• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2001년도 춘계학술발표회요약집
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• pp.530.2-530
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• 2001

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2001년도 추계학술대회 논문집
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• pp.101-105
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• 2001

This paper is to illustrate the possibility to use organizational knowledge and data warehouse simultaenously for a decision maker. Organizational knowledge is produced for qualitative decision-making process and data warehouse is used for quantitative decision-making process. However, two things are currently implemented separately in many organizations although being needed for decision makers. This research shows a model for building integrated system and a prototyping system based on the model. And its effectiveness is discussed.

• 대한인간공학회:학술대회논문집
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• 대한인간공학회 1996년도 추계학술대회논문집
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• pp.181-184
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• 1996

Fuzzy hierarchical analysis(FHA) has the usefulness to allow decision maker's ambiguities when comparing two alternatives. But, for easiuly appling it to a decision problem, the handling its many data and for decision makers much not knowing fuzzy theory are the obstacles to must be overcomed even if the results of final fuzzy weights can be computed by a personal computer. This paper decribes that FHA is revised, and input/output interfaces are developed to collect input data easily and interprete the fuzzy resultlts. Finally, a fuzzy decision process is suggested with them.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2003년도 추계공동학술대회
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• pp.239-250
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• 2003

In this research, we propose a hybrid group decision support mechanism (H-GDSM) based on Fuzzy AHP (Analytic Hierarchy Process) and FCM (Fuzzy Cognitive Map). The AHP elicits a corresponding priority vector interpreting the preferred information among the decision makers. Corresponding vector was composed of the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale. However, AHP couldn't represent the causal relationship among information, which were used by decision makers. In contrast to AHP, FCM could represent the causal relationship among variables or information. Therefore, FCMs were successfully developed and used in several ill-structured domains, such as strategic decision-making, policy making, and simulations. Nonetheless, many researchers used subjective and voluntary inputs to simulate the FCM. As a result of subjective inputs, it couldn't avoid the rebukes of businessman. To overcome these limitations, we incorporated the Fuzzy membership functions, AHP and FCM into a H-GDSM. In contrast to current AHP methods and FCMs, the H-GDSM method developed herein could concurrently tackle the pairwise comparison involving causal relationships under a group decision-making environment. The strengths and contributions of our mechanism were 1) handling of qualitative knowledge and causal relationships, 2) extraction of objective input value to simulate the FCM, 3) multi-phase group decision support based on H-GDSM. To validate our proposed mechanism we developed a simple prototype system to support negotiation-based decisions in electronic commerce (EC).

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2005년도 공동추계학술대회
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• pp.557-567
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• 2005

The increasing complexity of the socio-economic environments makes it less and less possible for single decision-maker to consider all relevant aspects of problem. Therefore are, many organizations employ groups in decision making. In this paper, we present a multiperson decision making method using fuzzy logic with linguistic quantifier when each of group members specifies imprecise judgments possibly both on performance evaluations of alternatives with respect to the multiperson criteria and on the criteria. Inexact or vague preferences have appeared in the decision making literatures with a view to relaxing the burdens of preference specifications imposed to the decision-makers and thus taking into account the vagueness of human judgments. Allowing for the types of imprecise judgments in the model, however, makes more difficult a clear selection of alternative(s) that a group wants to make. So, further interactions with the decision-makers may proceed to the extent to compensate for the initial comforts of preference specifications. These interaction may not however guarantee the selection of the best alternative to implement. To circumvent this deadlock situation, we present a procedure for obtaining a satisfying solution by the use of linguistic quantifier guided aggregation which implies fuzzy majority. This is an approach to combine a prescriptive decision method via a mathematical programming and a well-established approximate solution method to aggregate multiple objects.

