## 3 Results  
Of the 1139 studies that emerged from the search process, 649 duplicates were excluded, leaving 490 for evaluation. Of these, 319 were excluded, 171 studies were assessed for eligibility, and 149 studies were excluded for various reasons: published before 2015, lack of framing, not peer reviewed, mathematical models and stochastic models that do not specifically address reverse supply chains of solid waste (RSCSW) within the circular economy, studies about COVID-19, etc. The PRISMA flowchart is presented in Figure 1 to illustrate this process. Figure 1: PRISMA flowchart of study selection. All screening stages were performed by two independent reviewers. Databases: Scopus, WOS, IEEE, and Capes. Keywords: “waste”, “circular”, “MCDA”, “Al”. Inclusion: 2015–2024, peer-reviewed studies on MCDA/AI in RSCSW. Exclusion: non-peer-reviewed or out-of-scope studies (PRISMA Item 16a). After a thorough analysis of the entire text, 22 studies were included in the systematic review. The studies included in this review are summarized in Table 3, which presents the main characteristics of the studies (year, journal, title, methodology, and main contributions). \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Ref.** & **Author(s)** & **Year** & **Journal** & **Study Title** & **Methodology** & **Key Contributions** \\ \hline
[45] & [PERSON] et al. & 2024 & Applied Soft Computing & Preference learning based on adaptive graph neural network for multi-criteria decision support. & Graph neural networks (GNN) and TOPSIS. & Adaptive method with high accuracy and interpretability. \\ \hline
[46] & [PERSON] et al. & 2016 & Computers \& Enquierting & An integrated decision analytic framework of machine learning (SVM, ANN) and MCDM (AHP, VIKOR). & Combination of machine learning (SVM, VIKOR). & High accuracy in inventory classification with imbalanced data. \\ \hline
[47] & [PERSON] et al. & 2023 & Civil and Environmental Engineering & Optimization of Supply Chain Network in Solid Waste Management Using Genetic Algorithm and Fuzzy Logic. & Genetic algorithm and fuzzy logic. & Flexible model for waste management in Lagos, Nigeria. \\ \hline
[48] & [PERSON] et al. & 2023 & Environmental Science and Pollution Research & A sustainable green reverse logistics plan for plastic solid waste management using the TOPSIS method. & TOPSIS and mathematical modeling. & Reduction of costs and carbon emissions. \\ \hline
[49] & [PERSON] et al. & 2024 & Journal of Environmental Management & Empirical and simulated investigation of the solid waste reverse supply chain. & Agent-based modeling and regression analysis. & Identification of factors influencing waste sorting behavior. \\ \hline
[50] & [PERSON] et al. & 2022 & Heliyon & Integrated TOPSIS-COV approach for selecting a sustainable PET waste management technology. & TOPSIS combined with a coefficient of variation (COV). & Closed-loop recycling as the optimal solution. \\ \hline
[51] & [PERSON] et al. & 2020 & Expert Systems with Applications & A Fuzzy Inference-based Scenario Building in Two-Stage Optimization Framework for Sustainable Recycling Supply Chain Redesign. & Fuzzy Inference System and consultant method. & Robust solutions for redesigning sustainable reverse supply chain under uncertainty. \\ \hline
[52] & [PERSON] et al. & 2024 & Decision Analytics Journal & An analysis of critical factors for adopting machine learning in manufacturing supply chains. & DEMATEL and qualitative analysis. & “Technology integration” and “Forecasting” as essential factors. \\ \hline
[53] & [PERSON] et al. & 2023 & Sustainable Production and Consumption & Adopting Artificial Intelligence for enhancing the implementation of systemic circularity. & Systematic review and SWOT analysis. & Framework for AI application throughout the building product lifecycle. \\ \hline \end{tabular}
\end{table} 
Table 3: Studies related by eligibility criteria—main characteristics of the included studies.