• 한국건축시공학회지
• /
• 제11권3호
• /
• pp.238-246
• /
• 2011

Choosing the best-performance materials is a crucial task for the successful completion of a project in the construction field. In general, the process of material selection is performed through the use of information by a highly experienced expert and the purchasing agent, without the assistance of logical decision-making techniques. For this reason, the construction field has considered various artificial intelligence (AI) techniques to support decision systems as their own selection method. This study proposes the application of a systematic and efficient support vector machine (SVM) model to select optimal exterior materials. The dataset of the study is 120 completed construction projects in South Korea. A total of 8 input determinants were identified and verified from the literature review and interviews with experts. Using data classification and normalization, these 120 sets were divided into 3 groups, and then 5 binary classification models were constructed in a one-against-all (OAA) multi classification method. The SVM model, based on the kernel radical basis function, yielded a prediction accuracy rate of 87.5%. This study indicates that the SVM model appears to be feasible as a decision support system for selecting an optimal construction method.

• 한국경영과학회지
• /
• 제20권1호
• /
• pp.11-26
• /
• 1995

We have used various information technologies (IT) in group decision-making for increasing effectiveness and satisfaction of group decision-making process. Recently, a new form of IT so called Group Decision supprot System (GDSS) was introduced into group decision-making process. Previous experimental studies about effectiveness of GDSSS have been inconsistent and the results were mixed. There was no empirical studies about GDSS in Korea. In this study, we divide two groups-GDSS supported group and traditional face-to-face group and investigate impacts of GDSS on group decision-making processes and outcomes. An idea generation task for operational action plan development, implementing GEO's strategic vision, served as the decision-making context. Supported GDSS is GroupSystems V, which had been developed by Univ. of Arizona. It was translated by Korean. According to the results, Number of nonredundant ideas in GDSS group is two or three times more than Non-GDSS grop. GDSS group feel more equal status, have better interpersonal relationship, have more confidence on group decision than Non-GDSS group. But satisfaction of participants on decision-making process and outcome has not showed a significant difference between two groups. Because all of participants in GDSS group were novice to GDSS. With this results we suggest further studies on transforming western type GDSS to Korean decision-making culture. It will be cornerstone for development of GDSS in Korean desision-making culture. It will be cornerstone for devlopment of GDSS in Korean decsion making culture environment.

• 한국건설관리학회:학술대회논문집
• /
• 한국건설관리학회 2008년도 정기학술발표대회 논문집
• /
• pp.694-697
• /
• 2008

건설 생애 전 주기 중에서 기획단계의 지반 조사로 시작되는 흙막이 공법의 선정은 잘못 선정될 경우 해당 프로젝트의 공사비와 공기에 끼치는 영향이 매우 크다. 그렇기 때문에 공법 선정의 요인 및 프로세스에 관련된 연구가 많이 발표되었지만 일반인들의 활용이 어려워 실용화되지 못하고 있다. 본 연구에서는 본 연구의 선행연구에서 개발한 흙막이 공법 선정 프로세스에 기반하여 웹 기반 의사결정 지원시스템을 개발하였다. ASP.NET을 이용하여 사용자들의 접근성을 높이고 자료의 보완 및 수정이 용이한 웹 기반 시스템인 Dr. Underground로 발전시켰다. 이 의사결정 지원 시스템은 일반인들이 쉽게 접근하여 건축물 기본요구 사항과 대지 조건, 인접 대지 조건을 입력하면, 해당 프로젝트에 적절한 흙막이 공법을 선정해주고 그에 대한 상세 정보를 제공해준다.

• 한국산업정보학회논문지
• /
• 제13권5호
• /
• pp.55-65
• /
• 2008

기업의 전략적 의사결정자는 불확실성과 복잡성에 직면해왔다. 그리고 그들은 이러한 환경 하에서 의사결정을 내려야 함에도 불구하고 그들의 의사결정에 필요한 충분한 시간, 인력, 예산, 그리고 지식이 충분히 주어지지 않는다. 그래서 그들은 그 분야에 대한 암묵적 지식을 지닌 전문가들의 지원을 받는 수 밖에 없다. 그러나 의사결정자들이 어떤 문제에 직면할 때 마다 의사결정에 필요한 새로운 지식을 창조하고 변형하고 결합하고 응용하는 다른 절차나 방법을 발견하는 것은 매우 어렵다. 본 논문은 노나카에 의해 제안된 지식변환과정을 이용함으로써 전략적 의사결정을 지원할 새로운 방법을 제안한다. 본 연구는 컨설팅 산업분야에서 전략적 의사결정의 응용사례를 예시한다. 이 논문은 제안된 방법에 기초한 의사결정지원기법으로서 인지지도를 사용한다.

