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부트스트랩 자료포락분석을 이용한 프로젝트 관리 조직의 효율성 분석
고중훈,박성훈,배은송,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.
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.
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.
XGboost방식을 이용한 이동목적에 따른 이동수단 선택 모형개발
백의현(Euihyun Paik),정영준(Yungjoon Jung),윤기수(Kisoo Yoon),김시용(Siyong Kim),윤성웅(Soungwoong Yoon),고중훈(JoongHoon Ko) 한국정보과학회 2021 정보과학회 컴퓨팅의 실제 논문지 Vol.27 No.8
이동수단 선택 분석은 이동수단 수요를 이해하고 예측하게 하므로 교통관련 정책 수립에 중요하다. 기계학습 방식의 발전으로 다양한 분류기가 개발되었지만, 교통수단 선택 모형에 적합한 분류자 선택에 관한 연구는 미흡한 편이다. 본 연구에서는 세종시에서 사람들이 이동 시 고려하는 요인들을 분석하고, 이동수단 선택 모형에 적용 가능한 분류자(XGboost)를 이용하여 이동패턴을 학습하였으며, 이를 바탕으로 개인의 속성(연령, 성별, 운전면허, 직업, 이동 목적, 이동 거리 등)을 고려한 이동수단 선택 모형을 개발하였다. Analyzing travel mode choice patterns is important for establishing traffic-related policies, as it helps to understand and predict demand for transportation. Although various machine learning classifiers have been developed due to the advancement in machine learning methods, inadequate research has been performed on classifiers, suitable for the travel mode choice model. In this study, we analyze the factors that people consider when moving in Sejong City and learn movement patterns using XGboost, which is applicable to travel mode choice models. Based on this, we develop a travel mode selection model using personal properties (i.e., age, gender, driver"s license, occupation, purpose of travel, and travel distance).