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.

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.

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.

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.

An Application of a Multi-Tier Data Warehouse in Oil and Gas Drilling Information Management

Expenditure on wells constitute a significant part of the operational costs for a petroleum enterprise, where most of the cost results from drilling. This has prompted drilling departments to continuously look for ways to reduce their drilling costs and be as efficient as possible. A system called the Drilling Comprehensive Information Management and Application System (DCIMAS) is developed and presented here, with an aim at collecting, storing and making full use of the valuable well data and information relating to all drilling activities and operations. The DCIMAS comprises three main parts, including a data collection and transmission system, a data warehouse (DW) management system, and an integrated platform of core applications. With the support of the application platform, the DW management system is introduced, whereby the operation data are captured at well sites and transmitted electronically to a data warehouse via transmission equipment and ETL (extract, transformation and load) tools. With the high quality of the data guaranteed, our central task is to make the best use of the operation data and information for drilling analysis and to provide further information to guide later production stages. Applications have been developed and integrated on a uniform platform to interface directly with different layers of the multi-tier DW. Now, engineers in every department spend less time on data handling and more time on applying technology in their real work with the system.

基于深度学习的物流跟踪管理

利用先进的人工智能和计算机视觉技术,物流管理取得了重大进展。如何建立一套能够有效解决物体遮挡、运动模糊、目标相似等实际问题的检测技术,是一个重要的挑战。文章提出了一种基于YOLOv8和Deep-SORT的方法来跟踪货物位置。该系统可以有效地识别、定位、跟踪和计数镜头前的货物。称之为“warehouse management”,该算法基于示例跟踪范式,并将跟踪应用于检测对象的边界框。在此基础上,自动识别感兴趣区域(ROI),有效消除不需要物体。我们的F1的分数是0.816 7。

Uniform Representation Model for Metadata of Data Warehouse

A uniform metadata representation is introduced for heterogeneous databases, multi media information and other information sources. Some features about metadata are analyzed. The limitation of existing metadata model is compared with the new one. The metadata model is described in XML which is fit for metadata denotation and exchange. The well structured data, semi structured data and those exterior file data without structure are described in the metadata model. The model provides feasibility and extensibility for constructing uniform metadata model of data warehouse.

CONSTRUCTION OF SIMULATION SYSTEM AND ITS IMPLEMENTATION FOR JOB DISPATCH OF INPROCESS WAREHOUSE ON FLEXIBLE PRODUCTION LINE

The constructing method of a simulation system is discussed in this paper. It is for a Decision Support System (DSS) of main in-process warehouse on a large scale flexible production line. This system is decomposed into three function blocks: DSS, support environment for simulation, simulating dispatch module. It has a fine structure and works coordinatively to complete whatever assignment for simulation tasks of a complicated production system.

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.

Model Warehouse

This paper puts forward a new conception:model warehouse,analyzes the reason why model warehouse appears and introduces the characteristics and architecture of model warehouse.Last,this paper points out that model warehouse is an important part of WebGIS.

Data Warehouse Design for Big Data in Academia

This paper describes the process of design and construction of a data warehouse(“DW”)for an online learning platform using three prominent technologies,Microsoft SQL Server,MongoDB and Apache Hive.The three systems are evaluated for corpus construction and descriptive analytics.The case also demonstrates the value of evidence-centered design principles for data warehouse design that is sustainable enough to adapt to the demands of handling big data in a variety of contexts.Additionally,the paper addresses maintainability-performance tradeoff,storage considerations and accessibility of big data corpora.In this NSF-sponsored work,the data were processed,transformed,and stored in the three versions of a data warehouse in search for a better performing and more suitable platform.The data warehouse engines-a relational database,a No-SQL database,and a big data technology for parallel computations-were subjected to principled analysis.Design,construction and evaluation of a data warehouse were scrutinized to find improved ways of storing,organizing and extracting information.The work also examines building corpora,performing ad-hoc extractions,and ensuring confidentiality.It was found that Apache Hive demonstrated the best processing time followed by SQL Server and MongoDB.In the aspect of analytical queries,the SQL Server was a top performer followed by MongoDB and Hive.This paper also discusses a novel process for render students anonymity complying with Family Educational Rights and Privacy Act regulations.Five phases for DW design are recommended:1)Establishing goals at the outset based on Evidence-Centered Design principles;2)Recognizing the unique demands of student data and use;3)Adopting a model that integrates cost with technical considerations;4)Designing a comparative database and 5)Planning for a DW design that is sustainable.Recommendations for future research include attempting DW design in contexts involving larger data sets,more refined operations,and ensuring attention is paid to sustainability of operations.

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.

Development of Geological Data Warehouse

Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in different levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis always needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.

