다구찌 기법을 적용한 다기준 의사결정 모형 - MADM 중심으로 -

서지정보

ㆍ발행기관 : 한국산업경영시스템학회 ㆍ수록지정보 : 한국산업경영시스템학회 학술대회
ㆍ저자명 : 허준영, 서장훈, 박명규

목차

Abstract
1. 서 론
1.1 연구 목적
1.2 연구 방법 및 체계
2. 이론적 고찰
2.1 평가요소 가중치 계산방법
3. 손실함수와 SN비를 이용한 의사결정모형
3.1 SN비를 이용한 정성적요소 평가
3.2 손실함수를 이용한 정량적 평가
3.3 평가요소 평활계수 설정
4. 결 론
참 고 문 헌

영어 초록

Finding an optimal solution in MADM(Multi-Attribute Decision-Making) problems is difficult, when the number of alternatives, or that of attributes is relatively large. Most of the existing mathematical approaches arrive at a final solution on the basis of many unrealistic assumptions, without reflecting the decision-maker's preference structure exactly. In this paper we suggest a model that helps us find a group consensus without assessing these parameters in specific cardinal values. Therefore, This research provides a comprehensive Decision Making of the theory and methods applicable to the analysis of decisions that involve risk and multiple criteria attributes. after, The emphasis of the procedure will be on developments from the fields of decisions analysis and utility theory of Taguchi Method. This theoretical development will be illustrated through the discussion of several real-world application and a case study. When the multiple number of decision makers are involved in the decision making procedure, the problem of uncertainties invariably occurs, because of the different views between them. In this paper, New decision making model using Taguchi Method is applied to effectively model the multi-attribute-decision making(MADM) procedure in the uncertainties dominated two area(quantitative and qualitative factors), Quantitative factors evaluation is used Loss Function of Taguchi, qualitative factors evaluation is used S₩N ratio by each specialist. thus it can be used for aiding of preferable alternative. as a result, We will be proved efficiency about New decision making model of applied Taguchi Method with Analytical presentation of all the expecting outcomes when a specific strategy or an alternative plan is selected under expecting future environment.

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