短流通:商业产业组织的新理念

全球性零售商的迅速崛起,一些中间商被逐步挤出;同时,现代金融业、物流业和广告业的发展,使产品到达消费环节的交易变得越来越便捷和经济。基于国内外流通产业组织的发展变化,本文提出"短流通"概念,通过阐述其特征与实现形式,从产业经济学理论与实证角度研究了短流通的内涵与意义,综合现代流通产业发展的特点,说明短流通的实现条件,旨在说明"短流通"是现代商业产业组织的新理念。

商业组织中的短流通理念及先进经验借鉴

从短流通理念入手,研究市场交易与纵向一体化、长期契约关系等短流通模式。然后,选取日本农产品直销、戴尔直销以及美国大型零售商作为典型案例,探究发达国家短流通的先进经验。研究发现,短流通实现需要具备以下三个条件:一是厂商是大型企业或专业合作组织;二是零售商具有核心流通能力;三是拥有完善的物流体系。

电解车间母线的短路通电测试

焦作万方铝业公司新建 92台 2 80kA电解槽于 2 0 0 0年 11月通电投产。投产前 ,对电解车间母线作了短路通电测试。笔者主持制定了短路通电测试方案。

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

Short-time prediction for traffic flow based on wavelet de-noising and LSTM model

Aiming at the problem that some existing traffic flow prediction models are only for a single road segment and the model input data are not pre-processed,a heuristic threshold algorithm is used to de-noise the original traffic flow data after wavelet decomposition.The correlation coefficients of road traffic flow data are calculated and the data compression matrix of road traffic flow is constructed.Data de-noising minimizes the interference of data to the model,while the correlation analysis of road network data realizes the prediction at the road network level.Utilizing the advantages of long short term memory(LSTM)network in time series data processing,the compression matrix is input into the constructed LSTM model for short-term traffic flow prediction.The LSTM-1 and LSTM-2 models were respectively trained by de-noising processed data and original data.Through simulation experiments,different prediction times were set,and the prediction results of the prediction model proposed in this paper were compared with those of other methods.It is found that the accuracy of the LSTM-2 model proposed in this paper increases by 10.278%on average compared with other prediction methods,and the prediction accuracy reaches 95.58%,which proves that the short-term traffic flow prediction method proposed in this paper is efficient.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

An Improved Ant Algorithm for VRP with the Shortest Delivery Time in Distribution Center

It is considered here for an improved ant algorithm to minimize the delivery time of VRP （Vehicle Routing Problem） in distribution center. Firstly VRP which objective is to minimize the delivery time is given, then the optimal vehicle distributionroute-division approach of distribution center for given customer array is defined, and the ant colony algorithm for VRP with objective to minimize the delivery time is presented. The research of this paper is based on the information and application case of tobacco network in Hangzhou city. By investigating, we created two different system models. One is division of distribution route of distribution center, and the other is optimization of single vehicle route. The computational example tests and verifys the solution.