基于主成分回归的区域物流需求预测研究——以云南省为例

为了能够给区域物流发展政策的制定、物流基础设施建设规模的确定、物流市场态势的分析等提供定量的物流需求规模数据,建立科学合理的预测模型显得尤为重要。首先,研究区域物流与区域经济的关系;其次,从货运量、货运周转量两个指标中选取货运周转量来表征区域物流需求规模;最后,从区域经济指标中选取第一产业总产值、第二产业总产值、第三产业总产值、社会消费品零售总额、固定资产投资额、进出口额等指标作为影响因素,借助SPSS统计分析软件,以云南省统计数据为基础,建立基于主成分回归方法的区域物流需求预测模型。研究结果表明,该模型在对云南省物流需求规模进行预测时,模型平均相对误差小于4%,预测精度高,可以作为中短期物流需求预测的工具。     

公益性物流发展策略研究

物流业作为国民经济和社会发展的基础性、战略性产业,以"市场运作,政府引导"为主要原则的发展模式,极大地发挥了市场在物流发展中的决定性作用,较好地解决了政府与市场的关系问题。然而,物流不仅具有经济性,同时还具有社会性,以国有资本发展公益性物流是政府保持宏观经济稳定,加强和优化公共服务,保障公平竞争,推动可持续发展,促进共同富裕,弥补市场失灵的重要政策方向。     

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为降低物流需求建模中最小二乘支持向量机（LSSVM）的结构复杂性、进一步提高LSSVM对物流需求的预测精度,提出一种基于灰色关联分析（GRA）与核主成分分析（KPCA）的LSSVM预测方法。首先利用GRA找出物流需求的主要影响因素;然后利用KPCA提取主要影响因素的非线性主成分,消除因素之间的多重相关性;最后,将提取出的非线性主成分作为LSSVM的输入变量,构建物流需求预测模型,并采用改进粒子群(IPSO)算法调整LSSVM参数。运用该方法对我国物流需求进行实例分析,结果表明,该方法有效减少了LSSVM输入变量个数,简化了LSSVM结构,并且在一定程度上提高了物流需求预测精度。     

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货运量预测是制定物流政策和决定物流基础设施布局的重要依据。针对受多因素影响的货运量预测具备较强非线性和模糊性特征,提出一种基于趋势分解和小波变换的多重“分解—集成”预测方法。利用趋势分解将货运量分解为趋势项和非趋势项,通过小波分解将非趋势项进一步分解成低频项和高频项,分别建立预测模型,选用相加集成得到货运量预测值。实证表明,“分解—集成”的预测策略将非平稳货运量分解为相对平稳的子序列组合,降低了问题复杂度,有效提高了预测性能,与传统的趋势分解预测模型和小波分解预测模型相比,多重“分解—集成”预测模型精度更高。     

基于“门到门”运输的铁路物流“库存-运输”模型研究

我国铁路货运改革提出了"门到门"运输的发展模式,给铁路物流企业提出了新的要求和挑战。对于铁路货运来讲,这是改变体制、面向竞争、融入市场的良好发展契机。铁路物流企业将来要更多的承担全程物流的任务,因此,如何在具有"效益悖反"的库存和运输之间获得整体最优的运输模式成为亟待解决的问题。本文分析了铁路物流系统中的库存和运输费用,用时间因素反映二者的效益悖反关系,建立了以铁路运输为主导、联合多种运输方式实现"门到门"运输的库存-运输整体最优模型,提出求解模型的模拟退火算法,并对一算例进行简单分析,最后基于模型为铁路物流企业的发展提供针对性建议。     

基于集合覆盖模型的报废汽车回收点选址优化研究

报废汽车是重要的再生资源,为了提高报废汽车的回收利用率,实现其经济价值和资源环境价值,需要构建高效率、高效益的报废汽车逆向物流网络,而报废汽车回收点的选址对整个网络的运作有重大影响。本文以成都市为例构建了报废汽车回收点选址优化的集合覆盖模型,然后针对该模型设计了相应的算法,并对模型进行了分析和验证。     

