• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.113-118
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• 2002

Research and Development(R&D) was once considered to be a unique, creative and unstructured process that was difficult, if not impossible, to manage and control. R&D decisions impact the entire enterprise. Therefore, decisions must not be based solely on R&D's perception of what is important or worthwhile. R&D contributions are difficult to measure separately from other functional organizations such as manufacturing and marketing. While some firms are attempting to overcome perceived limitations in traditional accounting-based performance measures using ROI, EVA, others are embracing the use of non-financial measures for decision making and performance evaluation. In particular, many firms are implementing 'Balanced Scorecard(BSC)' systems that supplement traditional accounting measures with non-financial measures focused on at least three other perspectives-customers, internal business processes, and learning and growth. AHP is a popular multi-attribute decision making model that allows for the development of importance rankings. The AHP has been applied in a wide variety of practical settings to model complex decision problems. The former, determine Perspectives and the Key Performance indicator(KPI) through the former research, the latter compose the questionnaire for determine the weight of perspectives and KPIs. And then, make a survey with researchers about 4 perspectives and 18 KPIs. The results will be simulate with Expert Choice 2000 for determine the weights. This results helps establish the firm's business strategy and technology strategy The firm should establish the business strategy to consider market position, business growth potential, and technological capabilities.

• 대한기계학회논문집A
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• 제38권6호
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• pp.677-682
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• 2014

본 연구에서는 데이터 마이닝의 방법인 의사결정나무와 인공신경망을 이용하여 리튬 이차전지의 전류밀도 특성에 대해 핵심 설계 인자를 도출하고 비교하였다. 먼저 의사결정나무-인공신경망 모델을 이용한 설계방법으로, 비선형성을 나타내는 초기 극판 설계인자들 중에 의사결정나무 모델을 통해 주요 설계 인자를 도출한 다음 인공신경망을 이용하여 설계인자들 간의 중요도와 전류밀도와의 가중치 분석을 수행하였다. 두 번째 방법은 인공신경망 모델만을 이용한 방법으로, 초기 설계인자들을 별도의 주요 인자 도출 과정 없이 모두 인공신경망을 구축하는데 사용하여 전류밀도와의 연관성 및 가중치를 분석하였다.

• 지능정보연구
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• 제17권4호
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• pp.1-17
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• 2011

오늘날 글로벌한 경쟁에서 고객의 만족도를 유지하고 생산성과 효율을 높이기 위해서 대량맞춤(mass customization)이 많은 선도기업에서 채택되고 있다. 대량맞춤은 제품가족과 제품 플랫폼에 근거하여 기업으로 하여금 새로운 제품을 유연하고 효율적이며 고객 요구에 신속히 대응하는 것을 용이하게 한다. 따라서 제품 플랫폼에 기반한 제품가족 전략이 대량맞춤을 실현화하는데 적절한 방법이다. 제품가족이란 다양한 시장의 요구를 충족하기 위해서 공통의 특성, 구성부품, 서브 시스템을 공유하는 일련의 유사한 제품군으로 정의된다. 이 연구의 목표는 제품가족 설계 전략을 이용하여 고객의 요구를 충족시키는 제품의 구성부품간의 우선순위를 찾아내고자 하는 것이다. 신 제품 개발을 위한 의사결정 과정은 피드백을 가지는 다 변량 의사결정 모형을 필요로 한다. 이를 위해서 분석적 네트워크 과정(analytic network process) 방법을 이용하여 의사결정 모델과 절차를 수립하였다. 구현을 위해서 제품가족 모델에 적합한 소형 PC인 넷북 제품을 선정하고, 각 제품가족에 대한 구성부품에 대하여 제안된 방법에 따라서 우선순위를 도출하였다. 구현결과를 QFD 모델을 이용하여 고객요구사항과 구성부품간의 관계를 분석하고 평가하였다.

