A multi-criteria decision-making(MCDM) method allows the decision makers to systematically evaluate the alternatives based on a predefined set of decision criteria. The most commonly used MCDM methods include Analytic Hierarchy Process(AHP), Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS), Weighed Aggregated Sum Product Assessment(WASPAS), Preference Selection Index(PSI), etc. In MCDM Problems, it is common that performance ratings for different criteria are measured by different units. Normalization is thus used to convert performance ratings into commensurable data. There are many normalization techniques that can be used for MCDM problems. Much effort has been made for comparative studies on the suitability of normalization techniques used in MCDM methods. However, most studies present normalization methods suitable for specific MCDM problems based on specific data samples. Therefore, this study proposes the most suitable normalization method for each MCDM method under consideration using extensive data samples. A wide range of MCDM problems with various measurement scales are generated by simulation for comparative study. The experimental results show that vector normalization method is best suited for all MCDM methods considered in this study.

1. 서 론
2. 관련 연구
    2.1 다기준 의사결정 프로세스와 방법
    2.2 다기준 의사결정 방법과 정규화 방법의 적합성
3. 다기준 의사결정 방법에 대한 정규화 방법의 시뮬레이션 기반 성능 비교·분석
    3.1 시뮬레이션 및 성능평가 방법
    3.2 결과
    3.3 성능 비교·분석
4. 결 론
References

• Jong-Phil Park(Department of Mechanical Engineering, Hanbat National University) | 박종필 (국립한밭대학교 기계공학과)
• Sang-Heon Lee(Department of Mechanical Engineering, Hanbat National University) | 이상헌 (국립한밭대학교 기계공학과)
• Yoon-Eui Nahm(Department of Mechanical Engineering, Hanbat National University) | 남윤의 (국립한밭대학교 기계공학과) Corresponding author