• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.15-23
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• 2011

This research considers scheduling problems with jobs which can be divided into sub-jobs and do not required to be processed immediately following one another. Heuristic algorithms considering how to divide jobs are proposed in an attempt to find near-optimal solutions within reasonable run time. The algorithms contain two phases which are executed recursively. Phase 1 of the algorithm determines how jobs should be divided while phase 2 solves the scheduling problem given the sub-jobs established in phase 1. Simulated annealing and genetic algorithms are applied for the two phases and four heuristic algorithms are established. Numerical experiment is conducted to determine the best parameter values for the heuristic algorithms. Examples with different sizes and levels of complexity are generated. Performance of the proposed algorithms is evaluated. It is shown that the proposed algorithms are able to efficiently and effectively solve the considered problems.

• Korean Management Science Review
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• v.23 no.3
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• pp.41-61
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• 2006

This study is to propose a sales forecasting framework for new products in the prelaunch phase where no saies data are available. For the purpose we first develop an extended Bass model with the dynamic market potential and then propose an estimation method based on the market survey and scenario methodology. The proposed parameter estimation method is different from previous studies in that most of them have only Proposed the management judgments or analogies. We also apply the proposed model to satellite DMB market in Korea to verify the model.

• Management Science and Financial Engineering
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• v.17 no.1
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• pp.19-43
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• 2011

Tuning the architecture of SVM (support vector machine) is to build an SVM model of better performance. Two different tuning methods of the grid search and the GA (genetic algorithm) have been addressed in the literature, each of which has its own methodological pros and cons. This paper suggests a combined method for tuning the architecture of SVM models, which employs the GAM (generalized additive models), the grid search, and the GA in sequence. The GAM is used for selecting input variables, and the grid search and the GA are employed for finding optimal parameter values of the SVM models. Applying the method to a bankruptcy prediction problem, we show that SVM model tuned by the proposed method outperforms other SVM models.

• Journal of Korean Society for Quality Management
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• v.36 no.1
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• pp.31-39
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• 2008

This paper proposes a method of estimating the lifetime distribution at use condition for constant stress accelerated life tests when an infant-mortality failure mode as well as wear-out one exists. General limited failure population model is introduced to describe these failure modes. It is assumed that the log lifetime of each failure mode follows a location-scale distribution and a linear relation exists between the location parameter and the stress. An estimation procedure using the expectation and maximization algorithm is proposed. Specific formulas for Weibull distribution are obtained. An illustrative example and the simulation results are given.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.179-188
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• 2004

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as neural network, logistic regression and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as neural network, CBR. However, few studies have dealt with integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes the methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both feature subset and parameters of SVM simultaneously for bankruptcy prediction.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.4
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• pp.443-453
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• 2002

Many firms are trying to optimize their production and distribution systems separately, but possible profit increase by this approach is limited. Nowadays, it is more important to analyze these two systems simultaneously for the integrated supply chain management. This paper is a computational study to investigate the effectiveness of integrating production and distribution scheduling. We are interested in a multi-plant, multi-retailer, multi-product and multi-period industrial problem where the objective in solving production and distribution scheduling problem is to maximize the total net profit. Computational results on test problems of various sizes using the heuristic we developed show a substantial advantage of the integrated scheduling approach over the decoupled scheduling process. Sensitivity analysis on the parameter values indicates that, under the right conditions, the effectiveness of integrating production and distribution functions can be extremely high.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.4
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• pp.256-262
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• 2016

The Weibull is widely used in reliability analysis, and several studies have attempted to improve estimation of the distribution's parameters. least squares estimation (LSE) or Maximum likelihood estimation (MLE) are often used to estimate distribution parameters. However, it has been proven that Bayesian methods are more suitable for small sample sizes than LSE and MLE. In this work, the Weibull parameter estimation accuracy of LSE, MLE, and Bayesian method are compared for sample sets with 3 to 30 data points. The Bayesian method was most accurate for sample sizes under 25, and the accuracy of the Bayesian method was similar to LSE and MLE as the sample size increased.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.612-618
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• 2010

In order to successfully manage of the state-of-the-art weapon system R&D program which are under the limited budget and schedule but also has a high level of technical risks, it is gaining weights to use such a scientific program management tool as TPM. Therefore, the purpose of this research is to review the concept of the TPM and its relation among MOE, MOP and TPM, and introduce an application method of TPM mainly focusing on helicopter development program. It has the organized structure, detailed procedure and 282 parameters for performance management through the TPM.

• Journal of Korean Society for Quality Management
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• v.39 no.2
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• pp.320-328
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• 2011

This research proposes comprehensive models for analyzing common cause failures (CCF) due to cumulative shocks and to assess system reliability under the CCF. The proposed cumulative shock models are based on the binomial failure rate (BFR) model. Six kinds of models are proposed so as to explain diverse cumulative shock phenomena. The models are composed of the initial failure probability, shape parameter, and the total shock number. Some parameters of the proposed models can not be explicitly estimated, so we adopt the Expectation-maximization (EM) algorithm in order to obtain the maximum likelihood estimator (MLE) for the parameters. By estimating the parameters for the cumulative shock models, the system reliability with CCF can be assessed sequentially according to the number of cumulative shocks. The result can be utilizes in dynamic probabilistic safety assessment (PSA), aging studies, or risk management for nuclear power plants. Replacement or maintenance policies can also be developed based on the proposed model.

• Journal of Applied Reliability
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• v.13 no.3
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• pp.217-227
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• 2013

Reliability growth is defined as the positive improvement in a reliability parameter over a period of time due to implementation of corrective actions to system design, operation or maintenance procedures, or the associated manufacturing process. In recent, the importance of reliability growth management has emerged in the military authority and industries. For effective application of reliability growth models, it is necessary to understand their characteristics and differences. This paper presents the concepts of reliability growth management and compares the features of reliability tracking and projection models centered on MIL-HDBK-189C for selecting the appropriate model for an one-shot system under development.