Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Localization of pallets in warehouses using passive RFID system

Warehouse operation has become a critical activity in supply chain. Position information of pallets is important in warehouse management which can enhance the efficiency of pallets picking and sortation. Radio frequency identification(RFID) has been widely used in warehouse for item identifying. Meanwhile, RFID technology also has great potential for pallets localization which is underutilized in warehouse management. RFID-based checking-in and inventory systems have been applied in warehouse management by many enterprises. Localization approach is studied, which is compatible with existing RFID checking-in and inventory systems. A novel RFID localization approach is proposed for pallets checking-in. Phase variation of nearby tags was utilized to estimate the position of added pallets. A novel inventory localization approach combing angle of arrival(AOA) measurement and received signal strength(RSS) is also proposed for pallets inventory. Experiments were carried out using standard UHF passive RFID system. Experimental results show an acceptable localization accuracy which can satisfy the requirement of warehouse management.

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems:A Survey

Automated Guided Vehicles(AGVs)have been introduced into various applications,such as automated warehouse systems,flexible manufacturing systems,and container terminal systems.However,few publications have outlined problems in need of attention in AGV applications comprehensively.In this paper,several key issues and essential models are presented.First,the advantages and disadvantages of centralized and decentralized AGVs systems were compared;second,warehouse layout and operation optimization were introduced,including some omitted areas,such as AGVs fleet size and electrical energy management;third,AGVs scheduling algorithms in chessboardlike environments were analyzed;fourth,the classical route-planning algorithms for single AGV and multiple AGVs were presented,and some Artificial Intelligence(AI)-based decision-making algorithms were reviewed.Furthermore,a novel idea for accelerating route planning by combining Reinforcement Learning(RL)andDijkstra’s algorithm was presented,and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.

Multidimensional Design Paradigms for Data Warehouses: A Systematic Mapping Study

Data warehouses (DW) must integrate information from the different areas and sources of an organization in order to extract knowledge relevant to decision-making. The DW development is not an easy task, which is why various design approaches have been put forward. These approaches can be classified in three different paradigms according to the origin of the information requirements: supply-driven, demand-driven, and hybrids of these. This article compares the methodologies for the multidimensional design of DW through a systematic mapping as research methodology. The study is presented for each paradigm, the main characteristics of the methodologies, their notations and problem areas exhibited in each one of them. The results indicate that there is no follow-up to the complete process of implementing a DW in either an academic or industrial environment;however, there is also no evidence that the attempt is made to address the design and development of a DW by applying and comparing different methodologies existing in the field.

ETL Maturity Model for Data Warehouse Systems:A CMMI Compliant Framework

The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it produces.The decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better decisions.The Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW system.The more mature the ETL process the more reliable the DW system.In this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge produced.The EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and Reliable.Each level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL system.Quality Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL process.Each KPA has its own set of QOs,the execution of which meets the requirements of that KPA.Multiple brainstorming sessions with relevant industry experts helped to enhance the model.EMMwas deployed in two key projects utilizing multiple case studies to supplement the validation process and support our claim.This model can assist organizations in improving their current ETL process and transforming it into a more mature ETL system.This model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.

Spatial aggregations for spatial based decision making in spatial data warehouses

For spatial based decision making such as choice of best place to construct a new department store, spatial data warehousing system is required more and more previous spatial data warehousing systems; however, provided decision making of non-spatial data on a map and so those cannot support enough spatial based decision making. The spatial aggregations are proposed for spatial based decision making in spatial data warehouses. The meaning of aggregation operators for applying spatial data was modified and new spatial aggregations were defined. These aggregations can support hierarchical concept of spatial measure. Using these aggregations, the spatial analysis classified by non-spatial data is provided. In case study, how to use these aggregations and how to support spatial based decision making are shown.

Decision-Making Information System for Academic Careers in Congolese Universities: From Analysis to Design of a Data Warehouse

Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of several disparate files. This makes it difficult to use this data efficiently and profitably. The aim of this study is to develop this transactional database-based information system into a data warehouse-oriented system. This tool will be able to collect, organize and archive data on the student’s career path, year after year, and transform it for analysis purposes. In the age of Big Data, a number of artificial intelligence techniques have been developed, making it possible to extract useful information from large databases. This extracted information is of paramount importance in decision-making. By way of example, the information extracted by these techniques can be used to predict which stream a student should choose when applying to university. In order to develop our contribution, we analyzed the IT information systems used in the various universities and applied the bottom-up method to design our data warehouse model. We used the relational model to design the data warehouse.