• 한국환경복원기술학회지
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• 제21권2호
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• pp.39-51
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• 2018

Long-term unexecuted urban parks will be released from urban planning facilities after 2020, this may result in development of those parks. However, little research have been focused on how to develop those parks considering conservation, development, spatial pattern, and so on. Therefore, in this study, we suggested an optimization planning model that minimizes the fragmentation while maximizing the conservation and development profit using ACO (Ant Colony Optimization). Our study area is Suwon Yeongheung Park, which is long-term unexecuted urban parks and have actual plan for private development in 2019. Using our optimization planning model, we obtained four alternatives(A, B, C, D), all of which showed continuous land use patterns and satisfied the objectives related to conservation and development. Each alternative are optimized based on different weight combinations of conservation, development, and fragmentation, and we can also generated other alternatives immediately by adjusting the weights. This is possible because the planning process in our model is very fast and quantitative. Therefore, we expected our optimization planning model can support "spatial decision making" of various issue and sites.

• 경영과학
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• 제13권3호
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• pp.75-90
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• 1996

This paper presents an improved method of the Analytic Hierarchy Process (AHP) when customers are to select the best automobile purchase models. In order to support group decisions and as a different procedure of the conventional AHP, we combine the AHP model with a spreadsheet model that applies the Likert's rating scheme to each alternative. We only consider individual pairwise comparison matrices where the consistency ratio (C.R.) is less than or equal to 0.2. Finally, we regard the weight of each decision maker as a reciprocal number of C.R. As a case study we prioritize three passenger cars of medium size in the domestic market. The major evaluation criteria include:exterior or interior features, performance, safety, pricing, salesman, and after service.

• 산업공학
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• 제24권2호
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• pp.128-138
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• 2011

This research proposes a resource allocation model of Data QC (Quality Control) activities using COQ (Cost of Quality). The model has been developed based on a series of research efforts such as COQ classifications, weight determination of Data QC activities, and an aggregation approach between COQ and Data QC activities. In the first stage of this research, COQ was divided into the four typical classifications (prevention costs, appraisal costs, internal failure costs and external failure costs) through the opinions from five professionals in Data QC. In the second stage, the weights of Data QC activities were elicited from the field professionals. An aggregation model between COQ and Data QC activities has been then proposed to help the practitioners make a resource allocation strategy. DEA (Data Envelopment Analysis) was utilized for locating efficient decision points. The proposed resource allocation model has been validated using the case of Korea national defense information system. This research is unique in that it applies the concept of COQ to the data management for the first time and that it demonstrates a possible contribution to a real world case for budget allocation of national defense information.

• 한국지능시스템학회논문지
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• 제15권4호
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• pp.443-450
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• 2005

본 논문에서는 웹 사용자의 선호도를 추출하기 위한 지능적 모델을 제안하고 이에 대한 평가결과를 제시한다. 이를 위해 현재 정보검색엔진의 문제점을 분석하고, 선호도 가중치를 학습기에 반영한다. 이것은 키워드에 의한 단어별 빈도수에 의존하지 않고 지능적으로 사용자의 행동유형을 학습하게 함으로써 질의에 대한 결과집합을 사용자의 의도에 맞게 제공하는 메커니즘이다. 다음으로 선호도 유행성에 대한 개념과 고려요소를 제안하며, 선호도 추출 알고리즘과 이에 대한 예를 제시한다. 또한 행동유형 추출을 위한 지능모델을 설계하고 HTML 색인과 선호도 결정 지능학습과정을 제안한다. 마지막으로 선호도를 적용한 후의 문서 랭킹 측정결과를 비교함으로써 본 논문에서 제안한 모델의 타당성을 검증한다.