• International Journal of Computer Science & Network Security
• /
• 제24권2호
• /
• pp.150-154
• /
• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• Asia pacific journal of information systems
• /
• 제8권3호
• /
• pp.51-73
• /
• 1998

This paper is to evaluate the application potentials of data mining in the areas of Supply Chain Management (SCM) and to suggest the architectures of Decision Support Systems (DSS) that support data mining activities. We first briefly introduce data mining and review the recent literatures on SCM and then evaluate data mining applications to SCM in three aspects: marketing, operations management and information systems. By analyzing the cases about pricing models in distribution channels, demand forecasting and quality control, it is shown that artificial intelligence techniques such as artificial neural networks, case-based reasoning and expert systems, combined with traditional analysis models, effectively mine the useful knowledge from the large volume of SCM data. Agent-based information system is addressed as an important architecture that enables the pursuit of global optimization of SCM through communication and information sharing among supply chain constituents without loss of their characteristics and independence. We expect that the suggested architectures of intelligent DSS provide the basis in developing information systems for SCM to improve the quality of organizational decisions.

• 경영정보학연구
• /
• 제9권3호
• /
• pp.45-65
• /
• 2007

경영 환경이 급격하게 변화함에 따라서 전략적 의사결정의 중요성이 커지고 있고, 효과적인 전략적 의사결정 지원 시스템을 개발하기 위해서는 체계적인 전략적 의사결정 프로세스 개발, 효과적인 문제 해결 기법, 편리한 IT 도구가 필요하다. 본 연구에서는 전략적 의사결정 프로세스를 개발하고, 문제해결 기법을 분류하고, 이를 기반으로 기법 추천 시스템을 개발하였다. 이를 기반으로 한 SDSS 프레임웍은 기업의 전략적 의사결정을 지원하기 위한 SDSS 개발에 많은 도움을 줄 것으로 기대된다.

• Journal of Information Processing Systems
• /
• 제3권2호
• /
• pp.64-72
• /
• 2007

Mobile technology appears promising as a method to promote group performance in circumstances dependent on time, but not member proximity. However, the success of mobile technology in group decision-making situations has not yet been proven. This paper aims to see how mobile technology affects the performance of group decision-making tasks that should be resolved urgently and/or sources of idea are disconnected with on-line network. Laboratory experiment was used to investigate the effects of mobile factors on group decision-making. The results from the experiment supported the proposition that pressures of time and location play a significant role in the assessment of group decision performance measures. We found that the adoption of mobile technology to group decision-making procedures might be competitive when group decision-making tasks are urgent and sources of idea are disconnected with on-line network, even though mobile technology is not a panacea on which to depend when designing group decision-making.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1996년도 춘계공동학술대회논문집; 공군사관학교, 청주; 26-27 Apr. 1996
• /
• pp.679-682
• /
• 1996

The level of informaiton provided by most current EIS systems is not above presentation and reporting. To maximize the effect of EIS, we must increase executives' use-rate. To do so, it is important to provide information really required by the user. In a addition, the decision support funcitons should be provided for strategic decision making. In this paper we propose the methodology for adding the analysis and prediciton functions to the EIS that is, for supporting the decision making with the system. Finally, we present a pilot system based on the proposed methodology.

• Smart Structures and Systems
• /
• 제26권4호
• /
• pp.533-544
• /
• 2020

Effective bridge maintenance reduces bridge operation costs and extends its service life. The possibility of storing bridge life-cycle data in a 3D parametric model of the bridge through Bridge Information Modeling (BrIM) provides new opportunities to enhance current practices of bridge maintenance management. This study develops a Decision Support System (DSS), namely BrDSS, which employs BrIM and an efficient optimization model for bridge maintenance planning. The BrIM model in BrDSS extracts basic data of elements required for the optimization process and visualizes the inspection data and the optimization results to the user to help in decision makings. In the optimization module of the DSS, the specifically formulated Genetic Algorithm (GA) eliminates the chances of producing infeasible solutions for faster convergence. The practicality of the presented DSS was explored by utilizing the DSS in the maintenance planning of a bridge under operation in the southwest of Iran.