基于改进YOLOv5s的仓储货物检测算法研究

针对目前仓储货物分类速度慢、易出错、灵活性差等问题,提出了一种改进YOLOv5s的货物检测算法,对仓储货物进行预分类。首先,根据仓储货物的外形特征,将其分为包装箱与包装袋两大类,形成训练数据集;其次,将骨干网络更换为具有更小模型尺寸的MobileNetV3,加快推理;再次,添加SE注意力机制模块,旨在提高模型的检测精度;最后,结合α_CIoU损失函数,增强模型的灵活度。通过实验验证,改进后的算法相较于原始算法在精确率(Precision,P)、平均类别精度(mean Average precision,mAP)和帧率(Frames per second,FPS)三方面分别提升2.1%、0.5%和10.6%,能够高效地完成对仓储货物的预分类工作。

基于golang微服务的库房智能管理系统设计

针对传统库房管理主要依赖人工、效率低、数据难以及时更新、人力成本高等问题,文中基于golang微服务,结合ZigBee、RFID、人工智能和区块链等技术设计一种新型仓库智能管理系统。该系统主要由综合信息处理控制系统、以ZigBee为主的信息交互系统、以STM32与人脸识别为核心的门禁控制系统、RFID读写设备、后台管理五个部分构成,采用分布式架构将仓库设备管理、数据采集与处理和任务决策整合为一体,既能便捷联动ZigBee、RFID及人脸识别,又具有极强的内存管理和跨平台性。应用系统基于golang语言开发,使用vue、uniapp、electron、php、tdsql-c等技术,采用B/S和C/S两种架构;后端服务采用go-micro微服务形式来提高系统可插拔性和高并发能力;为提高租借数据的可靠性,系统采用区块链对其进行加密,实现每条数据自动上链功能。数据库采用tdsql-c云原生数据库,以方便横向扩展。实验表明,该系统能高效实现盘点和更新、全程无纸化记录及影像和出库数据留存,确保资料溯源,信息化水平高,极大地降低了企业运营成本。

地震作用下散粮楼房仓仓壁动态侧压力分析

地震作用下贮料对仓壁产生的动态侧压力是影响散粮楼房仓结构安全的一个重要因素。为此,该研究设计制作了缩尺比例为1:25的三层楼房仓试验模型,进行了3条地震波下不同加载等级的振动台试验,分析获得了贮料地震反应特性、仓壁动态侧压力变化规律及超压系数,提出了仓壁动态侧压力计算方法。研究表明:不同楼层同一高度处仓壁动态侧压力达到峰值的时间不同步,沿楼房仓高度方向逐步滞后,且其均滞后于对应楼层仓壁加速度峰值时刻;各楼层仓壁动态侧压力逐层增大,每升高一层,增大约29%,且同一楼层的仓壁动态侧压力沿高度逐渐增大,仓壁上部、中部动态侧压力分别是下部的2.5倍、1.4倍;一、二、三层的超压系数最大值依次为2.9、3.4、4.1,且均位于每层仓壁上部位置;通过试验结果验证了所提出的仓壁动态侧压力计算方法的有效性;散粮楼房仓结构抗震设计,应考虑仓壁动态侧压力的影响与不同楼层的差异性。研究成果可为散粮楼房仓的抗震设计提供参考。

