• Journal of Korean Institute of Industrial Engineers
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• v.20 no.3
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• pp.125-137
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• 1994

Manufacturing environs constantly change, and any efficient software system to be used in manufacturing must be able to adapt to the varying situations. In a CAPP (Computer-Aided Process Planning) system, a learning capability is necessary for the CAPP system to do change along with the manufacturing system. Unfortunately only a few CAPP systems currently possess learning capabilities. This research aims at the development of a learning system which can increase the knowledge in a CAPP system. A part in the system is represented by frames and described interactively. The process information and process planning logic is represented using a decision tree. The knowledge expansion is carried out through an interactive expansion of the decision tree according to human advice. Algorithms for decision tree modification are developed. A path can be recommended for an unknown part of limited scope. The processes are selected according to the criterion such as minimum time or minimum cost. The decision tree, and the process planning and learning procedures are formally defined.

• Proceedings of the KSR Conference
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• 2003.10b
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• pp.454-459
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• 2003

The process of determining whether, when, where and how to intervene, of deciding on optimum allocation of resources and minimizing the cost is a very complex problem: different track sections tend to behave differently under the effects of loading; decision-making processes for maintenance work are closely interrelated technically and economically; decision-making for maintenance plans is based on a large quantity of technical and economic information, extensive knowledge and above all experience. For that reason, It is considered very important to develope objective and computer-aided decision-making support system for track maintenance plan. On this paper, we reviewed ECOTRACK system and present the plan of develope decision-making support system for track maintenance appropriate to local condition.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.3
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• pp.625-632
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• 2012

Real Estate investment decision process make a purpose of the investment, the investment environment, cost convenience ananlysis and the investment validity. For this, Real Estate investment intention decision has investment ananlysis and grope for practice. therefore this paper has a purpose for Real Estate investment decision process that solve the more than brilliant course about Real Estate investment.

• Journal of Information Technology Applications and Management
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• v.15 no.1
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• pp.139-152
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• 2008

Recently organizations identify information security as one of essential means for gaining competitive advantage. However, they do not actively increase investment in this area because they consider spending for information security as a cost rather than an investment. This is because organizations don't have a clear understanding of information security objectives which can be achieved through investment, and they don't have criteria for alternatives which can be considered in information security investment decision-making. In this paper we propose to model the decision-making process of information security investment by the AHP (Analytic Hierarchy Process). The results will show that availability is the most important criterion for the decision of information security alternatives, and intrusion detection is the most important information security alternative. We hope that the results of this paper provide a guideline for clear decision-making in information security investment.

• Proceedings of the KSR Conference
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• 2004.10a
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• pp.886-891
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• 2004

The process of determining whether, when, where and how to intervene, of deciding on optimum allocation of resources and minimizing the cost is a very complex problem: different track sections tend to behave differently under the effects of loading; decision-making processes for maintenance work are closely interrelated technically and economically; decision-making for maintenance plans is based on a large quantity of technical and economic information, extensive knowledge and above all experience. For that reason, It is considered very important to develope objective and computer-aided decision-making support system for track maintenance plan. On this paper, we made a survey of the state-of-art of decision-making support systems for track maintenance which were developed and used abroad and reviewed requirements of system and present the plan of develope decision-making support system for track maintenance appropriate to local condition.

• IE interfaces
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• v.20 no.1
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• pp.41-48
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• 2007

The main purpose of this paper is cost reduction in absurd pilot positive expense and human accident prevention which is caused by in the pilot selection process. We use classification models such as logistic regression, decision tree, and neural network based on aptitude test results of 505 ROK Air Force applicants in 2001~2004. First, we determine the reliability and propriety against the aptitude test system which has been improved. Based on this conference flight simulator test item was compared to the new aptitude test item in order to make additional yes or no decision from different models in terms of classification accuracy, ROC and Response Threshold side. Decision tree was selected as the most efficient for each sequential flight training result and the last flight training results predict excellent. Therefore, we propose that the standard of pilot selection be adopted by the decision tree and it presents in the aptitude test item which is new a conference flight simulator test.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2006.10a
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• pp.144-153
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• 2006

In order to take TC forecasts from different observatories into consideration, and make quantitative assessment and analysis for avoiding TC routes from the view of safety and cost, a new safe-economic decision making method of TC avoidance routing on ocean was put forward. This model is based on combining forecast of TC trace based on neural networks, technical method to determine the future TC wind and wave fields, technical method of fuzzy information optimization, risk analysis theory, and meteorological-economic decision making theory. It has applied to the simulation of MV Tianlihai's shipping on ocean. The result shows that the model can select the optimum plan from 7 plans, the selected plan is in accordance with the one selected by experienced captains.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.1144-1149
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• 2005

