• IE interfaces
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• v.13 no.3
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• pp.358-377
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• 2000

A supply chain is a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers. SCM(Supp1y Chain Management), by definition, encompasses all activities associated with moving goods, from the raw materials stage through to the end user. It includes source and procurement, production scheduling, order processing, inventory management, transportation, warehousing, and customer service. Importantly, it encompasses the information systems used to monitor these activities. In this paper, the present situation and problems of marketing process in Korean agricultural environments were reviewed through a systematic methodology, and then we proposed a new business process for solving these problems by appling a supply chain management. We expect this supply chain management system applied to agricultural marketing process can improve significantly the rationality and transparency of Korean agricultural marketing structure.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.147-149
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• 2001

Customer centered supply chain Management is becoming a very important issue for manufacturing and distribution company. Increasingly, leading companies improve their competitiveness by shorting the time to delivery from warehousing to delivering, increasing the level of customer service. They also do their best to collaborate with suppliers and customers based on the information sharing. This case introduces a integrated approach to develop a logistics automation and information system for the spare parts distribution center of L electronics. We pursue to optimize the supply chain by implementing intelligent logistics system which has the capabilities of responding to the changes in market demands. This would provide many implications to the clients in solving logistics problems and secure superior competitiveness.

• Industrial Engineering and Management Systems
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• v.6 no.2
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• pp.125-135
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• 2007

An automated guided vehicle (AGV) system is a group of collaborating unmanned vehicles which is commonly used for transporting materials within manufacturing, warehousing, or distribution systems. The performance of an AGV system depends on the dispatching rules used to assign vehicles to pickup requests, the vehicle routing protocols, and the home location of idle vehicles, which are called dwell points. In manufacturing and distribution environments which emphasize just-in-time principles, performance measures for material handling are based on response times for pickup requests and equipment utilization. In an AGV system, the response time for a pickup request is the time that it takes for the vehicle to travel from its dwell point to the pickup station. In this article, an exact dynamic programming algorithm for selecting dwell points in a tandem-loop multiple-vehicle AGV system is presented. The objective of the model is to minimize the maximum response time for all pickup requests in a given shift. The recursive algorithm considers time restrictions on the availability of vehicles during the shift.

• Journal of Computing Science and Engineering
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• v.2 no.3
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• pp.221-248
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• 2008

Data provenance is the background knowledge that enables a piece of data to be interpreted and used correctly within context. The importance of tracking provenance is widely recognized, as witnessed by significant research in various areas including e-science, homeland security, and data warehousing and business intelligence. In order to further advance the research on data provenance, however, one must first understand the research that has been conducted to date and identify specific topics that merit further investigation. In this work, we develop a framework based on semiotics theory to assist in analyzing and comparing existing provenance research at the conceptual level. We provide a detailed review of data provenance research and compare and contrast the research based on d semiotics framework. We conclude with an identification of challenges that will drive future research in this field.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.393-396
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• 2005

Due to the fast growing rate of the global container trade, every major port is under the pressure of meeting the projected capacity demand. As a result, alternative solutions have been sought for improving capacity and meeting the growing demand for container storage area and terminal capacity. Moreover, material handling process re-engineering is now a critical issue for logistics and supply chain managers of airline, shipping lines, terminal and warehousing enterprises around the world. Therefore, the purpose of this paper is to develop the 3D simulator for executing performance valuation of port transportation systems. The developed 3D simulator system is to measure the effectiveness of the proposed total system and compare it with existing practices. The performance analysis variables are also defined for these comparisons

• The Journal of Information Systems
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• v.14 no.2
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• pp.237-256
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• 2005

데이터 웨어하우스는 기업의 통합된 데이터의 저장하는 곳이며, 대게는 상당한 규모를 가지고 있다. 또한, 데이터 웨어하우스는 일반적으로 다양한 종류의 데이터를 저장함으로 데이터 웨어하우스에 저장된 데이터는 의사결정 임무에 따라서는 그 질적, 적합성에 차이를 나타내고는 한다. 이러한 데이터 웨어하우스의 특성으로 인해서 때로는 데이터 웨어하우스의 데이터의 효용성이 기업의 의사결정을 지원하는데 있어 제한적일 수 있다. 정보 시스템의 문헌에는 데이터의 질이 의사결정 성과에 미치는 영향에 대해서 많이 알려져 있지 않다. 그래서, 본 연구는 contextual data(상황적 데이터)의 질과 업무의 복잡성이 의사결정 성과에 미치는 영향에 대해서 탐구해보고자 한다. Contextual data의 질과 업무의 복잡성이 의사결정의 성과에 미치는 영향을 조사하기 위하여 웹을 기반으로 하는 데이터 웨어하우스를 이용하는 실험을 실행했다. 연구의 결과는 contextual data의 질이 의사결정의 성과에 영향을 미친다는 것을 통계적으로 보여주었다. 이러한 연구결과는 의사결정자의 의사결정 성과를 향상시키기 위해서는 데이터 웨어하우스의 contextual data의 질을 향상시켜야한다는 것을 제시하고 있다.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2008.11a
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• pp.141-145
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• 2008

Because of world financial crisis, The Korea domestic construction industry has been damaged hugely on material supplying work. A current domestic material supply system can't react properly on a great rise of foreign exchange rate and raw material price, so the system has a problem which is not properly supplying material. To solve this problem, a more innovative material supply work system is seriously needed than ever. So this study wants to improve material supply system for supplying material right time, right place through applying a web-based system. Applying this, overall process of material-supply-work can be done on a web-based system for immediately understanding change of ordering and warehousing by material-supply-schedule to a domestic construction field.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.41-48
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• 2018

The recent sudden increase of big data has characteristics such as continuous generation of data, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such big data due to the limited processing speed and the significant storage expansion cost. Current implemented solutions are mainly based on relational database that are no longer adapted to these data volume. NoSQL solutions allow us to consider new approaches for data warehousing, especially from the multidimensional data management point of view. In this paper, we develop and propose the integrated design methodology based on MongoDB for big data applications. The proposed methodology is more scalable than the existing methodology, so it is easy to handle big data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.3
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• pp.49-54
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• 2018

A data warehouse is a system that collectively manages and integrates data of a company. And provides the basis for decision making for management strategy. Nowadays, analysis data volumes are reaching critical size challenging traditional data ware housing approaches. Current implemented solutions are mainly based on relational database that are no longer adapted to these data volume. NoSQL solutions allow us to consider new approaches for data warehousing, especially from the multidimensional data management point of view. In this paper, we extend the data warehouse design methodology based on relational database using star schema, and have developed a consistent design methodology from information requirement analysis to data warehouse construction for large scale data warehouse construction based on MongoDB, one of NoSQL.