• 경영정보학연구
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• 제25권1호
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• pp.47-73
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• 2023

본 연구는 인공지능 기법 중 다중레이블 속성선택 방법을 적용하여 복잡한 경영환경에서 의사결정의 효과성을 증대시키는 방안을 설명한다. 인공지능 기반의 의사결정 시스템은 의사결정자의 선택과 판단을 돕거나, 대신하는 중요한 역할을 한다. 더욱이 최근 인공지능을 중심으로 한 비즈니스 의사결정은 기업의 성장 동력으로 평가받는데, 이를 위해서는 효과적인 의사결정 방법이 수반되어야 한다. 이에 본 연구는 의미 있는 속성값을 선별하는 CFS-BR(이진연관성 접근 기반의 상관관계 속성선택 모델)을 제안하여, 효과적인 의사결정을 지원하는 것을 돕는다. 예시데이터와 실증데이터의 분석 결과, CFS-BR은 유의미한 속성을 최상우선선별 알고리즘 기반으로 최상의 조합을 선별하므로 효율적 의사결정을 지원할 수 있고, 기존의 다중 레이블 속성선택 방법과 비교하였을 때 정확도가 높은 것으로 보아 효과적인 의사결정을 증대시키는 데 유용하다.

• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.71-78
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• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.

• 국제학술발표논문집
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.47-51
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• 2013

Existing buildings now represent the greatest opportunity to improve building energy efficiency. Building performance analysis is becoming increasingly important because decision makers can have a better visualization of their building's performance and quickly make the solution for improving building energy efficiency and reducing environmental impacts. Nowadays, building information models (BIMs) have been widely created during the design phase of new buildings, and it can be easily imported to third party software to conduct various analyses. However, a BIM is not always available for all existing buildings. Even if a BIM is available during the design and construction phases, it is very challenging to keep updating it while a building is aged. A manual process to create or update a BIM is very time consuming and labor intensive. A laser scanning technology has been a popular tool to create as-is BIM. However it still needs labor-intensive manual processes to create a BIM out of point clouds. This paper introduces automatic as-is simplified BIM creation from point clouds for energy simulations. A framework of decision support system that can assist decision makers on retrofits for existing buildings is introduced as well. A case study on a residential house was tested in this study to validate the proposed framework, and the technical feasibility of the developed system was positively demonstrated.

• 지능정보연구
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• 제12권2호
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• pp.125-144
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• 2006

최근들어 모바일 환경에서 운영되는 많은 응용시스템들이 개발되고 있다. 대부분의 시스템들은 모바일 사용자와의 단순한 상호작용만을 필요로 하는 메시지 전송, 은행 거래, 위치 서비스 등을 위한 것들이다. 단순한 기능만을 지원하는 이유는 모바일 장치가 스크린 크기가 제한적이고, 네트워크 대역폭이 좁으며, 컴퓨팅 능력이 낮기 때문이다. 이와 같은 이유로 모바일 장치를 활용하여 그룹의사결정을 지원하는 복잡한 알고리즘을 구현하는 것은 거의 불가능하였다. 본 연구에서는 모바일 환경에서의 그룹의사결정 과정을 지원하기 위하여 간결한 상호교호적 절차를 제시하고자 한다. 이 상호교호적 절차는 모바일 환경에서 그룹의 절충해를 선택하도록 돕기 위한 다목적 선형계획 프로그램에 기반을 두고 있다. 이 절차를 활용하게 되면 그룹의사결정자들의 정보제공의 부담을 줄여줄 수 있다. 최선의 절충해를 찾기 위해서 변수 및 목적식에 대한 부분적 순위 정보만을 활용하였다. 본 방법론은 의사결정자의 효용함수에 대한 형태 혹은 존재 여부에 대한 어떠한 가정도 하지 않고 있다. 본 절차의 실험적 연구를 위해서 모바일 환경에서의 그룹의사결정지원시스템인, MOBIGSS를 개발하였으며, 이 시스템을 개인 투자자의 자산 투자 문제에 적용하였다.

• 산업공학
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• 제2권1호
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• pp.47-58
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• 1989

Many decision problems in the real world have uncertainty and complexity. In many cases, decision makers do not have decision-analytic knowledge enough to solve a given decision problem. This paper developes a Decision Support System(DSS) that can be used for structuring decision problem into decision tree based on the concept of influence diagram and analyzing the decision problem by following Decision Analysis Cycle. This study suggests a DSS system(DACUL) in order to implement Decision Analysis Cycle using Lotus1-2-3. DACUL system has been developed in IBM XT/AT compatible PC.