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.

Intelligent logistics system of steel bar warehouse based on ubiquitous information

Internet of Things and artificial intelligence technology are the key elements of the intelligent construction of iron and steel production warehouse. This paper puts forward a whole set of intelligent scheme for bar warehouse crane for the guidance of metallurgical process engineering, including cluster rapid self-awareness technology of the smart crane, precise self-executing technique of crane with rigid-flexible hybrid structure, multi-body system kinematics model of the smart crane sling and the swing characteristics model at different azimuth, antiswing control technology based on the optimization objective function, the vehicle model recognition system based on lidar, and the clustering crane dynamic scheduling method based on multi-agent reinforcement learning. The complete intelligent logistics system of the bar warehouse has changed the original operation mode of the warehouse area and realized the unmanned operation and intelligent scheduling of the crane,which is of great significance for improving the production efficiency, reducing the production cost, and improving the product quality.

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.

Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma:Challenges and opportunities

Hepatocellular carcinoma(HCC)constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths.Currently,treatment selection is based on the stage of the disease.Emerging fields such as three-dimensional(3D)printing,3D bioprinting,artificial intelligence(AI),and machine learning(ML)could lead to evidence-based,individualized management of HCC.In this review,we comprehensively report the current applications of 3D printing,3D bioprinting,and AI/ML-based models in HCC management;we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them,and finally,we discuss the opportunities that arise from these applications.Notably,regarding 3D printing and bioprinting-related challenges,we elaborate on cost and cost-effectiveness,cell sourcing,cell viability,safety,accessibility,regulation,and legal and ethical concerns.Similarly,regarding AI/ML-related challenges,we elaborate on intellectual property,liability,intrinsic biases,data protection,cybersecurity,ethical challenges,and transparency.Our findings show that AI and 3D printing applications in HCC management and healthcare,in general,are steadily expanding;thus,these technologies will be integrated into the clinical setting sooner or later.Therefore,we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Three-dimensional visualization and virtual reality simulation role in hepatic surgery:Further research warranted

Artificial intelligence(AI)is the study of algorithms that enable machines to analyze and execute cognitive activities including problem solving,object and word recognition,reduce the inevitable errors to improve the diagnostic accuracy,and decision-making.Hepatobiliary procedures are technically complex and the use of AI in perioperative management can improve patient outcomes as discussed below.Three-dimensional(3D)reconstruction of images obtained via ultrasound,computed tomography scan or magnetic resonance imaging,can help surgeons better visualize the surgical sites with added depth perception.Preoperative 3D planning is associated with lesser operative time and intraoperative complications.Also,a more accurate assessment is noted,which leads to fewer operative complications.Images can be converted into physical models with 3D printing technology,which can be of educational value to students and trainees.3D images can be combined to provide 3D visualization,which is used for preoperative navigation,allowing for more precise localization of tumors and vessels.Nevertheless,AI enables surgeons to provide better,personalized care for each patient.

Warehouse Environment Parameter Monitoring System and Sensor Error Correction Model Based on PSO-BP

The warehouse environment parameter monitoring system is designed to avoid the networking and high cost of traditional monitoring system.A sensor error correction model which combines particle swarm optimization(PSO)with back propagation(BP)neural network algorithm is established to reduce nonlinear characteristics and improve test accuracy of the system.Simulation and experiments indicate that the PSO-BP neural network algorithm has advantages of fast convergence rate and high diagnostic accuracy.The monitoring system can provide higher measurement precision,lower power consume,stable network data communication and fault diagnoses function.The system has been applied to monitoring environment parameter of warehouse,special vehicles and ships,etc.

Glimpse into the future of prosthodontics:The synergy of artificial intelligence

Prosthodontics,deals in the restoration and replacement of missing and structurally compromised teeth,this field has been remarkably transformed in the last two decades.Through the integration of digital imaging and threedimensional printing,prosthodontics has evolved to provide more durable,precise,and patient-centric outcome.However,as we stand at the convergence of technology and healthcare,a new era is emerging,one that holds immense promise for the field and that is artificial intelligence(AI).In this paper,we explored the fascinating challenges and prospects associated with the future of prosthodontics in the era of AI.

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。

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。

智能魔方仓库出入库作业虚拟仿真实验系统建设

智能魔方仓库具有存储密度大、集成度高等优点,在小件订单拣选领域发展迅速,但因其特殊的堆存形式使得出入库较之传统仓库多出一个翻箱的环节。为了直观展示出入库作业过程,在研究出入库、翻箱规则及机器小车调度策略的基础上,基于Unity 3D开发了智能魔方仓库出入库作业虚拟仿真实验系统。学生通过交互操作实现不同仓库规模、存储水平及订单数的出入库调度,对智能魔方仓库运作有更深切的认识。该系统可为仓储系统、机器小车调度等课程开展提供虚拟仿真实验环境,也是对现有仓储仿真软件的有效拓展。