台湾运输物流知识管理系统之建立

因应全球产业变动趋势、经济贸易动态与运输物流活动此三者存在着密切的连动关系,其中运输物流涵盖范围广大,有陆、海、空运及复合运输等,信息相当庞大,因此需建立完整的数据库作系统化整合信息与统计数据,并转化为知识以利用。运用微软SharePoint建构运输物流知识管理系统,搜集国际最新消息与国际评比提供产业最新国际发展趋势与建立统计数据库以供后续进一步分析,最后导入台湾运输物流竞争力指标研究成果。最终,运输物流知识管理系统成功地将运输物流数据库、台湾运输物流竞争力指标建立之研究经验与知识管理价值核心进行整合。     

Vehicle Positioning for Improving Road Safety

Abstract

Vehicle positioning is a key requirement for many safety applications. Active safety systems require precise vehicle positioning in order to assess the safety threats accurately, especially for those systems which are developed for warning/intervention in safety critical situations. When warning drivers of a local hazard (e.g. an accident site), accurate vehicle location information is important for warning the right driver groups at the right time. Global positioning system and digital maps have become major tools for vehicle positioning providing not only vehicle location information but also geometry preview of the road being used. Advances in wireless communication have made it possible for a vehicle to share its location information with other vehicles and traffic operation centres which greatly increases the opportunities to apply vehicle positioning technologies for improving road safety. This paper presents a state‐of‐the‐art review of vehicle positioning requirements for safety applications and vehicle positioning technologies. The paper also examines key issues relating to current and potential future applications of vehicle positioning technologies for improving road safety.     

Collaborative Logistics from the Perspective of Road Transportation Companies

ABSTRACT

To survive under the ever increasing competitive and global pressures to operate more efficiently, transportation companies are obliged to adopt a collaborative focus. Various types of cooperative supply chain relationships have been discussed in both professional and academic literature over the last decades. However, research on horizontal cooperation in logistics remains scarce and scattered across various research domains. Companies operating at the same level of the supply chain and performing comparable logistics functions may cooperate horizontally to increase their productivity, improve their service level and enhance their market position. In this paper, the focus is on the operational planning of horizontal cooperations between road transportation carriers. Following a scientific literature review, a distinction may be made between two operational approaches to horizontal logistics collaboration: order sharing and capacity sharing. For both research streams, a detailed overview of solution techniques proposed in literature is presented. Moreover, some interesting opportunities for future research are identified.     

Impact of scale increase of container ships on the generalised chain cost

In recent years, an increase in the size of the container ships could be observed. The question is how these larger ships will influence the total generalised costs from a port of loading to a destination in the European hinterland. The second question is whether a scale increase of the container ships on other loops, such as a loop from the United States to Europe, has the same impact on the generalised chain costs as on the loop from Asia to Europe. A derived question is which element of the total chain has the highest importance, and whether this balance varies as the ship size changes. In this article, a model is developed that allows answering the above research questions. The model is designed to simulate the cost of a complete loop of a container ship and of a chain that uses that same loop. For the chain cost simulation, the maritime part is determined by the loop. From the ports of loading and unloading, the port container handling and the hinterland transportation costs are also integrated. The model also allows calculating the total chain cost from a point of origin (either a hinterland region or a port) to a destination point (also a port or a hinterland region). An actual container loop of a container shipping company can be introduced in the model. An application is made to two existing container loops, namely from Asia respectively the United States to Europe. It turns out that changing ship does indeed lead to economies of scale, but also that the impact is larger on the Asia–Europe connection than on the US–Europe connection. Furthermore, the maritime component has the biggest share in the total chain cost, but as ship size increases, the shares start getting closer to each other. This research contributes to the existing literature in two ways. First of all, it quantifies the impact of the scale increase of container ships throughout the total chain. Second, this is done from a bottom-up engineering modelling approach.