• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.04a
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• pp.62-71
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• 1993

Recently, the computer software technology is not keeping pace with the increasing demand of application software development and rapid changes in business environment. To overcome this "software crisis", many researchers have studied the methodologies to improve the productivity in software development and the flexibility of software usage. Among these methodologies, the most promising and intensively studied methodology is the Object-Oriented Approach The purpose of this study is, therefore, to examine the applicability of the Object-Oriented Approach for improving productivity and flexibility in Management Information Systems development. For an application, we selected a financial planning model, especially focusing on the budgeted income statement. In this thesis, we identified relevent objects in the budgeted income statement, and represented them in the object models. By implementing these object models using C++ language, we evaluated their adaptability in the budget-making process, and showed, by comparing them with the existing Planning '||'&'||' Modeling Languages such as IFPS(Interactive Financial Planning Saystem), their practicability in Management Information Systems. The results of this study are as follows: First, the same object models can be used in making the budgeted income statement both in the department level and in the corporate level. Second, the object models provide the flexibility and extensibility of an application program in case of the changes in business environment. Third, the Object-Oriented Approach is a practical methodology to improve the productivity and cut down the maintenance cost of application software development.t.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.31-39
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• 2007

In this study we developed a turbidity management system to support the operation for effective turbid water management. The decision-making system includes various models for prediction of turbid water inflow, effective reservoir operation using the selective withdrawal facility, analysis of turbid water discharge in the downstream. The system is supported by the intensive monitoring devices installed in the upstream rivers, reservoirs, and downstream rivers. SWAT and HSPF models were constructed to predict turbid water flows in the Imha and Andong catchments. CE-QUAL-W2 models were constructed for turbid water behavior prediction, and various analyses were conducted to examine the effects of the selective withdrawal operation for efficient high turbid water discharge, turbid water distribution under differing amount and locations of turbid water discharge. A 1-dimensional dynamic water quality model was built using Ko-Riv1 for simulation of turbidity propagation in the downstream of the reservoirs, and 2-dimensional models were developed to investigate the mixing phenomena of two waters discharged from the Andong and Imha reservoirs with different temperature and turbidity conditions during joint dam operation for reducing the impacts of turbid water.

• East Asian Journal of Business Economics (EAJBE)
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• v.1 no.1
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• pp.17-21
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• 2013

Purpose: Forecasting of petroleum consumption is useful in planning and management of petroleum production and control of air pollution. Research Design, Data and Methodology: ARMA models, sometimes called Box-Jenkins models after the iterative Box-Jenkins methodology usually used to estimate them, are typically applied to auto correlated time series data. Results: Petroleum consumption modeling plays a role key in big urban air pollution planning and management. In this study three models as, MLFF, MLFF with GARCH (1,1) and ARMA(1,1), have been investigated to model the petroleum consumption forecasts. Certain standard statistical parameters were used to evaluate the performance of the models developed in this study. Based upon the results obtained in this study and the consequent comparative analysis, it has been found that the MLFF with GARCH (1,1) have better forecasting results.. Conclusions: Survey of data reveals that deposit of government policies in recent yeas, petroleum consumption rises in Tehran and unfortunately more petroleum use causes to air pollution and bad environmental problems.

• Proceedings of the Korea Water Resources Association Conference
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• 1992.07a
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• pp.443-447
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• 1992

This study describes the strategy for advanced decision support system (ADSS) development for integrated management of water resources and quality in reservoir systems. The developed ADSS consists of database that contain hydrologic data, observed operational data, and data to support specific reservoir operations simulation, optimization models, and water quality models. The optimization model, mass balance simulation model and water quality models are used in a general prototype ADSS, menu driven controlling framework that assists the user to specify and evaluate the alternative operational scenarios at one time. These alternative scenarios are evaluated by the models and the results are compared through the use of a graphical based display system. This graphical based system uses an icon based schematic representation of the system to organize the presentation of the results. The ADSS includes the ability to use monthly or weekly time periods of analysis for the models and it can use monthly historical or stochastically generated inflows.

