• 한국경영과학회지
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• 제23권2호
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• pp.103-129
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• 1998

Corporate level decision making with multiple decision makers in a consistent way is essential in Decision Support System. However, since the decision makers have different interests and knowledge, the models used by them are also different in their level of abstraction. This makes decision makers waste a lot of efforts for an integrated decision making. The purpose of this paper is to propose an integration mechanism so that collaborative decision making models may be used synthetically in multi-abstraction level. Models are classified as multimedia model, mathematical model, qualitative model, causal ＆ directional model, causal model, directional model and relationship model according to the level of abstraction. The proposed integration mechanism consists of model interpretation phase. model transformation phase, and model integration phase. Specifically, the model transformation Phase is divided into (1) model tightening mode which gather information to make a model transformed into upper level model, and (2) model relaxing mode which makes lower level model. In the model integration phase, models of same level are to be integrated schematically. An illustrative M＆A-decision example is given to show the possibility of the methodology.

• 한국병원경영학회지
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• 제21권4호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2006년도 춘계학술대회 논문집
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• 한국농공학회논문집
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• 제66권4호
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• pp.17-26
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• 2024

This study aimed to design and build a web-based decision support system for wheat cultivation management. The system is designed to collect and measure the weather environment at the growth stage on a daily basis and predict the soil moisture content. Based on this, APSIM, one of the process-based crop models, was used to predict the potential yield of wheat cultivation in real time by making decisions at each stage. The decision-making system for wheat crop management was designed to provide information through a web-based dashboard in consideration of user convenience and to comprehensively evaluate wheat yield potential according to past, present, and future weather conditions. Based on the APSIM model, the system estimates the current yield using past and present weather data and predicts future weather using the past 40 years of weather data to estimate the potential yield at harvest. This system is expected to be developed into a decision support system for farmers to prescribe irrigation and fertilizer in order to increase domestic wheat production and quality by enhancing the yield estimation model by adding influence factors that can contribute to improving wheat yield.

• Asia pacific journal of information systems
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• 제15권2호
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• pp.195-218
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• 2005

The emergence of ubiquitous computing environment will change the service architecture of business information systems such as Decision Support System(DSS), which will be a new application. Recent mobile DSSs allow the decision makers to be benefited from web and mobile technology. However, they seldom refer to context data, which are useful for proactive decision support. Meanwhile, ubiquitous applications so far provide restricted personalization service using context and preference of the user, that is, they do not fully make use of decision making capabilities. Hence, this paper aims to describe how the decision making capability and context-aware computing are jointly used to establish ubiquitous applications. To do so, an amended DSS paradigm: CKDDM(Context-Knowledge-Dialogue-Data-Model) is proposed in this paper. What will be considered for the future decision support systems when we regard ubiquitous computing technology as an inevitable impact that enforces the change of the way of making decisions are described. Under the CKDDM paradigm, a framework of ubiquitous decision support systems(ubiDSS) is addressed with the description of the subsystems within. To show the feasibility of ubiDSS, a prototype system, CAMA-myOpt(Context-Aware Multi Agent System-My Optimization) has been implemented as an illustrative example system.

• 대한기계학회논문집A
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• 제33권2호
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• pp.114-126
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• 2009

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• 국제학술발표논문집
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• The 1th International Conference on Construction Engineering and Project Management
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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.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.93-111
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• 2020

Purpose The purpose of this paper is to observe the relative priorities of importances among the modified versions of Block chain system, being based on AHP decision support model which should be also proposed in this paper. Design/methodology/approach Four versions modified from the beginning of Block chain were divided into Public& Permissionless, Private&Permissionless, Public&Permissioned and Private&Permissioned types. Five criteria for evaluating the four versions whether the version were suitable for Medical information system were introduced from five factors of Technologies Accept Model, which were Security, Availability, Variety, Reliability and Economical efficiency. We designed Decision support model based on AHP which would select the best alternative version suitable for introducing the Block chain technology into the medical information systems. We established the objective of the AHP model into finding the best choice among the four modified versions. First low layer of the model contains the five factors which consisted of Security, Availability, Variety, Reliability and Economical efficiency. Second low layer of the model contains the four modified versions which consisted Public&Permissionless, Private&Permissionless, Public&Permissioned and Private& Permissioned types. The structural questionnaire based on the AHP decision support model was designed and used to survey experts of medical areas. The collected data by the question investigation was analyzed by AHP analysis technique. Findings The importance priority of Security was highest among five factors of Technologies Accept Mode in the first layer. The importance priority of Private&Permissioned type was highest among four modified versions of Block chain technologies in second low layer. The second importance priority was Private&Permissionless type. The strong point of Private&Permissioned type is to be able to protect personal information and have faster processing speeds. The advantage of Private& Permissionless type is to be also able to protect personal information as well as from forging and altering transaction data. We recognized that it should be necessary to develop new Block chain technologies that would enable to have faster processing speeds as well as from forging and altering transaction data.

• 대한가정학회지
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• 제50권7호
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• pp.59-66
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• 2012

The purpose of this study was to identify a causal relationship in the career preparation behavior, parental social support, career decision making self-efficacy and the career maturity of the pre-service elementary school teachers. A total of 374 questionnaires were used for data analysis, excluding the 23 copies deemed insincere in response. To ensure the reliability and validity of the questions, technical statistics of the frequency, ratio, average, standard deviation, skewness, and kurtosis via PASW 18.0, item-total correlation, the totality, and the reliability analysis. The structural analysis via AMOS 7.0 in the bootstrapping method was undertaken to perform the path analysis among the variables and to assess the suitability of the model. The findings of the study led to the following conclusions: First, the causal model for the career preparation behavior, parental social support, career decision making self-efficacy, and the career maturity of the pre-service elementary school teachers is suitable to empirical analysis on research variables. Second, the career decision making self-efficacy of pre-service elementary teachers has direct effect on career preparation behavior positively. Third, parental social support of the pre-service elementary teachers has indirect effects on the career preparation behavior positively.

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