• IE interfaces
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• v.10 no.3
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• pp.209-216
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• 1997

This mixed push and pull production system defines that all stages are not ordered by either of the production systems. Some stages are ordered by a push-type production system and the other stages are ordered by a pull-type production system. A decision support system is built by using a combination of optimization program and the "tunable" SIMAN discrete-event simulation for the implementation of an optimally mixed production system. Finding this optimal system requires 6 CPU hours for the case study on a Pentium. Both the simulation and optimization model are validated with a case study of Phoenix company that manufactures transmitters. This paper uses survey from experts in this company for evaluation and validation of this system.

• IE interfaces
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• v.13 no.4
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• pp.627-638
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• 2000

During last ten years, logistics network management has become one of the most important sources of competitive advantage regarding logistics cost and customer service in numerous business segments. The key to success in this dynamic and severe business environment is the ability to design and plan the logistics network optimally in an integrated way. Network design and planning involves many issues relating to strategic, tactical, and operational decisions. To assist a logistics designer or planner, many computer-based decision support systems (DSS) have been developed. In this paper, the issues related to design, development, and implementation of the DSS for logistics network design and planning are discussed, and an ideal framework of the DSS is proposed with some ideas on the future development. Finally, DLNET, the DSS developed by author, is briefly introduced.

• Journal of Information Technology Services
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• v.4 no.2
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• pp.61-69
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• 2005

RFID is used for tracking systems in various business fields these days and these systems brought considerable efficiencies and cost savings to companies. Real-time based information acquired through RFID devices could be a valuable source of information for making decisions if it is combined with decision support tools like OLAP of a data warehouse that has originally been designed for analyzing static and historical data. As an effort of extending the data source of a data warehouse, RFID is combined with a data warehouse in this research. And OLAP is designed to analyze the dynamic real-time based information gathered through RFID devices. The implemented prototype shows that ubiquitous computing technology such as RFID could be a valuable data source for a data warehouse and is very useful for making decisions when it is combined with online analysis. The system architecture of such system is suggested.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.200-203
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• 1996

Object-Oriented Modeling Technique (OMT) [1] promotes better understanding of requirements, cleaner designs, and more maintainable systems. A development of Group Decision Support System (GDSS) needs this advantage of OMT. GDSS designed through OMT proposes 3 modelings, object modeling, dynamic modeling, and functional modeling. This paper illustrates a design of GDSS using these 3 modelings. By exploiting the object-oriented paradigm, this design results in a highly system-independent design. And this paper shows some implementation issues including a tip of C++ code.

• The Journal of Information Systems
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• v.15 no.1
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• pp.121-144
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• 2006

For constructing the purchasing decision-making model to maximize customer value on the electronic commerce, Means-Ends Network model was used for identifying means and fundamental objectives and their relationships were analyzed by the structural equation. A questionnaire survey of 481 customers in their internet shopping experiences was conducted to extract valid means and fundamental objectives' factors. As a result, 6 means objectives shopping travel, shipping errors, vendor trust, online payment, product choice, and recommender systems and 3 fundamental objectives-shopping convenience, internet ecology, and customer support were founded. Using these 9 factors, structural equation was analyzed 4 times to ensure statistical validities and to establish new interrelationships among them. The results showed that fundamental objectives are affected by the strong relationships within means objectives. This interrelationship with mens and fundamental objectives is interpreted as the purchasing decision-making model to maximize customer value on the electronic commerce in this paper.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.855-864
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• 2013

Decision tree classification is one of the important problems in data mining fields and data minings have been important tasks in the fields of large database technologies. Therefore the coupling efforts of data mining systems and database systems have led the developments of database primitives supporting data mining functions such as decision tree classification. These primitives consist of the special database operations which support the SQL implementation of decision tree classification algorithms. These primitives have become the consisting modules of database systems for the implementations of the specific algorithms. There are two aspects in the developments of database primitives which support the data mining functions. The first is the identification of database common primitives which support data mining functions by analysis. The other is the provision of the extended mechanism for the implementations of these primitives as an interface of database systems. In data mining, some primitives want be stored in DBMS is one of the difficult problems. In this paper, to solve of the problem, we describe the database primitives which construct and apply the optimized decision tree classifiers. Then we identify the useful operations for various classification algorithms and discuss the implementations of these primitives on the commercial DBMS. We implement these primitives on the commercial DBMS and present experimental results demonstrating the performance comparisons.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.1
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• pp.1-7
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• 2011

