• International Journal of Contents
• /
• 제17권4호
• /
• pp.79-90
• /
• 2021

With the deepening of population aging, pension has become an urgent problem in most countries. Community smart pension can effectively resolve the problem of traditional pension, as well as meet the personalized and multi-level needs of the elderly. To predict the pension intention of the elderly in the community more accurately, this paper uses the decision tree classification method to classify the pension data. After missing value processing, normalization, discretization and data specification, the discretized sample data set is obtained. Then, by comparing the information gain and information gain rate of sample data features, the feature ranking is determined, and the C4.5 decision tree model is established. The model performs well in accuracy, precision, recall, AUC and other indicators under the condition of 10-fold cross-validation, and the precision was 89.5%, which can provide the certain basis for government decision-making.

• 한국경영과학회지
• /
• 제40권2호
• /
• pp.19-29
• /
• 2015

The purpose of this study is to suggest the grid search method for selecting an optimal decision tree model. It chooses optimal values for the maximum depth of tree and the minimum number of observations that must exist in a node in order for a split to be attempted. Therefore, the grid search method guarantees building a decision tree model that shows more precise and stable classifying performance. Through empirical analysis using data of job satisfaction of workplace reserve force commanders, we show that the grid search method helps us generate an optimal decision tree model that gives us hints for the improvement direction of labor conditions of Korean workplace reserve force commanders.

• 한국건축시공학회지
• /
• 제23권6호
• /
• pp.851-862
• /
• 2023

본 연구에서는 감영 복구사업 계획 수립을 위한 의사결정지원 모델 개발을 통해 감영의 우선 복원대상 건물 선정 업무에 객관적이고 체계적인 검토기준을 마련하였다. 제안된 모델은 문헌고찰을 통한 평가항목 도출과 이를 정립하기 위해 델파이 방법을 적용하였으며, 평가항목 가중치 부여를 위한 AHP 설문을 수행하였다. 도출된 지원모델은 복원대상 건물들 간의 상대적인 우선순위를 평가 항목별로 점수화하여 표현함으로써 복원예산 편성 시 합리적인 의사결정을 가능하게 하였다. 사례연구를 통해 본 연구에서 제안한 모델의 적용성을 검증하였다. 전문가 자문결과 본 연구의 모델이 실제 복원사업의 계획수립시 유용하게 활용될 수 있을 것으로 나타났다.

• 한국경영과학회지
• /
• 제3권1호
• /
• pp.81-91
• /
• 1978

Decision models are an attempt to reduce uncertainty in the decision making process. The models describe the relationships of variables and given proper input data generate solutions to managerial problems. These solutions may not be answers to the problems for one of two reasons. First, the data input into the model may not be consistant with the underlying assumptions of the model being used. Frequently parameters are assumed to be deterministic when in fact they are probabilistic in nature. The second failure is that often the decision maker recognizes that the data available are not appropriate for the model being used and begins to collect the required data. By the time these data has been compiled the solution is no longer an answer to the problem. This relates to the timeliness of decision making. The authors point out throught the use of an illustrative problem that stocastic models are well developed and that they do not suffer from any lack of mathematical exactiness. The primary problem is that generally accepted procedures for data generation are historical in nature and not relevant for probabilistic decision models. The authors advocate that management information system designers and accountants must become more familiar with these decision models and the input data required for their effective implementation. This will provide these professionals with the background necessary to generate data in a form that makes it relevant and timely for the decision making process.

• 한국공작기계학회논문집
• /
• 제18권1호
• /
• pp.59-67
• /
• 2009

The purpose of this study is to provide a decision support to select an appropriate layered manufacturing(LM) machine that suits the application of a part. Selection factors include concept model, form/fit/functional model, pattern model far molding, material property, build time and part cost that greatly affect the performance of LM machines. However, the selection of a LM is not an easy decision because they are uncertain and vague. For this reason, the aim of this research is to propose hybrid multiple attribute decision making approaches to effectively evaluate LM machines. In addition, because subjective considerations are relevant to selection decision, a fuzzy logic approach is adopted. The proposed selection procedure consists of several steps. First, we identify LM machines that the users consider After constructing the evaluation criteria, we calculate the weights of the criteria by applying the fuzzy Analytic Hierarchy Process(AHP) method. Finally, we construct the fuzzy Technique of Order Preference by Similarity to Ideal Solution(TOPSIS) method to achieve the ranking order of all machines providing the decision information for the selection of LM machines.

