## 2 Literature Review
[43] assessed the 2014 performance of OECD nations through the application of CRITIC, SAW, and Peters' fuzzy regression models and found discrepancies between the results from [PERSON] FLR model and those obtained from other MCDM methods. In addition, ref. [44] evaluated the performance of Central and Eastern European (CEE) countries in 2018 by employing the Statistical Variance and MABAC methods, with the Czech Republic and Poland identified as the leading performers. In their study, ref. [12] analyzed OECD countries' performance over the period from 2010 to 2018 with the fuzzy AHP-ARAS-G model, identifying Germany as the top performerand Latvia as the lowest. In another example, ref. [4] used the BWM method to evaluate the significance of six logistics performance criteria set by the World Bank, concluding that \"Infrastructure\" was the most important and \"Tracking and Tracing\" the least. Another study, [45], employed the grey SWARA-MOORA model to evaluate the logistics performance of transition economy countries, identifying \"Infrastructure\" as the top criterion and Serbia as the best-performing nation according to the grey MOORA results. Meanwhile, ref. [2] assessed the logistics efficiency of 141 nations over the period 2007 to 2014 using DEA, ranking Belgium first and Somalia last. Additionally, ref. [46] used the fuzzy AHP model to evaluate intermodal transport logistics performance in Thailand during 2005-2006, while [37] applied the MEREC-MARCOS model to assess the logistics performance of EU countries, with Germany ranking first. In their study, ref. [47] evaluated the logistics performance of EU countries and Turkey using the CRITIC-COPRAS model, identifying the Netherlands as the highest-ranked and Latvia as the lowest-ranked country. Furthermore, ref. [48] analyzed logistics performance in EU countries from 2007 to 2023 using LPI data announced by the World Bank, and [49] proposed an integrated approach using fuzzy c-means clustering (FCM) and MCDM to evaluate the logistics performance of 160 countries worldwide. Countries were clustered based on income groups considering per capita gross domestic product (GDP) and ranked using SAW, TOPSIS, MOORA, and ARAS methods with 2018 LPI data. The results were consistent with the LPI rankings published by the World Bank. In another study, ref. [40] determined the logistics performance of EU countries using 11 MCDM techniques across 33 indicators. Criteria weights were established using the Genetic Algorithm (GA), and different MCDM rankings were integrated using the Copeland method. It was found that the correlation between the actual LPI scores and the genetic algorithm results was higher compared to other weighting techniques (CRITIC, Entropy, and equal weight). Meanwhile, ref. [50] assessed the logistics performances of Turkey and EU countries using the Entropy-EDAS model, identifying Germany, Sweden, and Denmark as the top three performing countries. Similarly, ref. [51] evaluated the logistics performances of EU member states and 5 EU candidate countries using the COPRAS-G method, with Germany, the Netherlands, and Sweden ranking prominently. Finally, in their study evaluating the logistics performances of OECD countries, ref. [52] concluded that the countries with the highest logistics performance, according to the SWARA-EDAS model, were Germany, The Netherlands, and Sweden.