• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.8
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• pp.3371-3380
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• 2012

This paper finds the selecting evaluation index of technical development supporting business, applied to determine its validity. Measuring the new evaluation index for selection against the existing select evaluation index, it shows which index has accurately estimated the ranking of the companies with good performance. Using the BSC and AHP method, this study developed the select evaluation index of technology development supporting business.

• Korean Management Science Review
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• v.12 no.2
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• pp.15-30
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• 1995

The objectives of this paper are to provide software developer selection criteria and to develop evaluation framework using AHP (Analytic Hierarchy Process). The selection criteria are extracted from Software Development Life Cycle, Quality Assurance, and Productivitiy of Organization. In this paper, the selection model is proposed and its examples are illustrated. Though some further research is required, the proposed model can be regarded as a basis of a DSS for the selection of the software developer.

• Journal of the Korea Safety Management & Science
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• v.7 no.4
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• pp.165-177
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• 2005

Supply Chain Management(SCM) system is a critical investment that can affect future competitiveness and performance of a company. Selection of a right SCM system is one of the critical issues. This paper provides the characteristic factors of SCM system selection and the SCM system evaluation and selection model based on Analytic Hierarchy Process(AHP). The proposed model can systematically construct the objectives of SCM system selection to support the business goals. A empirical example demonstrates the feasibility of the proposed model and the model can help a company to make better decision-making in selecting SCM system.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.235-247
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• 2013

The evolution of information technology and proliferation of hospital management and managerial applications of computing has led to change in the characteristics, uses and evaluations of software for the hospital management. With the growing proliferation of microcomputer use and the value-added for management strategies, more and more software has been massively developed, produced and distributed for the hospital industry. The user is faced with an increasingly difficult choice in the evaluation and selection of software. For many reasons, users frequently must rely on expert evaluations of the technical functions and quality of software. The objectives of this study are to provide selection criteria for an Electronic Medical Record (EMR) and to develop an evaluation framework for the Hospital Information Systems. The major findings of our study are as follows (1) the identification of EMR evaluation characteristics (2) the design and development of EMR selection model and (3) the evaluation of the importance for EMR characteristics using Analytic Hierarchy Process (AHP). We identify 6 characteristics and 22 sub-characteristics of the EMR, calculate their weights, and decide the best configuration. Especially, the AHP methodology can be applied to gather knowledge from multiple experts. Because AHP can 1) facilitate the participation of multiple experts 2) increase group productivity and therefore result in both quantitatively and qualitatively superior outcomes than that of a single individual's work 3) provide a mechanism for reconciling conflict from multiple expert 4) validate the acquired knowledge, providing consistency of facts, and 5) enhance the accuracy reliability of the acquired knowledge increase through of the reliability provided by consensus across multiple experts. Although some further research is required, the proposed model can be regarded as a basis for the selection of EMR.

• Journal of Korean Society for Quality Management
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• v.32 no.2
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• pp.77-92
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• 2004

It is difficult but very necessary to measure the productivity of container terminals as logistics service provider. It is meaningful to find the appropriate inputs and outputs of the logistics service delivery systems and to measure the relationship between these inputs and outputs. This study proposes a model of evaluating the efficiency of container terminals. The evaluation consists of three phases. First, DEA(Data Envelopment Analysis) phase, determines the efficiency score and weights of DMUs(Decision Making Unit). This phase performs through four steps : selection of DMU, selection of DEA model, determination of input and output factors, calculation of efficiency score and weights for each DMU. Secondly, CEM (Cross Evaluation Model) phase, is to calculate the cross-efficiency scores of DMUs. This phase performs through three steps: selection of CEM, determination of cross-efficiency score for each DMU and development of cross-efficiency matrix. Finally, average cross-efficiency analysis phase is to compute the average cross-efficiency score. The proposed model discriminates among DMUs and ranks DMUs, whether they are efficient or inefficient.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.79-90
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• 1995

The material handling equipment selection, that is a key task in the material handling system design, is a complex, difficult task, and requires a massive technical knowledge and systematic analysis. It is invaluable to justify the selected equipment model by the performance evaluation before its actual implementation. This paper presents an intelligent knowledge-based expert system called "IMESE" created by authors, for the selection and evaluation of material handling equipment model suitable for movement and storage of materials in a manufacturing facility. The IMESE is consisted of four modules: a knowledge base to select an appropriate equipment type, a multiple criteria decision making procedure to choose the most favorable commercial model of the selected equipment type, a database to store the list of commercial models of equipment types with their specifications, and simulators to evaluate the performance of the equipment model. The whole process of IMESE is executed under VP-Expert expert system environment.vironment.

• Asian-Australasian Journal of Animal Sciences
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• v.6 no.4
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• pp.497-507
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• 1993

Four truncation selection schemes (SSs) were framed to predict and compare the age structures and genetic responses under the influence of various factor employing the scheme-specific algorithms. Two paths of selection, sires (bulls' sires) and dams (bulls' dams) to breed young bulls were considered. Among variable factors, four levels (0.3, 0.5, 0.7, 0.9) of precision of evaluation, five levels (0.0, 0.05, 0.10, 0.15, 0.20 genetic standard deviation) of genetic differences among age classes and 4 levels of proportions selected (for bulls' sire, 0.05, 0.10, 0.125, 0.25, and for bulls' dams 0.02, 0.04, 0.05, and 0.10) contemplated on both paths of selection. The number of age classes for bulls' dams and bulls' sires were 4 or 8 and 2 or 4, respectively. The stayability across age classes for bulls' dams was assumed to be 0.80 or 0.60. The candidates for selection for bulls' sires were equally distributed (0.5 or 0.25) across the age classes. The SS1 (selection on same proportions as candidates' distribution) revealed longest generation lengths and lowest yearly genetic responses. The average ages were youngest and yearly genetic responses were highest in SS4 (selection at each age-specific truncation point with the same average genetic superiority of selected parents across the ages) and followed by SS3 (selection at each agespecific truncation point with same predicted genetic values) and SS2 (selection at common truncation point on phenotypic values) in a population with overlapping generations. The results revealed the importance of choosing suitable selection scheme to acquire maximum yearly genetic responses especially when the genetic differences among age classes are large and the precision of evaluation is relatively low.

• Korean Management Science Review
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• v.12 no.1
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• pp.35-50
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• 1995

The material handling equipment selection, that is a key task in the material handling system design, is a complex, difficult task, and requires a massive technical knowledge and systematic analysis. It is also invaluable to justify the selected equipment model by the performance evaluation before its actual implementation. This paper presents an intelligent knowledge-based expert system called "IMESE" created by author, for the selection and evaluation of material handling equipment model suitable for movement and storage of materials in a manufacturing facility. The IMESE was constructed by using the tools of VP-Expert expert system shell, DBASEIII plus, FORTRAN 77, and SLAMII simulation language. The whole process of IMESE is executed under VP-Expert expert system environment.vironment.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.