物流发展呼唤综合政策

“转型”问题的有效破解近年来,浙江省以构建社会化道路物流服务体系为目标,着重在物流企业试点培育、快速货运发展、信息化、物流业务培训、     

"芯片荒"倒逼汽车产业革命

2021年9月初,在美国的汽车芯片大宗交易中出现了涨价十倍的现象.一些舆论认为,这是因为自2020年12月起,汽车芯片产能下降,而全球汽车生产复苏,导致芯片价格涨势不止.这或许只是事实的一部分.今后,摆在无数车企面前的主要矛盾,将不是芯片的产能和价格,而是汽车车型不断创新的大环境中,新型汽车对芯片的需求量更大、品质要求更高.这种情况在相当长时间里将使得芯片需求压力不会缓解,反而可能产生出一个个新的产业子部门,并且细化、分化.     

中国面对全球动力电池"荒"的解决方案

动力电池是新能源汽车的"心脏",在一定程度上掌管着新能源汽车发展的命脉.对车企来说,动力电池就像悬在头上的"达摩克利斯之剑",其性能质量、生产供应和服务保障决定着新能源汽车的生命持久力.随着新能源汽车热度不断提升,全球动力电池供需缺口引发电池"荒",中国唯有突破动力电池产能、原材料、技术三大困境,化危机为先机,才能在这场全球竞技中实现弯道超车,占领优势地位.     

"2006现代物流管理高层论坛"圈点物流"十一五"

5月19日,由中国交通运输协会主办、中国物流与采购联合会等单位协办的"2006现代物流管理高层论坛"在京举行.     

民营物流"安居工程"

俗话说,安居才能乐业.物流基地的建设,正仿如民营物流的"安居工程". 宝供安居样本 "对外资全面开放后,毫不夸张地说,物流业将会血雨腥风.宝供一定要转型为物流供应链的专业服务商才有可能应对."     

校企深度合作 共育物流人才

《国务院关于大力发展职业教育的决定》和《国家中长期教育改革和发展规划纲要（2010—2020年）》强调,要“大力推行工学结合、校企合作的培养模式。”教育部部长袁贵仁在2010年3月召开的职业教育与成人教育工作会议上指出,“当前我国职业教育发展的致命弱点是校企合作。”     

启动中国物流人才教育工程

一、我国物流业的快速发展对物流人才提出了迫切需求2001年是中国物流理论研究和物流实践极为活跃的一年，是多层次物流教学快速启动与发展的一年，也是物流界同仁努力拼搏、硕果累累的一年。物流产业的蓬勃发展必然导致对物流人才的迫切需求，目前物流专业人才已被列为我国12类紧缺人才之一。第一，企业物流人才。为了提高企业的核心竞争力与市场竞争力，不同类型企业都或先或后把物流作为一个重大战略问题来抓，要么把物流作为企业的核心竞争力而改造提升自己的物流系统，要么把物流活动分离出来交给第三方物流公司，把上游与下游优化结合…     

“互联网+”下,物流人才培训之“渴”有解!

<正>物流业未来需要什么样的人才?最需要技能型人才、管理型人才,更需要专业化、国际化、互联网化的复合型专业人才。借助互联网时代的契机,物流教育互联"网平台"物留客"应运而生,成为物流教育领域重视业务实操、分享经验的典范,形成业界生态圈,用自身的行动为中国的物流人才培养与教育贡献力量。     

寻找物流"前世"之旅

刚刚过去的"妇女节",成为了电商促销的"机遇",这让很多人想起去年11月11日,阿里巴巴天猫"双十一"打响14秒成交额破10亿,1分36秒成交额破100亿.大家都知道,"双十一"是一场恶战,拼手速、捡便宜;"双十一"是场盛宴,疯狂剁手买买买.而疯狂过后,就是等快递了……     

物流企业:思路决定出路--访物流专家丁克义

丁克义,这位刚退休的物流专家曾在交通运输行业任过多项要职,征战商场多年.退休并没有隔断他与物流行业的缘分,他仍然活跃于物流行业的各种会议和论坛上.不久前,记者往深圳招商大厦请教了丁总.     

Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network

Based on train scheduling, this paper puts forward a multi-objective optimization model for train routing on high-speed railway network, which can offer an important reference for train plan to provide a better service. The model does not only consider the average travel time of trains, but also take the energy consumption and the user satisfaction into account. Based on this model, an improved GA is designed to solve the train routing problem. The simulation results demonstrate that the accurate algorithm is suitable for a small-scale network, while the improved genetic algorithm based on train control (GATC) applies to a large-scale network. Finally, a sensitivity analysis of the parameters is performed to obtain the ideal parameters; a perturbation analysis shows that the proposed method can quickly handle the train disturbance.     

The demand impacts of train punctuality in great britain: systematic review,meta-analysis and some new econometric insights

Transportation - This paper updates and extends the systematic review and meta-analysis of Wardman and Batley (Transportation 41:1041–1069, 2014), which hitherto was the most comprehensive...     

Assessing the effects of several variables on freight train fuel consumption and performance using a train performance simulator

The impact of several variables on freight train fuel consumption and performance are assessed using a train performance simulator. These variables include: wind, precipitation, number of cars, and number and type of locomotives. The input to the train performance simulator includes data related to train characteristics and data related to external conditions such as weather. The simulator output represents fuel consumption expected under a given set of conditions. Graphical and cost/benefit approaches were used to assess operations alternatives.     

A simulation model of train travel on a rail transit line

A simplified simulation model for the operational analysis of a rail rapid transit train is presented. The model simulates the movement of a train along a route, and develops the relationships of time—distance, time—speed and distance—speed. The inputs to the model are the profile of speed limits and the dynamic characteristics of the train. Without the information on the track geometry and tractive effort, the model determines the speed of the train at a location based on the previous and future speed limits relative to the location. It was found that the model can fairly accurately simulate the relationship between travel time and distance. A comparison of the train travel times between the actual and simulated runs is presented. Because of the simplicity of input and calculation method, the model can be a useful tool for the “desk-top” analysis of frequently occurring planning problems of a commuter rail or rail rapid transit line, such as the impacts of changes in speed limits, station locations, station stopping policy, addition/elimination of stations, and types of rail cars.     

Efficient dispatching rules on double tracks with heterogeneous train traffic

The most natural and popular dispatching rule for double-track segments is to dedicate one track for trains traveling in one direction. However, sometimes passenger trains have to share some portions of the railway with freight trains and passenger trains are traveling faster and faster nowadays. The major drawback of this dedicated rule is that a fast train can be caught behind a slow train and experience significant knock-on delay. In this paper, we propose a switchable dispatching policy for a double-track segment. The new dispatching rule enables the fast train to pass the slow train by using the track traveled by trains in the opposite direction if the track is empty. We use queueing theory techniques to derive the delay functions of this policy. The numerical experiments show that a switchable policy can reduce the fast train knock-on delay by as high as 30% compared to a dedicated policy. When there are crossovers at the middle of the double-track segment, our proposed switchable policy can reduce the delay of the fast trains by as high as 65%.     

Reinforcement learning approach for train rescheduling on a single-track railway

Optimal rail network infrastructure and rolling stock utilization can be achieved with use of different scheduling tools by extensive planning a long time before actual operations. The initial train timetable takes into account possible smaller disturbances, which can be compensated within the schedule. Bigger disruptions, such as accidents, rolling stock breakdown, prolonged passenger boarding, and changed speed limit cause delays that require train rescheduling. In this paper, we introduce a train rescheduling method based on reinforcement learning, and more specifically, Q-learning. We present here the Q-learning principles for train rescheduling, which consist of a learning agent and its actions, environment and its states, as well as rewards. The use of the proposed approach is first illustrated on a simple rescheduling problem comprising a single-lane track with three trains. The evaluation of the approach is performed on extensive set of experiments carried out on a real-world railway network in Slovenia. The empirical results show that Q-learning lead to rescheduling solutions that are at least equivalent and often superior to those of several basic rescheduling methods that do not rely on learning agents. The solutions are learned within reasonable computational time, a crucial factor for real-time applications.     

“八挂来网”的魅与惑

"自8月份央视报道后,河南物流信息系统‘八挂来网'每天都会遭遇黑客各种形式的攻击.经过这段时间的磨练,我的防黑客水平又提升了一个层次!"河南物流信息系统"八卦来网"负责人齐正元诙谐地打趣说.