基于零库存目标的体外诊断试剂供应链质量评价与改进研究

目的:制定体外诊断试剂供应链的零库存管理目标,评价供应链质量,改进试剂供应中存在的问题。方法:从体外诊断试剂的库存量、供应效率和产品质量方面分析体外诊断试剂管理中存在的问题,制定医院运营、管理质量和患者利益最优化的管理体系,构建零库存管理路径和质量评价模型。选取2020年1月至2023年3月江苏省苏北人民医院采购的21种类型85个型号的体外诊断试剂,根据供应链质量管理方式不同,分别采用按需库存管理模式(模式1)和零库存管理模式(模式2),对比两种管理模式的需求采购、库存管理和临床使用效果。结果:采用模式2管理方式的体外诊断试剂的采购需求达标率、供应能力优质率和临床使用匹配度分别为(93.35±3.62)%、(94.87±2.63)%和(96.08±2.31)%,均高于模式1,其差异有统计学意义(Z=2.489、2.836、2.838,P<0.05),存产品长期积压、环境不达标和信息不及时的例数分别为(2.92±2.54)例、(2.83±1.59)例和(5.58±3.12)例,均低于模式1,其差异有统计学意义(Z=2.959、3.037、3.703,P<0.05);临床科室、医技科室及采购中心对模式2的体外诊断试剂的供应配送和信息沟通的满意度分别为97.8%和93.3%、97.0%和87.9%、100%和84.6%,均高于模式1,其差异有统计学意义(χ^(2)_(临床科室)=5.428、6.133,χ^(2)_(医技科室)=3.958、3.937,χ^(2)_(采购中心)=5.159、4.996,P<0.05)。结论:零库存管理模式能够提高体外诊断试剂需求采购的规范性,降低库存管理中积压失效问题的发生率,提升临床供应服务质量。

基于CA-RL的堆石坝填筑仓面碾压作业动态路径规划

碾压作业是堆石坝填筑仓面施工的关键工序,科学合理规划碾压路径能够在保障碾压质量的前提下提高碾压效率。在当前路径规划研究基础上进一步考虑碾压机数量变化、压实质量感知等动态要素可以提高路径规划模型面对复杂动态的仓面施工环境的适应性。本文通过建立基于元胞自动机的填筑仓面信息模型、提出条带整体压实质量评价方法,解决压实质量等仓面信息的储存更新的问题;通过建立基于强化学习的碾压作业路径规划模型、构建碾压机状态集和动作集、设计奖励函数和探索利用策略,解决碾压机数量变化的路径分配的问题;耦合上述两种模型,实现堆石坝填筑仓面碾压作业动态路径规划。结合国内某堆石坝工程开展了工程应用,结果表明:本方法可动态考虑碾压机数量变化、压实质量感知等要素,路径规划结果在保障碾压质量的前提下路径长度相较现场施工实际路径平均缩短22.3%,能够有效提升碾压效率。

河北省应急物资储备库选址与管理研究

科学的应急物资储备库选址是影响应急物资配送时效和突发公共事件处理效率的关键因素。文章在分析河北省应急物资储备选址问题的基础上,构建集合覆盖模型,并利用启发式算法完成求解。根据模型求解结果,河北省应在保定和廊坊两地建立应急物资储备库,以便能够保证在应急事件发生后6小时内将应急物资运至省内各处。同时,进一步提出了河北省应急物资仓储管理对策,旨在应急灾害发生时能够全面保障河北省的应急物资管理需求。

小程序结合目视管理在心外科病区库房管理中的应用效果分析

目的:分析小程序结合目视管理在心外科病区库房管理中的应用效果。方法:回顾性分析2020年1—12月广东省中医院心外科病区库房管理数据,2020年1—6月为实施前,采用常规库房管理,2020年7—12月为实施后,在常规库房管理基础上联合小程序结合目视管理。比较实施前后库房管理工作效率、缺货告知次数、物资积压过期数、护理人员满意度。结果:实施后查找物品时间、盘点时间、整理库房时间短于实施前,差异有统计学意义(P<0.05)。实施后缺货告知次数、物资积压过期数少于实施前。实施后护理人员在物品充足、方便寻找、摆放布局好、管理分配合理的满意度高于实施前,差异有统计学意义(P<0.05)。结论:小程序结合目视管理在心外科病区库房管理中的应用效果显著,可提高库房管理工作效率,减少物资缺货、过期数,提高护理人员满意度。

基于集成改进蚁群算法的作战环推荐方法

作战环推荐是依靠优化算法从作战网络中为指挥员推荐最优的作战环,以对目标形成高质量打击。未来作战中的作战环推荐面临体系规模大、决策节奏快的特点。对此,提出了一种集成改进的蚁群算法,能够实现高效、高质的作战环推荐优化求解。首先,将作战环推荐问题转换为一种基于多仓库路径规划的数学模型。然后,针对原始蚁群算法前期收敛速度慢、算法参数对结果影响大和容易陷入局部最优的问题分别提出了3种改进策略:基于边权重信息的信息素初始化、基于差分进化的蚁群算法参数自适应优化和基于遗传算子的全局搜索能力提升,并进行了集成改进。最后,在案例分析中对集成改进蚁群算法进行了分析和对比,验证了所提算法在不需要大幅提高耗时的情况下,优化结果要优于未集成改进的蚁群算法,且相比于原始蚁群算法提升效果显著。