• 한국건설관리학회논문집
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• 제16권4호
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• pp.50-59
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• 2015

한국 건설 기업들의 해외 진출이 기하급수적으로 늘어나고 있지만 프로젝트를 수행함에 있어 사업의 수익률은 대기업과 경험이 부족한 중소기업을 비교하였을 때 큰 차이가 나타난다(대기업 5건 중 1건 적자, 중소기업 3건 중 1건 적자 공사). 또한 경험이 부족한 중소, 중견 기업들, 특히 하도급 업체에게는 프로젝트 참여시 사업의 적절성을 판단하기란 어려우며 그에 따른 수익률 또한 예측하기 어렵다. 이에 본 연구는 중소/중견 업체, 특히 하도급 업체 관점에서 해외 건설공사 진출 시 수익률에 영향을 미치는 영향인자를 도출하기 위해 1965년부터 시행된 8,637건의 해외건설 준공데이터 및 문헌고찰 기반으로 수익률에 영향을 미치는 10개 인자를 도출 후 다중회귀분석을 통해 영향인자 간 가중치를 도출하였다. 이를 기반으로 사례기반 추론 기법을 이용하여 수익률 예측 모델을 개발하였으며, Type1 &Type2 error 분석을 통해 검증 결과 11%의 오차율을 보였다. 이러한 수익성 예측 모델을 활용하여 국내 건설 하도급업체들은 해외건설공사 진출 시 해당 프로젝트의 수익성 분포를 사전에 확인하여 양질의 프로젝트를 선별하고, 사업 참여의 의사결정에 중요한 참고자료가 될 것을 기대한다.

• 정보기술응용연구
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• 제1권
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• pp.173-211
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• 1999

The task of classification permeates all walks of life, from business and economics to science and public policy. In this context, nonlinear techniques from artificial intelligence have often proven to be more effective than the methods of classical statistics. The objective of knowledge discovery and data mining is to support decision making through the effective use of information. The automated approach to knowledge discovery is especially useful when dealing with large data sets or complex relationships. For many applications, automated software may find subtle patterns which escape the notice of manual analysis, or whose complexity exceeds the cognitive capabilities of humans. This paper explores the utility of a collaborative learning approach involving integrated models in the preprocessing and postprocessing stages. For instance, a genetic algorithm effects feature-weight optimization in a preprocessing module. Moreover, an inductive tree, artificial neural network (ANN), and k-nearest neighbor (kNN) techniques serve as postprocessing modules. More specifically, the postprocessors act as second0order classifiers which determine the best first-order classifier on a case-by-case basis. In addition to the second-order models, a voting scheme is investigated as a simple, but efficient, postprocessing model. The first-order models consist of statistical and machine learning models such as logistic regression (logit), multivariate discriminant analysis (MDA), ANN, and kNN. The genetic algorithm, inductive decision tree, and voting scheme act as kernel modules for collaborative learning. These ideas are explored against the background of a practical application relating to financial fraud management which exemplifies a binary classification problem.

• Animal Bioscience
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• 제37권5호
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• pp.817-825
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• 2024

Objective: The aim of this study was to identify suitable polynomial regression for modeling the average growth trajectory and to estimate the relative development of the rib eye area, scrotal circumference, and morphometric measurements of Guzerat young bulls. Methods: A total of 45 recently weaned males, aged 325.8±28.0 days and weighing 219.9±38.05 kg, were evaluated. The animals were kept on Brachiaria brizantha pastures, received multiple supplementations, and were managed under uniform conditions for 294 days, with evaluations conducted every 56 days. The average growth trajectory was adjusted using ordinary polynomials, Legendre polynomials, and quadratic B-splines. The coefficient of determination, mean absolute deviation, mean square error, the value of the restricted likelihood function, Akaike information criteria, and consistent Akaike information criteria were applied to assess the quality of the fits. For the study of allometric growth, the power model was applied. Results: Ordinary polynomial and Legendre polynomial models of the fifth order provided the best fits. B-splines yielded the best fits in comparing models with the same number of parameters. Based on the restricted likelihood function, Akaike's information criterion, and consistent Akaike's information criterion, the B-splines model with six intervals described the growth trajectory of evaluated animals more smoothly and consistently. In the study of allometric growth, the evaluated traits exhibited negative heterogeneity (b<1) relative to the animals' weight (p<0.01), indicating the precocity of Guzerat cattle for weight gain on pasture. Conclusion: Complementary studies of growth trajectory and allometry can help identify when an animal's weight changes and thus assist in decision-making regarding management practices, nutritional requirements, and genetic selection strategies to optimize growth and animal performance.