by the training and testing of the studying samples.4. Using an example to validate that the indicator system and model are feasible and effective. The paper has evaluated the competitive ability of three TPL listed companies which has competitive relationship and obtained their competitiveness ranking, through the well trained BP neural network. It has been proved that the result was correct by analyzing the actual state of the three TPL listed companies.In summary, the application of BP neural network model for third-party logistics evaluation of the competitiveness of enterprises is effective and feasible, which provides a new way of thinking about the evaluation of third-party logistics enterprises competitive ability.更多还原