GIS与电子商务下的物流管理

为更好地完善电子商务下的物流管理技术,讨论了GIS技术服务于物流管理的优势及其在物流分析中的应用,基于GIS的物流管理技术符合物流特点,能更好地满足电子商务下物流的发展。     

运用ActiveX技术开发炼钢物流可视化仿真平台

基于工位、作业和作业规则的炼钢物流细胞自动机仿真模型,运用ActiveX技术开发了具有计算机图形化建模、仿真和分析功能的炼钢物流仿真平台。该仿真平台使用了面向对象的软件设计方法和图形窗口环境,界面友好,使用简便。以攀钢提钒炼钢厂的实例仿真检验了仿真平台组态建模、捆绑仿真核心算法、仿真运行和仿真结果处理等功能的有效性。仿真平台具有优良的扩展性,通过扩展工位组件以及选择适当的仿真算法,可用于整个钢铁制造流程的仿真。     

装备系统转场时间计算方法研究

装备系统转场时间研究对于装备系统快速反应能力的设计与评价意义重大,从3个方面分析装备系统转场时间的影响因素,包括：装备系统规模、转场活动和所需资源、运输站点之间的拓扑结构。给出各类转场活动时间的计算方法,分析各转场活动时顺的确定原则和依据,建立描述各个转场活动之间时序与逻辑的关系矩阵,在此基础上给出装备系统转场时间计算方法。并以实际应用案例加以佐证,为装备系统转场时间分析提供方法和范例。     

基于 WebGL 的多AGV物流调度仿真系统的研究与应用

针对K公司汽车总装生产线中多AGV(Automated Guided Vehicle)电量分配不均、路径冲突、AGV利用率低等物流调度问题，为了提高K公司生产线中多AGV物流调度的效率，设计了一种多AGV物流调度仿真系统；利用WebGL(Web Graphics Library) 技术建立了整个生产线场景的三维模型；系统能够自动给AGV分配任务，在AGV执行任务时，将AGV的电池电量和AGV路径规划问题考虑到物流调度系统中；该系统实现了对物流调度系统中AGV运输任务的合理分配，提高了物流调度的效率；经实际应用满足了K公司汽车总装生产线多AGV物流调度工程上的应用。     

数字孪生驱动的车间物流可视化监控方法

为解决数字化车间配送过程不透明、监控手段单一、实时性差、智能化管控难等问题，在分析复杂产品装配车间物流监控需求的基础上，提出一种数字孪生驱动的车间物流配送实时监控方法。构建数字孪生体系框架和运行模式，并对支持该系统实现的车间虚拟建模技术、车间物流实时状态信息处理等关键技术进行了研究。设计并开发了服务系统，实验验证结果表明：采用该监控系统显著提高了车间物流的透明度、实时性、交互性，所提方法可行。     

Production logistics digital twins: Research profiling,application, challenges and opportunities

In the era of Industry 4.0, Production Logistic Digital Twins (PLDTs) have garnered remarkable attention from both academic and industrial communities. This is evident from the growing number of research publications on PLDTs in international scientific journals and conferences. However, given the diversity and complexity of production logistics activities, there is a pressing need for systematic literature review to chart past research and identify potential directions for future endeavors. Therefore, this study primarily focuses on the application of Digital Twins (DTs) in Production Logistics (PL). Firstly, an analysis of PLDTs research profiling is carried out based on general trends, keywords, application scenarios, and basic functions. Secondly, the functional characteristics of PLDTs are examined while summarizing their advantages and limitations across various application scenarios such as transportation, packaging, warehousing, material distribution, and information processing. And the roles played by smart technologies such as Internet of Things (IoT) in PLDTs system are discussed. Finally, possible challenges and future directions of PLDTs in industrial application are presented, accompanied by appropriate classification and extensive recommendations.     

MGA-Net+: Acceleration-based packaging work recognition using motif-guided attention networks

This study presents a new method for recognizing complex human activities within the logistics domain, such as packaging operations, using acceleration data from a body-worn sensor. The recognition of packaging tasks using standard supervised machine learning is complex because the observed data vary considerably depending on the number of items to be packed, the size of the items, and other parameters. In this study, we focused on the characteristics and necessary key actions (motions) that occur during a specific operation. For instance, when the packaging tape is stretched while assembling the shipping boxes. To focus on these characteristic actions when recognizing data, we propose the use of an attention-based neural network. With our method, the attention-based neural network’s training is guided such that its focus is on the motifs. In addition, this method was designed to accurately recognize short operations by leveraging data augmentation techniques. We tested our method on two logistics datasets and achieved a 3.9% improvement over the previous MGA-Net approach.