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본 논문의 연구목적은 공급망에서 퍼지 의사결정방법을 적용하여 공급업체 선정문제를 해결하는것이다. 일반적으로 공급업체 선정을 위해서는 품질, 가격, 납기, 공급업체와의 관계 등과 같은 다양한 양적,질적 변수들이 고려되어야한다. 본 논문에서는 위와 같은 변수들을 측정하고 가중치를 부여하기 위해서 언어적 변수를 사용하였으며, 이러한 언어적 측정값은 사다리꼴이나 삼각형 퍼지 수로 나타낼 수 있다. 공급업체 선정문제의 평가기준에 대한 가중치를 도출하고 의사결정자들의 공급업체 평가결과를 통합하기 위해서,퍼지 집합이론과 TOPSIS에 기반을 둔 계층형의 다기준 의사결정모형을 제안하였다. 4가지의 평가기준, 4개 공급업체, 3명의 평가자로 이루어진 사례회사의 공급업체 선정문제에 이 방법을 적용하는 절차를 단계적으로 제시하였다. 결론적으로 본 연구에서 제안한 모형은 공급업체 선정문제에 매우 적합한 것으로 나타났다.


Decision-making problem is the process of finding the best option from all of the feasible alternatives. In almost all such problems the multiplicity of criteria for judging the alternatives is pervasive. That is, for many such problems, the decision maker wants to solve a multiple criteria decision making(MCDM) problem. In general, multi-criteria problems adhere to uncertain and imprecise data, and fuzzy set theory is adequate to deal with it. Since human judgments including preferences are often vague and cannot estimate his preference with an exact numerical value. In decision-making process,very often, the assessment of alternatives with respect to criteria and the importance weight are suitable to use the linguistic variables instead of numerical values. A linguistic decision process is proposed to solve the multiple criteria decision-making problem under fuzzy environment. This paper is aimed to present a fuzzy decision-making approach to deal with the supplier selection problem in supply chain system. During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of these decisions usually is complex and unstructured. Many quantitative and qualitative factors such as quality,price, delivery, and relationship with supplier must be considered to determine suitable suppliers. In this paper we have proposed a similarity aggregation method to aggregate fuzzy individual opinions into a fuzzy group consensus opinion, according to their consensus degree coefficient, in MCDM with group decision problems. We consider the difference of importance of each expert as a crisp value in our method. The degree of importance of each expert also can be represented by a linguistic variable; i.e. a fuzzy number. However, if the importance degree of each expert is a fuzzy number,then the aggregation method will not satisfy the consistency requirement. In fact, the fuzzy TOPSIS method is very flexible. According to the closeness coefficient, we can determine not only the ranking order but also the assessment status of all possible suppliers. Significantly, the proposed method provides more objective information for supplier selection and evaluation in supply chain system. The systematic framework for supplier selection in a fuzzy environment presented in this paper can be easily extended to the analysis of other management decision problems. However, improving the approach for solving supplier selection problems more efficiently and developing a group decision support system in a fuzzy environment can be considered as a topic for future research.