COMPARISON R AND CURLI METHODS FOR MULTI-CRITERIA DECISION MAKING

Abstract

When multi-criteria decision making, decision makers will expend significant effort in selecting a data normalization method and a weighting method. If a mistake is made in those choices, it will result in decisions that do not find the best solution. Furthermore, with qualitative criteria, it is impossible to standardize the data. Similarly, determining the weights of criteria will be difficult if the criteria are in qualitative form. R and CURLI are two multi-criteria decision-making methods that do not require data normalization or the use of additional weighting methods for the criteria. They are therefore well suited for ranking alternatives when the criteria are both quantitative and qualitative. This study compares the two methods through three examples from different fields. The results show that these two methods jointly determine the best solution in all three fields and are also suitable when using other decision-making methods that require data normalization and have high requirements using the method of determining the weights for the criteria.

Keywords

• mcdm
• r method
• curli method
• data normalization
• weight