进入供应链系统的物流

信息化、全球化、多功能化和一流的服务水平,已成为迈向21世纪的物流企业追求的目标.     

论企业物流的营销战略

随着社会的迅速发展,物流业的发展对企业利润的影响正起着惊人的变化,正确认识物流的营销战略,摆正物流营销战略在企业中的位置,如何利用物流营销战略促进企业效益最优化,得到了人们越来越多的关注,本文就我国当前的企业物流管理中的一些问题进行阐述,并提出部分应对措施。     

日本电子商务物流发展一瞥

在物流作业的各个环节使用各种先进的物流专用设备和技术,是各物流企业提高作业效率和节约服务成本的一个主要手段。     

自建物流难在出路

在电商都开始自建物流的今天,技术是最容易克服的问题。相反,思想转变和未来的发展出路才是难点所在。     

从实例看企业物流三大资源的整合

在企业的物流战略规划和调整中,资源的整合是极其关键的。所谓资源整合,就是企业根据经营战略的定位和走向,对有关的物流资源进行重新配置的过程。虽然不同的企业为了实施经营战略所采取的资源整合方式不尽相同,但其重点基本上包括“客户资源的整合”、“物流服务资源的整合”和“信息资源的整合”三方面。     

中小企业的物流发展战略

我国中小企业所处的社会经济环境正在发生着深刻变化,日益要求中小企业在战略的高度上发展物流。     

中国外运大手笔实施物流发展战略

对于一个拥有众多分公司的大型集团企业来说．中国对外贸易运输（集团）总公司（以下简称中国外运）IT总监刘闽生的最大体会就是信息化建设一定要集中．不仅要统一管理，制定统一的标准、规范，而且在大型项目上要实现资金和人员的集中。     

日本电子商务物流发展一瞥

日本是个岛国,国土窄长狭小,发展快捷可控、灵活机动的物流方式,同时突出“海运立国”的发展战略无疑是其上选。20世纪50年代中期的日本,在经济复苏过程中,十分重视学习美国先进的技术和管理经验,日本企业在考察美国企业的产品流通环节（如搬运设备、搬运方法、库存物资的堆垛方式以及与厂内运输有关的工厂总体布局和搬运技术的状况之后）,正式引进了“物流”这一概念。     

商品流通业物流信息系统全攻略

近年来,随着商品流通业的快速发展以及竞争的加剧,行业中对物流的管理越来越重视。在基础设施和设备不断投入和完善的同时,物流信息系统成为发展物流必不可少的重要组成部分,它是实现物流经营与管理规范化、标     

《物流信息系统》课程教学现状与对策

《物流信息系统》是物流管理专业的一门必修课,是现代物流管理的必备技能.提高该课程的教学质量,在分析该课程教学的现状和存在问题的基础上,提出一些改善教学的对策方法.     

Japan's labor shortage

Japan needs to reassess its human resource strategy in the context of an emerging labor shortage triggered by demographic changes. To achieve a better quality of life to its people, it has to rely on a more diversified work force in terms of more senior and female workers. Corporate adaptation is eminent in order to sustain the basis of the economic miracle.     

Learning from Japan's Multinationals

Japanese multinationals have evolved a system of management well suited to handle multifaceted co-ordination. In electronics, machinery and automobiles, manufacturing and assembly is complex, demand shifts rapidly and technical innovation is continuous. In these circumstances a management system involving a ‘borderless’ structure, custodial leadership, incremental planning and personnel practices which foster competition among peers, hard work and commitment is effective. Western firms can benefit by adapting some aspects of Japanese practices.     

Logistic混沌映射

混沌理论是研究非线性动力学系统随时间变化的规律。由于混沌系统具有很多优良特性,便将其逐渐应用到密码学及密码分析等学科中。在简述混沌的基础上介绍了一维Logistic混沌映射由倍周期分岔达到混沌的过程,并分析了一些复杂动力学行为。最后将一维Logistic混沌映射应用到图像加密中,并通过仿真实验检验算法的安全性及优越性。     

Logistic label propagation

In this paper, we propose a novel method for semi-supervised learning, called logistic label propagation (LLP). The proposed method employs the logistic function to classify input pattern vectors, similarly to logistic regression. To cope with unlabeled samples as well as labeled ones in the semi-supervised learning framework, the logistic functions are learnt by using similarities between samples in a manner similar to label propagation. In the proposed method, these two methods of logistic regression and label propagation are effectively incorporated in terms of posterior probabilities. LLP estimates the labels of input samples by using the learnt logistic function, whereas the method of label propagation has to optimize the whole labels whenever an input sample comes. In addition, we suggest the way to provide proper parameter setting and initialization, which frees the users from determining a parameter value in trial and error. In experiments on classification (estimating labels) in the semi-supervised learning framework, the proposed method exhibits favorable performances compared to the other methods.     

日系手机动向

面对手机市场日益严峻的形势,NEC、日立和卡西欧选择了联合研发。日前,上述三大日系厂商宣布合并手机业务,成立新的合资公司。经过此次整合,新合资公司将成为日本第二大手机生产厂商,仅次于夏普。公告显示,新公司将于2010年6月     

Logistic Model Trees

Tree induction methods and linear models are popular techniques for supervised learning tasks, both for the prediction of nominal classes and numeric values. For predicting numeric quantities, there has been work on combining these two schemes into model trees, i.e. trees that contain linear regression functions at the leaves. In this paper, we present an algorithm that adapts this idea for classification problems, using logistic regression instead of linear regression. We use a stagewise fitting process to construct the logistic regression models that can select relevant attributes in the data in a natural way, and show how this approach can be used to build the logistic regression models at the leaves by incrementally refining those constructed at higher levels in the tree. We compare the performance of our algorithm to several other state-of-the-art learning schemes on 36 benchmark UCI datasets, and show that it produces accurate and compact classifiers.Editor Johannes FürnkranzThis is an extended version of a paper that appeared in the Proceedings of the 14th European Conference on Machine Learning (Landwehr et al., 2003).