Most of expert systems use the text-oriented knowledge bases. However, knowledge management using the knowledge bases is considered as a huge burden to the knowledge workers because it includes some troublesome works. It includes chasing and/or checking activities on Consistency, Redundancy, Circulation, and Refinement of the knowledge. In those cases, we consider that they could reduce the burdens by using relational database management systems-based knowledge management infrastructure and convert the knowledge into one of easy forms human can understand. Furthermore they could concentrate on the knowledge itself with the support of the systems. To meet the expectations, in this study, we have tried to develop a general-purposed knowledge conversion tool for expert systems. Especially, this study is focused on the knowledge conversions among text-oriented knowledge base, relational database knowledge base, and decision tree.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.148-151
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• 2003

Recently school food service operations are confronted with the wide spread pressures for accountability and the need to increase productivity. This paper is concerned with the make-or-buy decision framework for school food service systems considering the multi-attributes in the decision making. For the purpose of considering the multi-attributes analysis method in decision making for the school foodservice, we developed a make-or-buy decision framework using the multi-attribute analysis method, analytic hierarchy process, AHP method for school food service system. Finally, we developed a systematic and practical solution builder for a three-step decision support system in the view of 1) brainstorming for the idea generation, 2) analytic hierarchy process, AHP as a multi-attribute structure ed analysis method, and 3) aggregation logic model to integrate the results of reviewers. We developed web based program and applied it to a school foodservice problem.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.333-340
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• 2000

Research on group decisions and electronic meeting systems have been increasing rapidly according to the widespread of Internet technology. Although various issues have been raised in empirical research, we will try to solve an issue on idea categorizing in the group decision making process of electronic meeting systems. Idea categorizing used at existing group decision support systems was performed in a top-down procedure and mostly b participants; manual work. This resulted in tacking as long in idea categorizing as it does for idea generating clustering an idea in multiple categories, and identifying almost similar redundant categories. However such methods have critical limitation in the electronic meeting systems, we suggest an intelligent idea categorizing methodology which is a bottom-up approach. This method consists of steps to present idea using keywords, identifying keywords' affinity, computing similarity among ideas, and clustering ideas. This methodology allows participants to interact iteratively for clear manifestation of ambiguous ideas. We also developed a prototype system, IIC (intelligent idea categorizer) and evaluated its performance using the comparision experimetn with other systems. IIC is not a general purposed system, but it produces a good result in a given specific domain.

• Journal of Intelligence and Information Systems
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• v.6 no.2
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• pp.63-76
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• 2000

Research on group decisions and electroinc meeting systems have been increasing rapidly according to the widespread of Internet technology. Although various issues have been raised in empirical research, we will try to solve an issue on idea categorizing in the group decision making process of elecronic meeting systems. Idea categorizing used at existing group decision support systems was performed in a top-down procedure and mostly participants\` by manual work. This resulted in tacking as long in idea categorizing as it does for idea generating, clustering an idea in multiple categories, and identifying almost similar redundant categories. However such methods have critical limitation in the electronic meeting systems, we suggest an intelligent idea categorizing methodology which is a bottom-up approach. This method consists of steps to present idea using keywords, identifying keywords\` affinity, computing similarity among ideas, and clustering ideas. This methodology allows participants to interact iteratively for clear manifestation of ambiguous ideas. We also developed a prototype system, IIC (intelligent idea categorizer) and evaluated its performance using the comparision experimetn with other systems. IIC is not a general purposed system, but it produces a good result in a given specific domain.