智能仓储交通信号与多AGV路径规划协同控制方法

针对智能仓储多AGV(automated guided vehicle)路径规划实时性差,障碍物识别能力弱,多AGV碰撞、死锁和拥堵等问题,提出了一种智能仓储交通信号控制与多AGV路径规划协同控制方法,将交通信号与多AGV路径规划视为一个整体,设计一种交通信号与多AGV路径规划协同控制框架,并提出LS-A3C(long short-asynchronous advantage actor-critic)算法和Bi-LSTM-CBAM(bi-long short-term memory-convolutional block attention module)算法作为框架的核心算法。LS-A3C算法使用长短时编码器和注意力机制分别对交通信号的长期信息和短期信息进行编码,以学习元特征表示,并使用A3C框架计算元Q值和控制策略,实现交通信号时间自适应AGV流量,解决多AGV碰撞、死锁和拥堵等问题。Bi-LSTM-CBAM算法通过计算本时刻和前置时刻状态特征,对输出结果进行拼接处理,可以有效解决神经网络梯度消失和爆炸的问题,提高AGV路径规划实时性;引入注意力机制模块CBAM,根据输入信息重要程度分配权重,加强AGV对障碍物识别能力。在Sumo和Gazebo联合仿真平台进行仿真实验,实验结果表明,该协同控制方法使AGV碰撞、死锁及拥堵情况明显降低,障碍物识别能力显著提高,路径规划实时性大幅增强,达到提升AGV作业效率的目的。

有轨制导车堆垛的动态稳定性研究

有轨制导车(RGV)堆垛是智能仓储的关键一环,运动时倒塌不仅会降低工作效率,还可能带来货物损坏、轨道变形等安全隐患。为提高智能仓储系统的工作效率,从箱子位置和刚体受力的角度,提出一种RGV堆垛的可承载加速度(即不使任何箱子倒塌的最大加速度值)计算方法。首先,根据加速度方向和箱子的位置,将堆垛中箱子划分为多个刚体,每个刚体包含会一起倒塌的多个箱子;然后,根据堆垛中刚体的位置构建刚体树,分析刚体之间的通过树传递的阻倒力;最后根据阻倒力计算RGV堆垛的可承载加速度。提出的可承载加速度不仅可用于限制RGV运动加速度,还可用于定量评估堆垛的动态稳定性。实验结果表明,可承载加速度计算结果与Unity仿真堆垛的动态稳定性之间,呈现皮尔逊相关系数>0.8的极强线性相关性,优于现有的堆垛动态稳定性评估方法。

人工智能技术在智慧物流的应用研究

随着科技的发展,人工智能技术已经深入各个领域,智慧物流作为物流行业的一种新型模式,正面临着技术升级的挑战。人工智能技术的应用,如数据分析、自动化、物联网、机器学习和深度学习等,为智慧物流的发展提供了新的机遇。文章探讨了人工智能技术在智慧物流中的应用,包括数据分析、自动化调度、物联网和传感技术,以及机器学习和深度学习,分析了这些技术在智慧物流应用中面临的挑战,并提出了相应的优化建议,以推动智慧物流的发展。

基于互联网的物资仓库运行系统设计

随着科技的快速发展,智慧物流创新模式迅速发展,逐渐成为工业应用的核心内容。进行了基于互联网的物资仓库运行控制系统设计,利用IOA仿真软件,在软件中搭建总体场景,并在其中进行运动仿真演示。经过仿真调试,能够很好地实现对物品的运输、识别、分拣和出入库处理,已经达到任务标准,在实际工程应用与实验教学中有着广泛的应用前景。

智慧物联网新技术在智慧仓建设中的应用研究

随着物联网和人工智能技术的不断发展,智慧物联网新技术在智慧仓储建设中的应用越来越广泛。智慧仓储作为物流行业的重要组成部分,对提高物流效率和服务质量具有重要意义。智慧物联网新技术的应用,可以为智慧仓储建设提供更加智能化、高效化的解决方案,进一步提高仓储管理的效率和准确性。文章旨在探讨智慧物联网新技术在智慧仓储建设中的应用,以期为相关领域的研究和实践提供一定的参考。

数字化转型背景下的智慧建筑设计探究

从建筑数字化发展要求出发,结合项目实践,探讨智慧建筑设计的理念变化、技术关键和发展趋势,阐述智慧建筑的特征及以数据为核心的设计路径。

面向冷链的四向穿梭车关键结构设计与可靠性分析

为了增强我国冷链智能化储存设备的科技实力,从四向穿梭车的机械系统设计、机械结构优化分析、样机试制与试验3个方面详细阐述了四向穿梭车总体设计。在样机试验中,该穿梭车较好地实现了X/Y向行走、换向、存取货物的功能,其机械结构能很好地适应冷链物资自动化仓储系统的搬运需求。

涤纶成品物流效率的影响因素及提升举措的探讨

仓储作为影响涤纶成品物流效率的重要组成部分,其智能化、高效化对物流成本和出货效率作用日益凸显。现今,涤纶产能扩大,客户订单复杂多样,单一、低效的仓储管理系统已无法满足工厂日常运营需求。为了进一步提高成品物流效率,探讨了一种基于仓库管理系统(WMS)、自动化立体库监控系统(WCS)以及电子订货系统(EOS)等多系统交互集成的智能化、高效化出货管理系统。

铜冶炼企业中原料库智能天车的功能需求及关键技术探析

铜冶炼行业中原料的搬运大多依靠人工驾驶天车进行,员工劳动强度大,天车运行效率不高,信息及现场管理原始粗放,急需借助智能化手段优化原有作业模式,以减轻员工劳动强度,提高生产效率,改善工作环境,实现无人化管理。针对以上问题及优化目标,对多品种物料原料库智能天车的软硬件功能需求以及精准定位技术、吊具防摇摆等关键技术进行探析,为后续深入设计和实施提供技术支撑。