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.

Site selection of emergency material warehouse under fuzzy environment

The objective of this work was to determine the location of emergency material warehouses. For the site selection problem of emergency material warehouses, the triangular fuzzy numbers are respectively demand of the demand node, the distance between the warehouse and demand node and the cost of the warehouse, a bi-objective programming model was established with minimum total cost of the system and minimum distance between the selected emergency material warehouses and the demand node. Using the theories of fuzzy numbers, the fuzzy programming model was transformed into a determinate bi-objective mixed integer programming model and a heuristic algorithm for this model was designed. Then, the algorithm was proven to be feasible and effective through a numerical example. Analysis results show that the location of emergency material warehouse depends heavily on the values of degree a and weight wl. Accurate information of a certain emergency activity should be collected before making the decision.

Ant Colony System for a Fuzzy Adjacent Multiple-Level Warehouse Layout Problem

A warehouse layout problem where the warehouse has more than one level and both the distance from the cell to the receive/exit bay and demand of item types are fuzzy variables is proposed. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A fuzzy expected value model is given and an ant colony system is designed to solve the problem. Computational results indicate the efficiency and effectiveness of the method.

Task Assignment Problem of Robots in a Smart Warehouse Environment

The task assignment problem of robots in a smart warehouse environment （TARSWE） based on cargo-to-person is investigated. Firstly, the sites of warehouse robots and the order picking tasks are given and the task assignment problem for picking one order is formulated into a mathematical model to minimize the total operation cost. Then a heuristic algorithm is designed to solve the task assignment problem for picking multiple orders. Finally, simulations are done by using the orders data of online bookstore A. The results show that using the heuristic algorithm of this paper to assign robots, the cost was reduced by 2% and it can effectively avoid far route and unbalanced workload of robots. The feasibility and validity of the model and algorithm are verified. The model and algorithm in this paper provide a theoretical basis to solve the TARSWE.

Design and Realization of Automatic Warehouse Based on S7-1500PLC

In order to improve the efficiency of automatic warehouse control system,the experimental platform of stereoscopic warehouse with s7-1500plc is designed.The manipulator is driven by stepper motor and servo motor to realize x,y and Z three-axis space motion.The material transmission system is built by general-purpose G120 inverter.HMI KTP700 realizes control and status monitoring.The materials are identified and classified by RFID sensor and other sensors.TIAV15 software build PROFINET com­munication and PROFIBUS communication network.Using the GRAPH language programming can improve the visualization degree of application and solve the complex problems of program design and debugging of the warehouse control system.Through the design of hardware and software,a set of complete control system design scheme is formed,which has high practical value and provides an excellent teaching and experiment platform for the intelligent storage system.

Secondary Warehouse Management System of Medical Low Value Consumables under SPD Mode and its Working Method

Objective:To study the secondary warehouse management system and operation mode of medical low value consumables under SPD mode.Methods:From August 2018 to August 2019,the management mode of the secondary warehouse management system of medical low value consumables was selected.According to the management method,it can be divided into the observation group and the control group.The relevant data of the observation group was based on SPD mode,and the data of the control group was based on the traditional mode.According to the improvement of the two groups of data management effect,data integration and analysis are carried out to judge the effectiveness of the SPD mode of medical low value consumables secondary warehouse management system and its working mode.Results:During the analysis of the two groups of data,the relevant data were analyzed.The data validity of the observation group was higher(94.0%),and the differences of the limitations and problems in the management process had more advantages,while the relevant data corresponding to the two groups of data had significant differences(P<0.05).Conclusion:According to the current research results,Based on the collection,storage and central management of data electronic medical record information,the secondary warehouse management system of medical low value consumables under SPD mode is connected with clinical information system and management information system,which realizes the unification,resource integration and efficient operation of different business systems in the hospital,so as to help improve the quality and efficiency of medical care,prevent and reduce medical errors,Control and reduce medical expenses.

ROLE OF META-MODEL IN ENGINEERING DATA WAREHOUSE

Engineering data are separately organized and their schemas are increasingly complex and variable. Engineering data management systems are needed to be able to manage the unified data and to be both customizable and extensible. The design of the systems is heavily dependent on the flexibility and self-description of the data model. The characteristics of engineering data and their management facts are analyzed. Then engineering data warehouse (EDW) architecture and multi-layer metamodels are presented. Also an approach to manage anduse engineering data by a meta object is proposed. Finally, an application flight test EDW system (FTEDWS) is described and meta-objects to manage engineering data in the data warehouse are used. It shows that adopting a meta-modeling approach provides a support for interchangeability and a sufficiently flexible environment in which the system evolution and the reusability can be handled.