## 4 Results and Discussion
### Integration of MCDA and AI in RSCSW Optimization  
The integration of MCDA and AI has emerged as a transformative approach to addressing the complexities of RSCSW. The reviewed studies demonstrate that this integration enhances decision-making processes by combining the strengths of both methodologies:  
* MCDA provides a structured framework for evaluating multiple criteria, such as economic, environmental, and social factors, enabling decision makers to balance trade-offs effectively. Techniques like TOPSIS, AHP, and BWM have been widely applied to prioritize strategies, select optimal locations for recycling centers, and evaluate the sustainability of waste treatment methods [45; 49; 56].
* AI introduces advanced computational capabilities, such as machine learning, neural networks, and genetic algorithms, which enable the automation of complex decision-making processes, predictive analytics, and real-time optimization [35; 38].  
For example, [PERSON] et al. [45] introduced a method based on graph neural networks (GNNs) combined with TOPSIS, achieving a 5.78% improvement in ranking metrics compared to traditional methods. Similarly, [PERSON] et al. [48] used TOPSIS to select optimal strategies for plastic waste reverse logistics, demonstrating the effectiveness of MCDA in reducing costs and carbon emissions. [PERSON] [56] combined BWM and F-VIKOR to prioritize e-waste mitigation strategies, emphasizing the importance of \"take-back practices\" for sustainable e-waste management.  
In terms of a critical evaluation and divergent perspectives, the following aspects can be highlighted:  
* Computational constraints: [PERSON] [38] highlights that deep neural networks require significant computational resources and large datasets for training, which can be a  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref** & **Author(s)** & **Population** & **Intervention** & **Comparison** & **Outcome** \\ \hline
[65] & [PERSON], [PERSON] et al. & Multi-criteria & Review of AHP and & Traditional & Detailed comparison \\  & [PERSON], [PERSON] et al. & decision analysis & TOPSIS & Traditional & between AHP and \\  & & & TOPSIS for multi-criteria & TOPSIS for multi-criteria & decision-making \\ \hline
[66] & [PERSON], [PERSON] et al. & Fish reverse & MILP & Traditional & Increased profit and \\  & & supply chain & MILP & Traditional & reduced carbon \\  & & & & emissions & emissions \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._barrier in resource-constrained environments. This limitation is particularly relevant in developing countries, where digital infrastructure may be inadequate.
* Algorithmic sensitivity: [PERSON] et al. [35] note that genetic algorithms are highly sensitive to initialization parameters, which can lead to premature convergence or suboptimal solutions. This raises concerns about the robustness of AI-driven decision making in real-world waste management applications.
* Lack of standardization: Unlike traditional MCDA methods with well-defined evaluation criteria, AI-driven approaches lack standardized frameworks for waste management applications [14]. This divergence highlights the need for more empirical validation and cross-sectoral benchmarking.