• 한국경영과학회:학술대회논문집
• /
• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
• /
• pp.742-748
• /
• 1994

Scientific involvement in complex decision-making systems, characterized by multicriteria phenomena and fuzziness inherent in the structure of information, requires suitable methods. Especially, when powerful dependent criteria are introduced, the systems are become more complex. This paper presents a fuzzy dependence relation model for this kind of multicriteria decision-making. The model we propose is based on fuzzy relation in fuzzy system theory. For the application of the model a numerical example is quoted.

• 한국경영과학회:학술대회논문집
• /
• 한국경영과학회 2003년도 추계학술대회 및 정기총회
• /
• pp.25-28
• /
• 2003

The $\ulcorner$CIDEAR$\lrcorner$ model proceeds the following six steps : $\ulcorner$Decision Theory ＆ Evaluation Model$\lrcorner$, $\ulcorner$AR Decision ＆ Evaluation Model$\lrcorner$, $\ulcorner$Resource ＆ Performance Analysis Model$\lrcorner$, $\ulcorner$Cross Impact Assumption Model$\lrcorner$, $\ulcorner$Priority Oder Decision Model$\lrcorner$, and $\ulcorner$Efficiency Cause Analysis Model$\lrcorner$ - In this study, twenty-one R＆D projects of a leading company in electronic industry are selected to examine the usefulness of the constructed $\ulcorner$CIDEAR$\lrcorner$ model. Simulation method, Excel, Lindo(Linear Interactive and Discrete Optimizer) and Team EC are used in this case study.

• 대한안전경영과학회지
• /
• 제3권4호
• /
• pp.145-154
• /
• 2001

This paper deals with a network model for the efficient transportation of post and consists of the formulation based on the network model and the LINGO programming model including the operations of the post transportation. This network model is represented by using Time Space Network. The generalized formulation is built up with the input variables and the decision variables, which are defined on the basis of the network model. And LINGO programming model to be proposed with DB and LINGO is constructed in consideration of how to manage the post transportation and the intermodal transport. The results of the model implementation were represented on Time Space Network and they are analyzed and verified. The LINGO programming model is used as the module to be set in application software. Specifically with using GEOmania, GIS tool, the LINGO Model is applied to develop the application for Mail Transportation Decision Support System.

• 한국산학기술학회논문지
• /
• 제10권5호
• /
• pp.1060-1064
• /
• 2009

본 논문에서는 수도권 대학생을 대상으로 한 Fuzzy AHP 기반의 이동통신사 신정을 위한 의사결정모델을 제안한다. 고객이 이동통신사를 선택할 때 이동통신사의 수많은 경쟁적인 그리고 상호보완적인 요소들로 인해 의사결정을 내리는 것이 용이하지 않다. 고객이 최적의 이동통신사를 결정하기 위해서는 요금, 다양한 서비스, 부가 기능 등과 같은 서로 다른 정량적 그리고 정성적 요소들을 고려할 필요가 있다. 본 연구에서는 대학생들이 고객으로서 이동통신사를 선정할 때 다양한 정량적 그리고 정성적 요소들을 고려할 수 있는 퍼지 AHP 기반의 모델을 제안한다. 특히, 퍼지 이론은 불활성설과 애매모호한 문제를 다루기 위해 적용되고 또한 정량적 요인들을 피지수로 바꿀 수 있는 선형정규화 모델을 개발한다. 수도권 대학생들을 대상으로 한 사례 연구는 제안 모델의 유용성과 고객들이 이익을 위해 보다 나은 의사결정을 내릴 수 있도록 도와줄 수 있음을 보여준다.

• International Journal of Computer Science & Network Security
• /
• 제23권7호
• /
• pp.71-78
• /
• 2023

Employee turnover is one of the most important challenges facing modern organizations. It causes job experiences and skills such as distinguished faculty members in universities, rare-specialized doctors, innovative engineers, and senior administrators. HR analytics has enhanced the area of data analytics to an extent that institutions can figure out their employees' characteristics; where inaccuracy leads to incorrect decision making. This paper aims to develop a novel model that can help decision-makers to classify the problem of Employee Turnover. By using feature selection methods: Information Gain and Chi-Square, the most important four features have been extracted from the dataset. These features are over time, job level, salary, and years in the organization. As one of the important results of this research, these features should be planned carefully to keep organizations their employees as valuable assets. The proposed model based on machine learning algorithms. Classification algorithms were used to implement the model such as Decision Tree, SVM, Random Frost, Neuronal Network, and Naive Bayes. The model was trained and tested by using a dataset that consists of 1470 records and 25 features. To develop the research model, many experiments had been conducted to find the best one. Based on implementation results, the Neural Network algorithm is selected as the best one with an Accuracy of 84 percents and AUC (ROC) 74 percents. By validation mechanism, the model is acceptable and reliable to help origination decision-makers to manage their employees in a good manner.