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 function...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.展开更多
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 pr...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.展开更多
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 undesir...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.展开更多
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 ...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.展开更多
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,...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.展开更多
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 ...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.展开更多
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 ...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.展开更多
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 lay...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.展开更多
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)w...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.展开更多
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 war...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.展开更多
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 frequen...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.展开更多
Since 1990s,the spatial data warehouse technology has rapidly been developing, but due to the complexity of multi-dimensional analysis, extensive application of the spatial data warehouse technology is affected. In th...Since 1990s,the spatial data warehouse technology has rapidly been developing, but due to the complexity of multi-dimensional analysis, extensive application of the spatial data warehouse technology is affected. In the light of the characteristics of the flood control and disaster mitigation in the Yangtze river basin, it is proposed to design a scheme about the subjects and data distribution of the spatial data warehouse of the flood control and disaster mitigation in Yangtze river basin, i.e., to adopt a distributed scheme. The creation and development of the spatial data warehouse of the flood control and disaster mitigation in Yangtze river basin is presented .The necessity and urgency of establishing the spatial data warehouse is expounded from the viewpoint of the present situation being short of available information for the flood control and disaster mitigation in Yangtze river basin.展开更多
On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them an...On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).展开更多
To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maint...To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.展开更多
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 ma...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.展开更多
Objective: To establish an interactive management model for community-oriented high-risk osteoporosis in conjunction with a rural community health service center. Materials and Methods: Toward multidimensional analysi...Objective: To establish an interactive management model for community-oriented high-risk osteoporosis in conjunction with a rural community health service center. Materials and Methods: Toward multidimensional analysis of data, the system we developed combines basic principles of data warehouse technology oriented to the needs of community health services. This paper introduces the steps we took in constructing the data warehouse;the case presented here is that of a district community health management information system in Changshu, Jiangsu Province, China. For our data warehouse, we chose the MySQL 4.5 relational database, the Browser/Server, (B/S) model, and hypertext preprocessor as the development tools. Results: The system allowed online analysis processing and next-stage work preparation, and provided a platform for data management, data query, online analysis, etc., in community health service center, specialist outpatient for osteoporosis, and health administration sectors. Conclusion: The users of remote management system and data warehouse can include community health service centers, osteoporosis departments of hospitals, and health administration departments;provide reference for policymaking of health administrators, residents’ health information, and intervention suggestions for general practitioners in community health service centers, patients’ follow-up information for osteoporosis specialists in general hospitals.展开更多
In the smart warehousing system adopting cargo-to-person mode, all the items are stored in the movable shelves. There are some warehouse robots transporting the shelves to the working platforms for completing order pi...In the smart warehousing system adopting cargo-to-person mode, all the items are stored in the movable shelves. There are some warehouse robots transporting the shelves to the working platforms for completing order picking or items replenishment tasks. When the number of robots is insufficient, the task allocation problem of robots is an important issue in designing the warehousing system. In this paper, the task allocation problem of insufficient warehouse robots (TAPIR) is investigated. Firstly, the TAPIR problem is decomposed into three sub-problems: task grouping problem, task scheduling problem and task balanced allocation problem. Then three sub-problems are respectively formulated into integer programming models, and the corresponding heuristic algorithms for solving three sub-problems are designed. Finally, the simulation and analysis are done on the real data of online bookstore. Simulation results show that the mathematical models and algorithms of this paper can provide a theoretical basis for solving the TAPIR problem.展开更多
文摘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.
文摘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.
基金King Saud University for funding this work through Researchers Supporting Project Number(RSP-2021/387),King Saud University,Riyadh,Saudi Arabia.
文摘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.
文摘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.
文摘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.
基金financially supported by the National Key Research and Development Plan of China (No.2020YFB1713600)the National Natural Science Foundation of China (No.51975043)the Fundamental Research Funds for the Central Universities (Nos.FRF-TP-19002A3 and FRF-TP-20-105A1)。
文摘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.
文摘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.
基金the National Natural Science Foundation of China (70471063 ,70171036)
文摘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.
文摘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.
文摘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.
基金Project(2009BADB9B09)supported by the National Key Technologies R&D Program of China
文摘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.
文摘Since 1990s,the spatial data warehouse technology has rapidly been developing, but due to the complexity of multi-dimensional analysis, extensive application of the spatial data warehouse technology is affected. In the light of the characteristics of the flood control and disaster mitigation in the Yangtze river basin, it is proposed to design a scheme about the subjects and data distribution of the spatial data warehouse of the flood control and disaster mitigation in Yangtze river basin, i.e., to adopt a distributed scheme. The creation and development of the spatial data warehouse of the flood control and disaster mitigation in Yangtze river basin is presented .The necessity and urgency of establishing the spatial data warehouse is expounded from the viewpoint of the present situation being short of available information for the flood control and disaster mitigation in Yangtze river basin.
文摘On the bas is of the reality of material supply management of the coal enterprise, this paper expounds plans of material management systems based on specific IT, and indicates the deficiencies, the problems of them and the necessity of improving them. The structure, models and data organizing schema of the material management decision support system are investigated based on a new data management technology (data warehousing technology).
文摘To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.
文摘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.
文摘Objective: To establish an interactive management model for community-oriented high-risk osteoporosis in conjunction with a rural community health service center. Materials and Methods: Toward multidimensional analysis of data, the system we developed combines basic principles of data warehouse technology oriented to the needs of community health services. This paper introduces the steps we took in constructing the data warehouse;the case presented here is that of a district community health management information system in Changshu, Jiangsu Province, China. For our data warehouse, we chose the MySQL 4.5 relational database, the Browser/Server, (B/S) model, and hypertext preprocessor as the development tools. Results: The system allowed online analysis processing and next-stage work preparation, and provided a platform for data management, data query, online analysis, etc., in community health service center, specialist outpatient for osteoporosis, and health administration sectors. Conclusion: The users of remote management system and data warehouse can include community health service centers, osteoporosis departments of hospitals, and health administration departments;provide reference for policymaking of health administrators, residents’ health information, and intervention suggestions for general practitioners in community health service centers, patients’ follow-up information for osteoporosis specialists in general hospitals.
文摘In the smart warehousing system adopting cargo-to-person mode, all the items are stored in the movable shelves. There are some warehouse robots transporting the shelves to the working platforms for completing order picking or items replenishment tasks. When the number of robots is insufficient, the task allocation problem of robots is an important issue in designing the warehousing system. In this paper, the task allocation problem of insufficient warehouse robots (TAPIR) is investigated. Firstly, the TAPIR problem is decomposed into three sub-problems: task grouping problem, task scheduling problem and task balanced allocation problem. Then three sub-problems are respectively formulated into integer programming models, and the corresponding heuristic algorithms for solving three sub-problems are designed. Finally, the simulation and analysis are done on the real data of online bookstore. Simulation results show that the mathematical models and algorithms of this paper can provide a theoretical basis for solving the TAPIR problem.