This paper presents how to establish the decision support model for the cooperative design in order to improve design coordination and optimize the building system. With this view, the paper presents the method that analyzes decision making participants of each building system on drawings. It also presents the combination evaluation method from the viewpoint of performance, cost and constructability to improve the decision making process in cooperative design.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.11-19
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• 2015

This paper presents optimized artificial neural networks (ANNs) claims prediction and decision awareness framework that guides owner organizations in their pre-bid construction project decisions to minimize claims. The framework is composed of two genetic optimization ANNs models: a Claims Impact Prediction Model (CIPM), and a Decision Awareness Model (DAM). The CIPM is composed of three separate ANNs that predict the cost and time impacts of the possible claims that may arise in a project. The models also predict the expected types of relationship between the owner and the contractor based on their behavioral and technical decisions during the bidding phase of the project. The framework is implemented using actual data from international projects in the Middle East and Egypt (projects owned by either public or private local organizations who hired international prime contractors to deliver the projects). Literature review, interviews with pertinent experts in the Middle East, and lessons learned from several international construction projects in Egypt determined the input decision variables of the CIPM. The ANNs training, which has been implemented in a spreadsheet environment, was optimized using genetic algorithm (GA). Different weights were assigned as variables to the different layers of each ANN and the total square error was used as the objective function to be minimized. Data was collected from thirty-two international construction projects in order to train and test the ANNs of the CIPM, which predicted cost overruns, schedule delays, and relationships between contracting parties. A genetic optimization backward analysis technique was then applied to develop the Decision Awareness Model (DAM). The DAM combined the three artificial neural networks of the CIPM to assist project owners in setting optimum values for their behavioral and technical decision variables. It implements an intelligent user-friendly input interface which helps project owners in visualizing the impact of their decisions on the project's total cost, original duration, and expected owner-contractor relationship. The framework presents a unique and transparent hybrid genetic algorithm-ANNs training and testing method. It has been implemented in a spreadsheet environment using MS Excel$^{(R)}$ and EVOLVERTM V.5.5. It provides projects' owners of a decision-support tool that raises their awareness regarding their pre-bid decisions for a construction project.

• Korea Journal of Hospital Management
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• v.21 no.4
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• pp.1-12
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• 2016

The purpose of this study are suggest to performance measurement model of Electronic Medical Record(EMR) and Key Performance Index(KPI). For data collection, 665 questionnaires were distributed to medical record administrators and insurance reviewers at 31 hospitals, and 580 questionnaires were collected(collection rate: 87.2%). Regarding methodology, Critical Success Factor(CSF) and index of the information system were derived based on previous studies, and these were set as performance measurement factors of EMR system. The performance measurement factors were constructed by perspective using BSC, and analysis on causal relationship between factors was conducted. A model of causal relationship was established, and performance measurement model of EMR system was proposed through model validation. Analysis on causal relationship between performance management factors revealed that utility cognition of the learning & growth perspective factor had causal relationship with job efficiency(${\beta}=0.20$) and decision support(${\beta}=0.66$) of the internal process perspective factors, and security had causal relationship with system satisfaction(${\beta}=0.31$) of the customer perspective factor. System quality had causal relationship with job efficiency(${\beta}=0.66$) and decision support(${\beta}=0.76$) of the internal process perspective factors, all of which were statistically significant(P<0.01). Job efficiency of the internal process perspective had causal relationship with system satisfaction(${\beta}=0.43$), and decision support had causal relationship with decision support satisfaction(${\beta}=0.91$) and job satisfaction (${\beta}=0.74$), all of which were statistically significant(P<0.01). System satisfaction of the customer perspective had causal relationship with job satisfaction(${\beta}=0.12$), job satisfaction had causal relationship with cost reduction(${\beta}=0.53$) of the financial perspective, and decision support satisfaction had causal relationship with productivity improvement(${\beta}=0.40$)of the financial perspective(P<0.01). Also, cost reduction of the financial perspective had causal relationship with productivity improvement(${\beta}=0.37$), all which were statistically significant(P<0.05). Suitability index verification of the performance measurement model whose causal relationship was found to be statistically significant revealed that $X^2/df=2.875$, RMR=0.036, GFI=0.831, AGFI=0.810, CFI=0.887, NFI=0.838, IFI=0.888, RMSEA=0.057, PNFI=0.781, and PCFI=0.827, all of which were in suitable levels. In conclusion, the performance measurement indices of EMR system include utility cognition, security, and system quality of the learning & growth perspective, decision support and job efficiency of the internal process perspective, system satisfaction, decision support satisfaction, and job satisfaction of the customer perspective, and productivity improvement and cost reduction of the financial perspective. In this study, it is expected that the performance measurement indices and model of EMR system which are suggested by the author, will be a measurement tool available for system performance measurement of EMR system in medical institutions.