Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

Most of the open-source decision tree algorithms are based on three splitting criteria (Entropy, Gini Index, and Gain Ratio). Therefore, the advantages and disadvantages of these three popular algorithms need to be studied more thoroughly. Comparisons of the three algorithms were mainly performed with respect to the predictive performance. In this work, we conducted a comparative experiment on the splitting criteria of three decision trees, focusing on their interpretability. Depth, homogeneity, coverage, lift, and stability were used as indicators for measuring interpretability. To measure the stability of decision trees, we present a measure of the stability of the root node and the stability of the dominating rules based on a measure of the similarity of trees. Based on 10 data collected from UCI and Kaggle, we compare the interpretability of DT (Decision Tree) algorithms based on three splitting criteria. The results show that the GR (Gain Ratio) branch-based DT algorithm performs well in terms of lift and homogeneity, while the GINI (Gini Index) and ENT (Entropy) branch-based DT algorithms performs well in terms of coverage. With respect to stability, considering both the similarity of the dominating rule or the similarity of the root node, the DT algorithm according to the ENT splitting criterion shows the best results.

In this study, we divided the process operation scenarios into three categories based on raw water temperature and turbidity. We will select and operate the process operation scenario according to the characteristics of the raw water. The number of algae in the DAF treated water has been analyzed to be less than 100 cells/mL. These results indicated that the DAF process is effective in removing the algae. In addition, the scenario of the integrated management decision algorithm of the DAF process was developed. DAF pilot plants (500 m3/day) process has shown a constantly sound performance for the treatment of raw water, yielding a significantly low level of turbidity (DAF treated water, 0.21~1.56 NTU).

The consequences of rapid industrial advancement, diversified types of business and unexpected industrial accidents have caused a lot of damage to many unspecified persons both in a human way and a material way Although various previous studies have been analyzed to prevent industrial accidents, these studies only provide managerial and educational policies using frequency analysis and comparative analysis based on data from past industrial accidents. The main objective of this study is to find an optimal algorithm for data analysis of industrial accidents and this paper provides a comparative analysis of 4 kinds of algorithms including CHAID, CART, C4.5, and QUEST. Decision tree algorithm is utilized to predict results using objective and quantified data as a typical technique of data mining. Enterprise Miner of SAS and AnswerTree of SPSS will be used to evaluate the validity of the results of the four algorithms. The sample for this work chosen from 19,574 data related to construction industries during three years (2002~2004) in Korea.

최근 대규모 지진으로 내진 성능 및 보강에 대한 정부와 국민들의 관심이 높아지고 있으며, 재난관리 시장이 사후복구에서 사전예방중심으로 재편됨에 따라 시설물의 물리적인 상태등급만을 고려하는 기존의 결정론적 내진성능관리 의사결정기술에 지진의 불확실성 및 사회·경제적 판단요소와 도로망 시설물의 네트워크 시스템을 고려한 확률론적 내진성능관리 체계 확립이 필요하다. 결정론적 방법에 의한 도로시설물 내진성능관리 우선순위는 물리적인 상태등급에 따라 결정되며, 사회·경제적 판단요소가 결여되어 있어 전체적인 판단을 할 수 있는 근거를 제공하지 못하고 있는 실정이다. 도로시설물 내진성능관리 우선순위에 대한 의사결정을 위해서는 도로의 중요도, 노후거동특성, 부재 내진성능, 지진취약도 함수, 내진보수보강효과, 지진복원력 등과 같이 도로시설 및 도로망에 직·간접 적으로 가해지는 물리적, 사회적, 경제적 영향을 평가할 수 있는 의사결정지원 기술이 부재한 실정이다. 따라서 노후도에 따라 지진취약도가 크고 재난 발생 시 파급영향이 큰 도로시설물과 도로망을 식별하기위한 합리적이고 체계적인 내진성능 관리 핵심기술개발이 필요하며, 본 논문에서는 연구단 과제를 통해서 개발예정인 “노후도로망의 내진성능관리 의사결정 알고리즘”을 소개하고자 한다.

선박 침수 사고의 경우, 선박의 운용 책임자가 취할 수 있는 대응방안이 한정되어 있어 정확한고 신속한 의사결정을 위해서는 기존의 안전관련 시스템을 활용한 효율적인 의사결정 지원 시스템이 필요하다. 수밀 및 준수밀 문, 격벽 밸브, 배수 펌프 등과 같이 침수 사고 시작동하는 대부분의 시스템들은 침수가 선박 전체로 전파되는 것을 막도록 충분한 구획분할 정도를 확보하는데 목적이 있다. 침수 시나리오가 파국적이지 않다고 가정하더라도 발라스트 탱크의 사용은 침수 전파 방지와 선박 안정성을 향상하기 위한 매우 효과적인 방안이 될 수 있다. 본 논문에서는 침수 손상 시 최적의 대응방안을 위해 채워져야 하는 발라스트 탱크들을 선정하고, 각 발라스트 탱크의 수위를 결정하는 최적화 알고리즘을 기술한다.