## 1 Introduction
The circular economy has been identified as a crucial approach to mitigating the negative impacts of solid waste generation globally, promoting resource optimization and reducing dependence on the extraction of new raw materials [15]. Governments and private sectors have been implementing circular economy-based strategies, such as recycling incentives, more sustainable production models, and the creation of more resilient supply chains [14]. However, significant challenges remain in the practical implementation of these initiatives, requiring new technological and managerial approaches to ensure their effectiveness. This model demands the optimization of solid waste supply chains (RSCSW), which play a fundamental role in the recovery of discarded materials. Nevertheless, RSCSW management is complex and faces significant challenges, including logistical disruptions, fragmented legislation, unforeseen events, failures in waste digitization and tracking, and cyberattacks [17; 18; 19; 20; 21]. The absence of robust governance practices negatively impacts the efficiency and benefits of reverse supply chains for solid waste (RSCSW). Governance involves regulatory structures, coordination mechanisms, and incentives for different stakeholders to operate effectively. According to [PERSON] [23] and [PERSON] [24], the efficient coordination of economic activities is directly related to the ability to minimize transaction costs and optimize decision making. Furthermore, [PERSON] [25], [PERSON] and [PERSON] [26], and [PERSON] and [PERSON] [27] emphasize that effective governance depends on control and transparency mechanismsto ensure collaboration among stakeholders. Additionally, the G20/OECD Principles of Corporate Governance [28] provide a comprehensive framework to strengthen governance structures, highlighting the importance of transparency, accountability, and stakeholder participation in promoting sustainable and resilient systems. Moreover, effective governance in reverse supply chains and solid waste management is crucial to ensuring the efficiency, resilience, and sustainability of these systems [29; 30; 31; 32]. The complexity of these supply chains, which involve the allocation and commitment of financial resources, demands a systematic and evidence-based approach to making informed decisions [33]. In this context, the use of decision support tools, such as multi-criteria decision analysis (MCDA) and, more recently, Artificial Intelligence (AI) methods, can mitigate the risk of decisions that do not reflect maximum efficiency and resilience [34]. Considering these aspects of the need for robust governance as a form of resilience, managerial alternatives, such as the use of decision support models (DSMs), have been increasingly employed to optimize RSCSW management. Among these models, multi-criteria decision analysis (MCDA), Analytic Hierarchy Process (AHP), Goal Programming (GP), and Fuzzy Logic stand out, enabling the evaluation of trade-offs between multiple criteria and balancing economic, environmental, and social aspects [18; 19; 20; 21]. In another analytical dimension, the advancement of Artificial Intelligence (AI) has revolutionized logistics and supply chain management, introducing advanced forecasting, optimization, and automation techniques, such as machine learning, neural networks, and genetic algorithms [35; 36; 37; 38]. Thus, this study aims to (1) identify integrated optimization strategies using MCDA and AI to improve governance in reverse solid waste supply chains (RSCSW); (2) evaluate how these strategies contribute to operational efficiency, cost reduction, and material recovery across different sectors (e.g., construction, agriculture, urban management); (3) propose a structured decision support framework to guide the implementation of AI-MCDA integration in RSCSW governance; (4) analyze governance, regulatory, and technological barriers, offering recommendations to support evidence-based public policies and sustainable logistics practices.