MAQMC:Multi-Agent Deep Q-Network for Multi-Zone Residential HVAC Control

The optimization of multi-zone residential heating,ventilation,and air conditioning(HVAC)control is not an easy task due to its complex dynamic thermal model and the uncertainty of occupant-driven cooling loads.Deep reinforcement learning(DRL)methods have recently been proposed to address the HVAC control problem.However,the application of single-agent DRL formulti-zone residential HVAC controlmay lead to non-convergence or slow convergence.In this paper,we propose MAQMC(Multi-Agent deep Q-network for multi-zone residential HVAC Control)to address this challenge with the goal of minimizing energy consumption while maintaining occupants’thermal comfort.MAQMC is divided into MAQMC2(MAQMC with two agents:one agent controls the temperature of each zone,and the other agent controls the humidity of each zone)and MAQMC3(MAQMC with three agents:three agents control the temperature and humidity of three zones,respectively).The experimental results showthatMAQMC3 can reduce energy consumption by 6.27%andMAQMC2 by 3.73%compared with the fixed point;compared with the rule-based,MAQMC3 andMAQMC2 respectively can reduce 61.89%and 59.07%comfort violation.In addition,experiments with different regional weather data demonstrate that the well-trained MAQMC RL agents have the robustness and adaptability to unknown environments.

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.

Effect of Hydrogen Addition on Methane HCCI Engine Ignition Timing and Emissions Using a Multi-zone Model

Ignition timing control is of great importance in homogeneous charge compression ignition engines. The effect of hydrogen addition on methane combustion was investigated using a CHEMKIN multi-zone model. Results show that hydrogen addition advances ignition tim- ing and enhances peak pressure and temperature. A brief analysis of chemical kinetics of methane blending hydrogen is also performed in order to investigate the scope of its appli- cation, and the analysis suggests that OH radical plays an important role in the oxidation. Hydrogen addition increases NOx while decreasing HC and CO emissions. Exhaust gas recir- culation （EGR） also advances ignition timing; however, its effects on emissions are generally the opposite. By adjusting the hydrogen addition and EGR rate, the ignition timing can be regulated with a low emission level. Investigation into zones suggests that NOx is mostly formed in core zones while HC and CO mostly originate in the crevice and the quench layer.

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。

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。

杭州国家版本馆恒温恒湿空调系统设计

介绍了恒温恒湿库房空调系统的设计,提出了可在库房建成初期、运行稳定期、战时等灵活切换的3种运行模式,分析了其各自的处理过程与节能性;给出了平、战时空调系统选择方案,提出了平时回收空气源热泵机组和冷水机组的冷凝热作为再热热源,战时利用新风预冷实现温湿度独立控制的方法;利用CFD模拟对室内气流组织进行了优化设计;提出了采用含湿量控制代替定露点法进行湿度控制和风机变频等控制策略。通过调试与运行,证明了各项设计措施在项目中应用的有效性。

基于ISO-TW算法的多仓储机器人路径规划研究

针对当前智能仓库中多仓储机器人协同规划效率低下、动态性不足等问题,提出了一种融合改进蛇优化(Improved Snake Optimizer,ISO)算法和时间窗(Time Window,TW)模型的路径规划方法。首先,在静态规划阶段,利用考虑阻塞因素的改进蛇优化算法为多仓储机器人规划出全局最优路径,同时计算出各机器人的时间窗,从而提升仓储机器人的规划效率;其次,在动态规划阶段,通过建立的多仓储机器人时间窗模型并引入动态调节策略,以消解冲突提升算法的动态性能;最后,进行仿真及实验。仿真结果表明,ISO-TW算法相较其他算法在阻塞点数量上最多可减少27~43个,减少幅度达22.50%~31.62%(机器人规模为80个);重规划路径比重可降低39.82%~41.05%(机器人规模为40个);平均运行时长最多可缩短38~40 s,缩短幅度约为18.27%~19.05%(机器人规模为60个)。实验结果表明,ISO-TW算法较其他算法平均运行时长减少8.53%~9.23%,冲突次数减少11.63%~15.56%,能够在真实场景中实现多仓储机器人的高效协同规划。

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。

跨境电商退货逆向物流选择和策略

跨境电商是数字贸易的重要组成部分,也是一种新质生产力。在跨境电商迅猛发展的同时,退货逆向物流问题也日益严重。如何实现退货逆向物流降本增效,实现绿色逆向物流发展是跨境电商可持续发展的关键。本文通过对比分析法,将传统国际贸易成本分解方法融入跨境电商逆向物流研究,比较传统海外仓逆向物流退货模式和退运进境中心仓逆向物流模式,分析其流程和优缺点,并从预防场景角度、成本角度、效率角度、社会责任可持续发展角度阐述逆向物流创新解决策略。研究发现企业选择进境保税区中心仓的逆向物流方案,能实现最大化物品价值,提升商品资源利用率,减少海外废弃物品,强化企业社会责任,并践行了绿色可持续发展理念。通过提升平台、政府、企业数字化供应链赋能水平、借力AI精准预测分析、加强平台认证机制、构建逆向物流标准方案、创新简化通关模式、引导绿色发展理念以及完善跨境电商质量认证体系等多项措施,最终实现跨境电商逆向物流的高质量发展机制。

嵌入式冰箱压缩机仓散热优化研究

针对嵌入式冰箱压缩机仓散热效果不佳的问题,采用数值模拟与试验结合的方法对其散热进行研究。模拟了嵌入式冰箱压缩机仓的散热过程,结果显示冰箱背部气流未经过充分散热再次进入到压缩机仓形成短路气流,且由于橱柜的影响进入压缩机仓内的总气流相较于自由状态下减少了51.78%;针对散热效果差的原因提出了3种不同适用场景的优化方案,并对各方案在通风量、能耗、制冷性能方面进行对比研究。结果表明,3种优化方案均能改善散热效果,但各有优劣,优化方案的总风量比原嵌入方案分别提高了3.9%,75.3%,55.3%;在32℃和16℃的环境温度下,3种优化方案的能耗相较于原嵌入方案分别降低了6.50%和3.90%,23.03%和25.07%,17.70%和17.50%。研究结果为不同适用场景下的嵌入式冰箱压缩机仓散热优化提供了解决思路和参考。