• Journal of Environmental Impact Assessment
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• v.19 no.3
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• pp.307-314
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• 2010

Brebbia's model has been analyzed to develop the appropriate waterworks management system in Korea, and compared with the conventional models such as EPANET, WaterCad, and InfoWorks. The hydraulic theory of the models was analyzed. Each model's numerical techniques, required parameters, input data and operational methodologies, restrictions, practical applicability and other aspects were investigated. In order to check the validity of Brebbia model, the comparative analysis with EPANET, WaterCAD, and InfoWorks models was performed for linear and nonlinear cases. To find out advantages and disadvantages of each model, the modeling was performed for a simple network and for more complicated A city waterworks system, and the three models applicability was examined. Finally, optimal modeling technique and a model suitable for the use in Korea was suggested, and the problems related to present projects of waterworks management system in Korea were analyzed.

• Journal of the Korea Safety Management & Science
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• v.9 no.3
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• pp.165-171
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• 2007

Dependent models in quality statistics are classified as serially autocorrelated model, multivariate model and dependent sample model. Dependent sample model is most efficient in time and cost to obtain samples among the above models. This paper proposes to implement parametric and nonparametric models into production system depended on demand pattern. Nonparametric models have distribution free and asymptotic distribution free techniques. Quality statistical models are classified into two categories ; the number of dependent sample and the type of data. The type of data consists of nominal, ordinal, interval and ratio data. The number of dependent sample divides into 2 samples and more than 3 samples.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.1
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• pp.27-35
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• 2005

Due to the importance of lead time demand in the design of inventory management systems, researchers and practitioners have paid continuous attention and a few analytic models using the compound distribution approach have been reported. However, since the nature of compound distributions is hardly amenable, the analytic models have been done by non‐recognition of the compound nature of some components to reduce the analytic task. This study concerns some of the important aspects in the analytic models. Through the theoretic examination of the analytic model approach and the comparison with the rigid compound stochastic process approach, this study clarifies the assumptions implicitly made by the analytic models and provides some precautions in using the analytic models. Illustrative examples are also presented.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.21-43
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• 1994

Software acquisition involves purchase of new technology as well as the product itself. Consequently, evaluation of candidate packages or development contractors requires formal models that can objectively compare the candidates' technological characteristics with respect to user requirements. This paper proposes three technological evaluation models for software acquisition : 1) a structural model dthat organizes the technological factors to be evaluated, 2) a scoring model that quantifies the candidates' technological values, and 3) an organization model that orgnizes and assigns responsibilities to technical evaluators. Three models, initially built on expert surveys and later refined through interviews with opinion leaders, are primarily intended for governmental use : the Korean government is expected to use these models as the software acquisition standards starting in 1994.

• Journal of Korean Society for Quality Management
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• v.16 no.2
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• pp.18-33
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• 1988

A new diagnostic statistic for detecting outliers and influential observations in linear models is suggested and studied in this paper. The proposed statistic is a weighted sum of two measures ; one is for detecting outliers and the other is for detecting influential ovservations. The merit of this statistic is that it is possible to distinguish outliers from influential observations. This statistic can be used for not only regression models but also factorial design models. A Monte Carlo simulation study is reported to suggest critical values for detecting outliers and influential observations for simple regression models when the number of observations is 11. 21, 31, 41 or 51.

• Korean Management Science Review
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• v.10 no.2
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• pp.27-42
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• 1993

Markov chain models can be used to predict the state of the system in the future. We extend the existing Markov chain models in two ways. For the stationary model, we propose a procedure that obtains the transition probabilities by appling the empirical Bayes method, in which the parameters of the prior distribution in the Bayes estimator are obtained on the collaternal micro data. For non-stationary model, we suggest a procedure that obtains a time-varying transition probabilities as a function of the exogenous variables. To illustrate the effectiveness of our extended models, the models are applied to the macro and micro time-series data generated from actual survey. Our stationary model yields reliable parameter values of the prior distribution. And our non-stationary model can predict the variable transition probabilities effectively.