• 한국컴퓨터정보학회논문지
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• 제8권1호
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• pp.156-165
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• 2003

본 연구에서는 기업 내 정보시스템을 구축하는 과정에 있어 의사결정자들을 지원하기 위한 가능성분포 의사결정모형을 검토하였다. 급변하는 기업환경에 영향을 미치는 주요 변수중의 하나인 정보기술의 활용은 성공적인 기업활동에 중요한 역할을 하며, 아웃소싱할 경우 조직의 목표와 자원의 제약을 충분히 반영하는 벤더의 선정이 매우 중요하다 할 수 있다. 이를 위해 수리적 모형이 제시되어졌으며 가능성 이론을 적용한 모형이 제시되었다. 일반적 수리모형의 경우 투입되는 변수의 불명확성과 의사결정자의 열망수준을 반영하지 못한다는 단점이 있으며, 확률적 분포 모형의 경우 현실적 적용에 있어 어려움이 있어왔다. 가능성분포 의사결정모형에서는 투입변수의 불명확성이 고려된 다목적의사결정모형의 구축이 가능하였다.

• 한국컴퓨터정보학회논문지
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• 제9권4호
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• pp.203-214
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• 2004

본 연구에서는 정보시스템 프로젝트 계획모형에서의 가능성 계획법 적용을 제시하고 있다. 정보시스템 프로젝트 계획모형에서 관리자와 의사결정자들은 불명확한 정보나 주관적 견해로 인해 흔히 일정계획, 비용, 성능(품질)과 같은 모수의 추정에서 많은 어려움을 겪게 된다. 이러한 경우 가능성 의사결정모형은 현실적 의사결정모형에서 적용 가능한 해법을 제시할 수 있게 된다. 본 연구에서 적용된 CPM은 프로젝트가 완료되는 시점을 결정하고 프로젝트를 수행하기 위해 각 활동들의 수행시작 일정을 수립하는 데 목적이 있는 기법이다. 가능성 계획법을 적용한 CPM(Critical Path Method)에서는 의사결정자의 열망수준, 의사결정상의 애매함을 고려할 수 있으며, 계산상의 효율성도 제고할 수 있다. 본 모형은 GINO을 이용하여 해를 구하였으며, 수치 예와 해가 제시되어졌다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.506-511
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• 1998

In this paper we consider multiobjective linear programming problems with coefficients of the objective functions specified by possibility distributions. Possibly and necessarily efficient solution sets are defined as funny solution sets whose membership grades represent possibility and necessity degrees to which a feasible solution is efficient. Considering efficiency condition and its dual condition in ordinary multiobjective linear programming problem, we propose efficiency test methods based on an extreme ray generation method. Since the proposed methods can be put in the part of a bi-section method, we can develop calculation and methods of the degree of possible and necessary efficiency for feasible solutions.

• 산업경영시스템학회지
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• 제31권2호
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• pp.51-60
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• 2008

A solution method for fuzzy linear programs is proposed. A fuzzy linear program is converted to a crisp linear program with average indices being applied to the objective function and constraints. A comparative analysis between the proposed average index approach and the possibilistic approach is given. As an application example, the proposed method is applied to the linear programming model for fuzzy data envelopment analysis, and the result is compared with that of the possibilistic approach.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2004년도 학술발표논문집
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of applied mathematics & informatics
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• 제42권4호
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• pp.831-854
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• 2024

The transportation problem (TP) is one of the earliest and the most significant implementations of linear programming problem (LPP). It is a specific type of LPP that mostly works with logistics and it is connected to day-to-day activities in our everyday lives. Nowadays decision makers (DM's) aim to reduce the transporting expenses and simultaneously aim to reduce the transporting time of the distribution system so the bi-objective transportation problem (BOTP) is established in the research. In real life, the transportation parameters are naturally uncertain due to insufficient data, poor judgement and circumstances in the environment, etc. In view of this, neutrosophic bi-objective transportation problem (NBOTP) is introduced in this paper. By introducing single-valued trapezoidal neutrosophic numbers (SVTrNNs) to the co-efficient of the objective function, supply and demand constraints, the problem is formulated. The DM's aim is to determine the optimal compromise solution for NBOTP. The extended weighted possibility mean for single-valued trapezoidal neutrosophic numbers based on [40] is proposed to transform the single-valued trapezoidal neutrosophic BOTP (SVTrNBOTP) into its deterministic BOTP. The transformed deterministic BOTP is then solved using the dripping method [10]. Numerical examples are provided to illustrate the applicability, effectiveness and usefulness of the solution approach. A sensitivity analysis (SA) determines the sensitivity ranges for the objective functions of deterministic BOTP. Finally, the obtained optimal compromise solution from the proposed approach provides a better result as compared to the existing approaches and conclusions are discussed for future research.