生鲜电商前置仓选址因素分析与模型构建研究

前置仓作为生鲜电商企业满足顾客多样化需求的一种新型仓配模式,如选址不合理会导致配送成本过高、生鲜质量降低和时效性不足。如何进行合理选址和评估其各类成本收益,备受学术界关注。通过构建中心仓、前置仓以及需求点的三级网络模型,利用混合整数线性规划,并考虑了运输成本、运营成本、制冷成本以及货损成本,构建以综合总成本最小为目标函数的数学模型,再结合0~1变量的参数约束,借助善于解决线性规划问题的LINGO软件解决网络中前置仓的选址问题,并用郑州市中牟县的相关数据进行实例分析与计算。通过研究发现,前置仓建模中需要考虑的项目为运输成本和履约成本。期望为生鲜电商前置仓选址研究提供更优的前置仓选址模型。

物流园区业务整合视角下物流业集群升级研究

物流园区是物流业集群化发展的产物,是推进物流业升级的重要载体。以物流园区为研究对象,聚焦园区之间的业务整合问题,从专线运输、仓储配送和货运订单三种业务整合视角,分析归纳了物流园区业务整合推进物流业集群升级的方式。其中,专线运输视角归纳为主体企业整合和会员制整合两种方式,仓储配送视角归纳为物流业务整合和设施设备整合两种方式,货运订单视角归纳为横向整合和纵向整合两种方式。在介绍每种方式时,通过物流园区案例研究和探讨,归纳总结了我国目前各地以物流园区业务整合推进物流业集群升级的一些经验和做法。业务整合是近年来物流园区发展的重要方向,对推进物流业升级具有一定的借鉴价值。

多搬运任务下考虑碰撞避免的AGV路径规划

为提升自动导引小车在“货到人”仓库中的运行效率,针对AGV-托盘任务分配、单AGV路径规划及多AGV碰撞避免三个子问题的研究,以最小化AGV行驶距离为目标构建数学模型。首先,根据AGV与托盘的双边匹配问题特点设计改进的匈牙利算法求解匹配结果。其次,提出一种二维编码机制的改进遗传算法(improved genetic algorithm,IGA),采用一种局部搜索算子代替原变异操作,在提高算法搜索性能的基础上使其成功应用于单AGV路径规划问题。然后,利用时空数据设计一种三维网格冲突检测方法,并根据商品SKU数量设定AGV的优先级以降低多AGV执行任务时的碰撞概率。最后,在32 m×22 m的仓库中针对不考虑碰撞与考虑碰撞两种情形进行AGV路径优化分析,给出合理的行驶距离和碰撞次数。IGA与标准遗传算法的对比结果显示,IGA能够在合理的时间内获得更高质量的解,行驶距离减少约1.74%,算法求解时间缩短约37.07%。此外,针对AGV数量灵敏度分析,在不同目标托盘规模下测试不同数量的AGV对行驶距离和碰撞次数的影响,发现14~16台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。

基于RFID的造纸行业仓库系统设计

针对传统物资仓储系统中存在的效率低下、操作烦琐等问题,提出一种基于RFID技术的物资仓储系统设计方案。通过引入RFID标签和读写器,可实现物资仓储信息的无线自动采集和实时监控,大幅度提高仓库管理效率。基于此,介绍RFID技术的基本原理和特点,详细阐述基于RFID技术的物资仓储系统的设计框架,通过对比传统物资仓储系统和基于RFID技术的物资仓储系统的实验结果,验证了该方案的有效性。实验结果表明,基于RFID技术的物资仓储系统能够大大提高仓库管理效率,减少人工操作错误,实现物资的自动识别,提高仓库管理的准确性,为企业降低成本、提高竞争力提供有效的手段。

全孔板通风系统储粮调质的模拟研究

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