• Journal of the Korean Operations Research and Management Science Society
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• v.23 no.1
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• pp.67-95
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• 1998

This research addresses flow time prediction in the dynamic unbalanced job shop scheduling environment. The specific purpose of the research is to develop the job flow time prediction model in the dynamic unbalance djob shop. Such factors as job characteristics, job shop status, characteristics of the shop workload, shop dispatching rules, shop structure, etc, are considered in the prediction model. The regression prediction approach is analyzed within a dynamic, make-to-order job shop simulation model. Mean Absolute Lateness (MAL) and Mean Relative Error (MRE) are used to compare and evaluate alternative regression models devloped in this research.

• Journal of the Korea Society for Simulation
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• v.14 no.3
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• pp.91-100
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes a service prediction-based job scheduling model and present its scheduling algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts the next processing time of each processing component and distributes a job to a processing component with minimum processing time. This paper implements the job scheduling model on the DEVS modeling and simulation environment and evaluates its efficiency and reliability. Empirical results, which are compared to conventional scheduling policies, show the usefulness of service prediction-based job scheduling.

• Proceedings of the Korea Society for Simulation Conference
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• 2005.05a
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• pp.29-33
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• 2005

Grid computing is widely applicable to various fields of industry including process control and manufacturing, military command and control, transportation management, and so on. In a viewpoint of application area, grid computing can be classified to three aspects that are computational grid, data grid and access grid. This paper focuses on computational grid which handles complex and large-scale computing problems. Computational grid is characterized by system dynamics which handles a variety of processors and jobs on continuous time. To solve problems of system complexity and reliability due to complex system dynamics, computational grid needs scheduling policies that allocate various jobs to proper processors and decide processing orders of allocated jobs. This paper proposes the service prediction-based job scheduling model and present its algorithm that is applicable for computational grid. The service prediction-based job scheduling model can minimize overall system execution time since the model predicts a processing time of each processing component and distributes a job to processing component with minimum processing time. This paper implements the job scheduling model on the DEVSJAVA modeling and simulation environment and simulates with a case study to evaluate its efficiency and reliability Empirical results, which are compared to the conventional scheduling policies such as the random scheduling and the round-robin scheduling, show the usefulness of service prediction-based job scheduling.

• Journal of East-West Nursing Research
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• v.12 no.1
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• pp.5-13
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• 2006

Purpose: This research has been conducted in order to confirm the major factors that prediction organizational commitment in registered nurses. Method: The subjects were 350 registered nurses from 3 hospitals in Seoul. The sample for data collection consisted of 329 useable questionnaires (94% overall return rate) for 2 weeks. The Instrument tools utilized in this study were organizational commitment scale, empowerment scale, job stress scale and job satisfaction scale and thoroughly modified to verify validity and reliability. The collected data have been analyzed using SPSS 11.0 program. Three outliers which were bigger than 3 in absolute value were found, so after taking them off, Multiple Regression was used for further analysis. Result: The major factors that prediction organizational commitment in registered nurses were job satisfaction, empowerment, age and unit experience, which explained 51.9% of organizational commitment. Conclusion: It has been confirmed that the regression equation model of this research may serve as a organizational commitment prediction factors in Registered Nurses.

• The Journal of the Korea Contents Association
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• v.18 no.5
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• pp.265-279
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• 2018

This study conducted an experiment using data mining techniques to develop prediction models of worker job turnover. The experiment used data from the '2015 Graduate Occupational Mobility Survey' by the Korea Employment Information Service. We developed the prediction models using a decision tree, Bayes net, and artificial neural network. We found that the decision tree-based prediction model reported the best accuracy. We also found that the six influential factors affecting employees' turnover intention are type of working time, job status, full-time or not full-time, regular working hours per week, regular working days per week, and personal development opportunities. From the decision tree-based prediction model, we derived 12 rules of employee turnover for all job types. Using the derived rules, we proposed helpful directions for enhancing workers' job tenure. In addition, we analyzed the influential factors affecting employees' job turnover intention according to four job types and derived rules for each: office (ten rules), culture and art (nine rules), construction (four rules), and information technology (six rules). Using the derived rules, we proposed customized directions for improving the job tenure for each group.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.97-105
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• 1991

In developing a regression relationship for a standard data system in work measurement, job characteristics are frequently measured with error when measurements are made in the field under less controlled conditions or when accurate instruments are not available. This paper concerns with the prediction of the total task time when job characteristics are measured with error. Integrated mean square error of prediction(IMSE) is developed as a measure of the effect of errors in job characteristics on the predicted total task time. By evaluating how IMSE is affected by the measurement error in each job characteristic, we can determine which error should be controlled to develop a desirable standard data system.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.409-414
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• 2024

This paper utilizes artificial intelligence to analyze vocational training evaluation data for people with disabilities and selects the optimal prediction model using various machine learning algorithms. It predicts the job categories most likely to employ trainees based on data such as gender, age, education level, type of disability, and basic learning abilities. The goal is to design customized training programs based on these predictions to enhance training efficiency and employment success rates.

• The Journal of Korean Academic Society of Nursing Education
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• v.15 no.1
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• pp.72-80
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• 2009

Purpose: This study was designed to identify the prediction factors that influence nurses' organizational commitment. Method: The sample of this study consisted of 526 full-time nurses randomly picked at 19 general hospitals in Korea. The data was analyzed by computer using SPSS 15.0 for Pearson's correlation coefficient, and multiple regression analysis. Result: 1) According to general characteristics, nurses' organizational commitment levels among the sample were significantly different in age, religion, social status, marital status, clinical career, and department satisfaction. 2) Level of nurses' organizational commitment was average 2.70, job satisfaction 2.91, burnout 3.03, empowerment 3.36, autonomy 2.93, and self-efficacy 3.51. 3) Nurses' organizational commitment had significant positive correlations with job satisfaction, empowerment, self-regulation, social support, self-efficacy, clinical career, and personnel movement experience. On the other hand, it had significant negative correlations with occupational stress, burnout, and age. 4) The prediction factors which influence Nurses' organizational commitment were job satisfaction($\beta$=.405), burnout($\beta$=-.282), self-regulation($\beta$=.171), clinical career($\beta$=.135). These factors were approximately 49.6% reliable in explaining nurses' organizational commitment. Conclusion: These results can be used to develop hospitals' management strategies for increasing organizational commitment effectiveness and nursing productivity.

• Korean Journal of Social Welfare
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• v.28
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• pp.251-280
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• 1996

• Journal of Korean Society for Quality Management
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• v.38 no.1
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• pp.52-63
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• 2010

The purpose of this study is to investigate whether or not belt's jobs represent a source of job enrichment that translates into positive job characteristics and satisfaction. Hackman and Oldham's 5 job characteristics, 3 critical psychological states and MPS are utilized for analyzing results of the study. The results indicate that Black and Master Black Belt's jobs have positive effects on all five job characteristics, 3 psychological states, and MPS score. However, the findings indicate that Black and Master Black Belt's jobs are no direct effect on job satisfaction suggesting that growth need strength controls on job satisfaction. The result shows that there is a support for the prediction that growth needs strength controls the effect of belt's jobs on job satisfaction.