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- 저자 : 고중훈(Joong-Hoon Ko) (검색결과 2 건)
- 무료
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Efficiency Analysis of Project Management Offices Using Bootstrap DEA
Joong-Hoon Ko(고중훈),Sung-Hun Park(박성훈),Eun-Song Bae(배은송),Dae-Cheol Kim(김대철) 한국산업경영시스템학회 2018 한국산업경영시스템학회지 Vol.41 No.3
The purpose of this study is to analyze the efficiencies of project management offices in large information system construction projects using the data envelopment analysis. In addition, we tried to estimate the confidence interval of those efficiencies using bootstrap DEA to give a statistical meaning. The efficiency by the CCR model is analyzed as eight project management offices are fully efficient and 22 project management offices are inefficient. On the other hand, there are 15 project management offices are fully efficient, but 15 project management offices are inefficient in the BCC model. As the result of the scale efficiencies, of the inefficient project management offices, 13 project management offices are inefficient in scale. It is possible to eliminate the inefficiency in the CCR model by improving their project performances. And, the nine project management offices showed that the inefficiency was due to pure technical efficiency, and these companies should look for various improvements such as improvement of project execution system and project management process. In order that the inefficient project management offices be efficient, it is analyzed that more efforts must be made for on-budget and on-time as a result of examining the potential improvement potentials of inefficient project management offices.
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XGBoost를 이용한 교통노드 및 교통링크 기반의 교통사고 예측모델 개발
김운식(Un-Sik Kim),김영규(Young-Gyu Kim),고중훈(Joong-Hoon Ko) 한국산업경영시스템학회 2022 한국산업경영시스템학회지 Vol.45 No.2
This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.
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