• Korean Management Science Review
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• v.13 no.1
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• pp.57-70
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• 1996

This paper develops an adaptive scheduling policy for flexible manufacturing systems. The inductive learning methodology used for constructing this state-dependent scheduling policy provides and understanding of the relative importance of the various system parameters in determining the appropriate scheduling rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single scheduling rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tiiight due date jobs, one of the most common surces of disruptions in most manufacturing systems.

• International Commerce and Information Review
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• v.12 no.4
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• pp.509-535
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• 2010

This paper examines the Spaghetti bowl effect that different tariffs and rules of origin in multiple FTAs have resulted in increasing the significantly additional burden for business when it comes to apply for the use of FTA preference. The wide spread of FTAs in the several years, from 2003 to 2010, has been the most important trade policy development in economically important Korea. Korea presently has 5 FTAs in effect, and made 3 additional agreements which will be expected to take effect in next year. With the study result and expecting a growing number of FTAs in Korea in a next decade, the international trading firms will face rise of transaction costs for enterprises, particularly small- and medium-sized enterprises(SMEs) to cope with multiple tariffs and rule of origins in FTAs. To help mitigate negative effects and facilitate a more SEMs to use the FTA preference, providing new computer programs system, increasing the awareness of FTA provisions, improving business participation in FTA consultations, and SME support in light of education, and financial support are needed.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.331-339
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• 2004

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.1
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• pp.1-10
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• 2004

Advance in Internet technology has changed traditional production planning and control methods. In particular, collaborations between participants in supply chains are being increasingly addressed in industry for enhancing chain-wide productivity. A representative paradigm that emphasizes collaboration in production planning and control is CPFR(Collaborative Planning, Forecasting and Replenishment). In this paper, we present a multi-agent system architecture that supports the collaborations specified in CPFR. The multi-agent system architecture consists of event manager, data view agent, business rule agent, and collaboration agent. The collaboration agent systematically controls negotiation between supplier and buyer with the aid of collaboration protocol and blackboard. The multi-agent system has been implemented with EJB(Enterprise Java Beans).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.293-296
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• 2009

비즈니스 룰(Business Rule)은 하나의 목적을 성취하기 위해 조직에 적용하는 운영, 정의, 제약으로 구성되는 하나의 규칙이다. 이를 효율적으로 통합 관리하는 시스템이 BRMS(Business Rule Management System)이다. 대학에서의 졸업사정은 학사과정의 마지막 단계로 여러 복합적 충족 여부를 검사하는 과정이다. 학교의 전반적인 규칙이나 해마다 교육과정의 변화가 있어 프로그래밍 구현이 어렵고, 졸업사정시스템을 전산화하기 힘들다. 본 논문에서는 비즈니스 룰의 장점들을 활용해 변화를 빠르고 정확하게 반영하는 BRMS를 이용하여 졸업사정시스템의 설계를 제안하고자 한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.294-300
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• 2006

This case study shows constructing the knowledge-based system using a rule-based approach for detecting transactions regarding money laundering in the Korea Financial Intelligence Unit (KoFIU). To better manage the explosive increment of low risk suspicious transactions reporting from financial institutions and to conjugate data converged into the KoFIU from various organizations, the adoption of a knowledge-based system is definitely required. We designed and constructed the knowledge-based system for anti-money laundering by committing experts of each specific financial industry co-worked with a knowledge engineer. The outcome of the knowledge base implementation shows that the knowledge-based system is filtering STRs in the primary analysis step efficiently and so has made great contribution to improve efficiency and effectiveness of the analysis process. It can be said that establishing the foundation of the knowledge base under the entire framework of the knowledge-based system for consideration of knowledge creation and management is indeed valuable.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.139-157
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• 2002

The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2007.06a
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• pp.167-168
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• 2007

Design Rule이 작아짐에 따라 Transistor performance 향상을 위한 여러 방안중 SEG 공정이 적용되고 있으며 이에 따른 Transistor 특성 연구 결과이다. SEG공정 적용시 SEG Profile에 따라 Transistor의 Short Channel Effect 열화가 발생하였고 그 원인은 Sidewall Facet발생으로 추정되며 이를 개선시 Tr 특성이 개선됨을 확인하였다.

• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.135-154
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• 2003

Credit scoring system (CSS) starts from an analysis of delinquency trend of each individual or industry. This paper conducts a research on credit card delinquency of bank customers as a preliminary step for building effective credit scoring system to prevent excess loan or bad credit status. To serve this purpose, we use association rules as a rule generating data mining technique. Specifically, we generate sets of rules of customers who are in bad credit status because of delinquency by association rule mining. We expect that the sets of rules generated by association rule mining could act as an estimator of good or bad credit status classifier and basic component of early warning system.

• Asia pacific journal of information systems
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• v.14 no.4
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• pp.87-97
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• 2004

One of the most fundamental problems in business is ranking items with respect to profit based on historical transactions. The difficulty is that the profit of one item comes from its influence on the sales of other items as well as its own sales, and that there is no well-developed algorithm for estimating overall profit of selected items. In this paper, we developed a product network based on association rule and an algorithm for profit estimation and item selection using the estimated profit ranking(EPR). As a result of computer simulation, the suggested algorithm outperforms the individual approach and the hub-authority profit ranking algorithm.