• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.169-172
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• 2006

Most studies using state-space models have been conducted under the assumption of independently distributed noises in measurement and state equation without adequate verification of the assumption. To avoid the improper use of state-space model, testing the assumption prior to the parameter estimation of state-space model is very important. The purpose of this paper is to investigate the general relationship between parameters of state-space models and those of ARIMA processes. Under the assumption, we derive restricted parameter spaces of ARIMA(p,0,p-1) models with mutually different AR roots where $p\;{\le}\;5$. In addition, the results of ARIMA(p,0,p-1) case can be expanded to more general ARIMA models, such as ARIMA(p-1,0,p-1), ARIMA(p-1,1,p-1), ARIMA(p,0,p-2) and ARIMA(p-1,1,p-2).

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.39-42
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• 1996

Modeling techniques such as linear regression have been used to predict hurricane activity many months in advance of the start of the hurricane season with some success. In this paper, we construct feedforward neural networks to model Atlantic basin hurricane activity and compare the predictions of our neural network models to the predictions produced by statistical models found in the weather forecasting literature. We find that our neural network models produce reasonably accurate predictions that, for the most part, compare favorably to the predictions of statistical models.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.772-777
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• 2002

In this paper, economic screening procedures with dichotomous performance variable T and continuous screening variable X are considered when the rejected items are reprocessed. Two models are considered; normal and logistic models. It is assumed that X given T is normally distributed in the normal model, and $P(T=1{\mid}X=x)$ Is given by a logistic function in the logistic model. Profit models are constructed which involve four price/cost components; selling price, cost from an accepted nonconforming item, and reprocessing and inspectioncosts. Methods of finding the optimal screening procedures are presented and numerical examples are given.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.139-143
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• 2003

This paper is a report on in-progress investigation on antecedents of IS success in small and medium size enterprises. Previous studies on IS success in SME context largely extends research models derived from a large enterprise context. This research applies the grounded theory approach to 1109 ISP outputs exploring SME specific constructs in successful IS implementation and management. When successful, results of this research will provide insights into theories of IS in SME context that may practically applicable to IS practice in SMEs.

• Journal of the Korean Operations Research and Management Science Society
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• v.3 no.1
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• pp.81-91
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• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.201-216
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• 2013

Diffusion is a basic mathematical tool for modern financial engineering. The theory of the estimation methods for diffusion models is an important topic of the financial engineering. Many researches have been tried to apply the likelihood estimation method for estimating diffusion models. However, the likelihood estimation method for diffusion is complicated and needs much amount of computing. In this paper we develop the estimation methods which are simple enough to be compared to the Euler approximation method, and efficient enough statistically to be compared to the likelihood estimation method. We devise pseudo-likelihood and propose the maximum pseudo-likelihood estimation methods. The pseudo-likelihoods are obtained by approximating the transition density with normal distributions. The means and the variances of the distributions are obtained from the delta expansion suggested by Lee, Song and Lee (2012). We compare the newly suggested estimators with other existing estimators by simulation study. From the simulation study we find the maximum pseudo-likelihood estimator has very similar properties with the maximum likelihood estimator. Also the maximum pseudo-likelihood estimator is easy to apply to general diffusion models, and can be obtained by simple numerical steps.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.61-75
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• 2007

The purpose of this paper is to develop a new group classification method based on genetic algorithm and to com-pare its prediction performance with those of existing methods in the area of bond rating. To serve this purpose, we conduct various experiments with pilot and general models. Specifically, we first conduct experiments employing two pilot models : the one searching for the cluster center of each group and the other one searching for both the cluster center and the attribute weights in order to maximize classification accuracy. The results from the pilot experiments show that the performance of the latter in terms of classification accuracy ratio is higher than that of the former which provides the rationale of searching for both the cluster center of each group and the attribute weights to improve classification accuracy. With this lesson in mind, we design two generalized models employing genetic algorithm : the one is to maximize the classification accuracy and the other one is to minimize the total misclassification cost. We compare the performance of these two models with those of existing statistical and artificial intelligent models such as MDA, ANN, and Decision Tree, and conclude that the genetic algorithm-based group classification method that we propose in this paper significantly outperforms the other methods in respect of classification accuracy ratio as well as misclassification cost.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.4
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• pp.95-112
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• 2016

The purpose of this study is to develop a quantitative model to evaluate the performance of the servitized business model. We aim to quantitatively analyze the decisions of participants in the servitized supply chains, and provide methods to maximize the performance. We consider servitized supply chains consisting of a manufacturer and a service provider, that can be integrated, separated or coordinated based on the relationship between the manufacturer and the service provider. The decision models in each case are developed, and performance and profitability are analyzed. Utilizing the decision models in different cases, we compare the performances of different business models of the servitization. Since our models can be applied to analyze a wide range of the servitization business models, we expect this study can contribute to promote servitization in manufacturing companies by providing methods to evaluate the profitability of the servitization business model.

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.31-45
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• 2009

Reliability should be done from the initial stage of development to secure performance and safety of system. To establish and achieve target reliability of a system, reliability should be allocated into the subsystems. In the acquisition and development of a system, frequent failures will cause a negative effect on performing mission and occurs increasing operating cost. This study reviewed and evaluated the existing reliability allocation models using operation and maintenance costs to find the correlation between reliability allocation models and its operating cost. A target system reliability on the diesel engine to be developed for naval vessels is allocated into its subsystem based on the existing reliability allocation models. A selection methodology for reliability allocation models was made to minimize operating cost by using simulation based on the given operating diesel engine data for naval vessels.