An integrated MCDM approach using double normalization: introducing the DN-WENSLO and DN-RPEM methods for socio-economic performance evaluation
本文提出了两种新的多准则决策方法（DN-RPEM和DN-WENSLO），用于评估多个国家的社会经济绩效，其中瑞典排名第一，巴基斯坦排名最后，并验证了方法的稳健性。
This paper presents two significant additions to the scope of multi-criteria decision-making (MCDM) to assess socio-economic performance across various countries. The current study employed the Double Normalization Root-Proximity Evaluation Method (DN-RPEM), a refined integration approach. Furthermore, it was the first to implement the Double Normalization WENSLO (Weights by envelope and slope) (DN-WENSLO). With an architecture that integrates the RAM and the PIV approaches, the DN-RPEM is an integrated MCDM method for evaluating relative socio-economic performance in multiple countries. This integrated framework applies a linear and non-linear normalization approach to ensure the accuracy and validity of the decisions. At the same time, the DN-WENSLO method provides an advanced way to calculate criteria weights using weighted double normalization. Among the criteria, Unemployment, youth total (UYT) received the highest weight (0.1396), emphasizing its critical impact. At the same time, the mortality rate under 5 (MR) was assigned the lowest weight (0.0740), representing its relatively diminished influence in the analysis. Using the DN-RPEM method, Sweden was ranked as the top performer 1st rank (0.8131), while Pakistan was ranked as the least effective 12th rank (0.6415) among the evaluated countries. The DN-RPEM method outperforms TOPSIS, MOORA, CODAS, ARAS, WSM, and WASPAS in solving some of the best multi-criteria decision problems. Sensitivity analysis shows how rigid and flexible the DN-RPEM and DN-WENSLO methods are relevant to distinct case-specific situations, showing that they can be used for more than just socio-economic assessments.